1
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Li X, Eastham J, Giltnane JM, Zou W, Zijlstra A, Tabatsky E, Banchereau R, Chang CW, Nabet BY, Patil NS, Molinero L, Chui S, Harryman M, Lau S, Rangell L, Waumans Y, Kockx M, Orlova D, Koeppen H. Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy. J Pathol 2024. [PMID: 38525811 DOI: 10.1002/path.6274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 12/22/2023] [Accepted: 02/14/2024] [Indexed: 03/26/2024]
Abstract
Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Xiao Li
- Genentech, South San Francisco, CA, USA
| | | | | | - Wei Zou
- Genentech, South San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | | | - Shari Lau
- Genentech, South San Francisco, CA, USA
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2
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Wu R, Horimoto Y, Oshi M, Benesch MGK, Khoury T, Takabe K, Ishikawa T. Emerging measurements for tumor-infiltrating lymphocytes in breast cancer. Jpn J Clin Oncol 2024:hyae033. [PMID: 38521965 DOI: 10.1093/jjco/hyae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/01/2024] [Indexed: 03/25/2024] Open
Abstract
Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.
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Affiliation(s)
- Rongrong Wu
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Yoshiya Horimoto
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Breast Oncology, Juntendo University Hospital, Tokyo, Japan
| | - Masanori Oshi
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Matthew G K Benesch
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Thaer Khoury
- Department of Pathology & Laboratory Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Kazuaki Takabe
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan
- Department of Surgery, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, The State University of New York, Buffalo, NY, USA
- Department of Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Department of Breast Surgery, Fukushima Medical University, Fukushima, Japan
| | - Takashi Ishikawa
- Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan
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3
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Ly A, Garcia V, Blenman KRM, Ehinger A, Elfer K, Hanna MG, Li X, Peeters DJE, Birmingham R, Dudgeon S, Gardecki E, Gupta R, Lennerz J, Pan T, Saltz J, Wharton KA, Ehinger D, Acs B, Dequeker EMC, Salgado R, Gallas BD. Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research. Histopathology 2024. [PMID: 38433289 DOI: 10.1111/his.15140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/15/2023] [Accepted: 12/31/2023] [Indexed: 03/05/2024]
Abstract
A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.
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Affiliation(s)
- Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Victor Garcia
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA
| | - Kim R M Blenman
- Department of Internal Medicine, Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Anna Ehinger
- Department of Genetics, Pathology and Molecular Diagnostics, Laboratory Medicine, Region Skane, Lund University, Lund, Sweden
| | - Katherine Elfer
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA
| | - Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Dieter J E Peeters
- Department of Pathology, University Hospital Antwerp, Edegem, Belgium
- Department of Pathology, Algemeen Ziekenhuis (AZ) Sint-Maarten, Mechelen, Belgium
| | - Ryan Birmingham
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Sarah Dudgeon
- Center for Computational Health, Yale School of Medicine, New Haven, CT, USA
| | - Emma Gardecki
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Jochen Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Boston, MA, USA
| | - Tony Pan
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | - Daniel Ehinger
- Department of Clinical Sciences, Division of Oncology, Lund University, Lund, Sweden
- Department of Genetics, Pathology, and Molecular Diagnostics, Skane University Hospital, Lund, Sweden
| | - Balazs Acs
- Department of Oncology and Pathology, Cancer Centre Karolinska, Karolinksa Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Elisabeth M C Dequeker
- Department of Public Health and Primary Care, Biomedical Quality Assurance Research Unit, University of Leuven, Leuven, Belgium
| | - Roberto Salgado
- Department of Pathology, Gasthuiszusters Antwerpen-Ziekenhuis Netwerk Antwerpen (GZA-ZNA) Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Brandon D Gallas
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA
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4
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Jahangir CA, Page DB, Broeckx G, Gonzalez CA, Burke C, Murphy C, Reis-Filho JS, Ly A, Harms PW, Gupta RR, Vieth M, Hida AI, Kahila M, Kos Z, van Diest PJ, Verbandt S, Thagaard J, Khiroya R, Abduljabbar K, Acosta Haab G, Acs B, Adams S, Almeida JS, Alvarado-Cabrero I, Azmoudeh-Ardalan F, Badve S, Baharun NB, Bellolio ER, Bheemaraju V, Blenman KR, Botinelly Mendonça Fujimoto L, Burgues O, Chardas A, Cheang MCU, Ciompi F, Cooper LA, Coosemans A, Corredor G, Dantas Portela FL, Deman F, Demaria S, Dudgeon SN, Elghazawy M, Fernandez-Martín C, Fineberg S, Fox SB, Giltnane JM, Gnjatic S, Gonzalez-Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hart SN, Hartman J, Hewitt S, Horlings HM, Husain Z, Irshad S, Janssen EA, Kataoka TR, Kawaguchi K, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Akturk G, Scott E, Kovács A, Laenkholm AV, Lang-Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Kharidehal D, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault-Llorca F, Perera RD, Pinard CJ, Pinto-Cardenas JC, Pruneri G, Pusztai L, Rajpoot NM, Rapoport BL, Rau TT, Ribeiro JM, Rimm D, Vincent-Salomon A, Saltz J, Sayed S, Hytopoulos E, Mahon S, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, Verghese GE, Viale G, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Specht Stovgaard E, Salgado R, Gallagher WM, Rahman A. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer. J Pathol 2024; 262:271-288. [PMID: 38230434 DOI: 10.1002/path.6238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/17/2023] [Indexed: 01/18/2024]
Abstract
Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Chowdhury Arif Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - David B Page
- Earle A Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Glenn Broeckx
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
- Centre for Oncological Research (CORE), MIPPRO, Faculty of Medicine, Antwerp University, Antwerp, Belgium
| | - Claudia A Gonzalez
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Paul W Harms
- Departments of Pathology and Dermatology, University of Michigan, Ann Arbor, MI, USA
| | - Rajarsi R Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Akira I Hida
- Department of Pathology, Matsuyama Shimin Hospital, Matsuyama, Japan
| | - Mohamed Kahila
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Zuzana Kos
- Department of Pathology and Laboratory Medicine, University of British Columbia, BC Cancer, Vancouver, British Columbia, Canada
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
- Johns Hopkins Oncology Center, Baltimore, MD, USA
| | - Sara Verbandt
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jeppe Thagaard
- Technical University of Denmark, Kgs. Lyngby, Denmark
- Visiopharm A/S, Hørsholm, Denmark
| | - Reena Khiroya
- Department of Cellular Pathology, University College Hospital, London, UK
| | - Khalid Abduljabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | | | - Balazs Acs
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Sylvia Adams
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
- Department of Medicine, NYU Grossman School of Medicine, Manhattan, NY, USA
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), Rockville, MD, USA
| | | | | | - Sunil Badve
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Emory University Winship Cancer Institute, Atlanta, GA, USA
| | | | - Enrique R Bellolio
- Departamento de Anatomía Patológica, Facultad de Medicina, Universidad de La Frontera, Temuco, Chile
| | | | - Kim Rm Blenman
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | | | - Octavio Burgues
- Pathology Department, Hospital Cliníco Universitario de Valencia/Incliva, Valencia, Spain
| | - Alexandros Chardas
- Department of Pathobiology & Population Sciences, The Royal Veterinary College, London, UK
| | - Maggie Chon U Cheang
- Head of Integrative Genomics Analysis in Clinical Trials, ICR-CTSU, Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lee Ad Cooper
- Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - An Coosemans
- Department of Oncology, Laboratory of Tumor Immunology and Immunotherapy, KU Leuven, Leuven, Belgium
| | - Germán Corredor
- Biomedical Engineering Department, Emory University, Atlanta, GA, USA
| | | | - Frederik Deman
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Sandra Demaria
- Department of Radiation Oncology, Weill Cornell Medical College, New York, NY, USA
- Department of Pathology, Weill Cornell Medicine, New York, NY, USA
| | - Sarah N Dudgeon
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Mahmoud Elghazawy
- University of Surrey, Guildford, UK
- Ain Shams University, Cairo, Egypt
| | - Claudio Fernandez-Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
| | - Susan Fineberg
- Montefiore Medical Center and the Albert Einstein College of Medicine, New York, NY, USA
| | - Stephen B Fox
- Pathology, Peter MacCallum Cancer Centre and Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Sacha Gnjatic
- Department of Oncological Sciences, Medicine Hem/Onc, and Pathology, Tisch Cancer Institute - Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Anita Grigoriadis
- Cancer Bioinformatics, Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- The Breast Cancer Now Research Unit, Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Niels Halama
- Department of Translational Immunotherapy, German Cancer Research Center, Heidelberg, Germany
| | | | | | - Steven N Hart
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Johan Hartman
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Stephen Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hugo M Horlings
- Division of Pathology, Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | | | - Sheeba Irshad
- King's College London & Guys & St Thomas NHS Trust, London, UK
| | - Emiel Am Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | | | - Kosuke Kawaguchi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Andrey I Khramtsov
- Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | - Pawan Kirtani
- Histopathology, Aakash Healthcare Super Speciality Hospital, New Delhi, India
| | - Liudmila L Kodach
- Department of Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Konstanty Korski
- Data, Analytics and Imaging, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Guray Akturk
- Translational Molecular Biomarkers, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Ely Scott
- Translational Medicine, Bristol Myers Squibb, Princeton, NJ, USA
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anne-Vibeke Laenkholm
- Department of Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
- Department of Surgical Pathology, University of Copenhagen, Copenhagen, Denmark
| | - Corinna Lang-Schwarz
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Denis Larsimont
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Marvin Lerousseau
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM U900, Paris, France
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics, Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sai K Maley
- NRG Oncology/NSABP Foundation, Pittsburgh, PA, USA
| | | | - Douglas K Marks
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Elizabeth S McDonald
- Breast Cancer Translational Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Mehrotra
- Indian Cancer Genomic Atlas, Pune, India
- Centre for Health, Innovation and Policy Foundation, Noida, India
| | - Stefan Michiels
- Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, Inserm, University Paris-Saclay, Ligue Contre le Cancer labeled Team, Villejuif, France
| | - Durga Kharidehal
- Department of Pathology, Narayana Medical College and Hospital, Nellore, India
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, Warwick Cancer Research Centre, PathLAKE Consortium, Department of Computer Science, University of Warwick, Coventry, UK
| | - Shachi Mittal
- Department of Chemical Engineering, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - David A Moore
- CRUK Lung Cancer Centre of Excellence, UCL and Cellular Pathology Department, UCLH, London, UK
| | - Shamim Mushtaq
- Department of Biochemistry, Ziauddin University, Karachi, Pakistan
| | - Hussain Nighat
- Pathology and Laboratory Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Drammen Sykehus, Vestre Viken HF, Drammen, Norway
| | - Frederique Penault-Llorca
- Service de Pathologie et Biopathologie, Centre Jean PERRIN, INSERM U1240 Imagerie Moléculaire et Stratégies Théranostiques (IMoST), Université Clermont Auvergne, Clermont-Ferrand, France
| | - Rashindrie D Perera
- School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Christopher J Pinard
- Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- Department of Oncology, Lakeshore Animal Health Partners, Mississauga, Ontario, Canada
- Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), University of Guelph, Guelph, Ontario, Canada
| | | | - Giancarlo Pruneri
- Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Lajos Pusztai
- Yale Cancer Center, Yale University, New Haven, CT, USA
- Department of Medical Oncology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | | | - Bernardo Leon Rapoport
- The Medical Oncology Centre of Rosebank, Johannesburg, South Africa
- Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Tilman T Rau
- Institute of Pathology, University Hospital Düsseldorf and Heinrich-Heine-University, Düsseldorf, Germany
| | | | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, New York, NY, USA
| | - Shahin Sayed
- Department of Pathology, Aga Khan University, Nairobi, Kenya
| | - Evangelos Hytopoulos
- Department of Pathology, Aga Khan University, Nairobi, Kenya
- iRhythm Technologies Inc., San Francisco, CA, USA
| | - Sarah Mahon
- Mater Misericordiae University Hospital, Dublin, Ireland
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles (ULB), Brussels, Belgium
- Medical Oncology Department, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| | | | - Daniel Sur
- Department of Medical Oncology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania
| | - Fraser Symmans
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sabine Tejpar
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jonas Teuwen
- AI for Oncology Lab, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Trine Tramm
- Department of Pathology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - William T Tran
- Department of Radiation Oncology, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Jeroen van der Laak
- Head of Integrative Genomics Analysis in Clinical Trials, ICR-CTSU, Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Gregory E Verghese
- Cancer Bioinformatics, Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- The Breast Cancer Now Research Unit, Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Giuseppe Viale
- Department of Pathology, European Institute of Oncology & University of Milan, Milan, Italy
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Thomas Walter
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM U900, Paris, France
| | | | - Hannah Y Wen
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wentao Yang
- Fudan Medical University Shanghai Cancer Center, Shanghai, PR China
| | - Yinyin Yuan
- Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Sibylle Loibl
- Department of Medicine and Research, German Breast Group, Neu-Isenburg, Germany
| | - Carsten Denkert
- Institut für Pathologie, Philipps-Universität Marburg und Universitätsklinikum Marburg, Marburg, Germany
| | - Peter Savas
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Sherene Loi
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Roberto Salgado
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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5
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Fang S, Xia W, Zhang H, Ni C, Wu J, Mo Q, Jiang M, Guan D, Yuan H, Chen W. A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol 2024; 14:1323226. [PMID: 38420013 PMCID: PMC10899694 DOI: 10.3389/fonc.2024.1323226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Purpose This study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors. Methods This retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People's Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People's Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis. Results In the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes. Conclusion The developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.
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Affiliation(s)
- Shan Fang
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wenjie Xia
- General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Haibo Zhang
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chao Ni
- Department of Breast Surgery (Surgical Oncology), Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun Wu
- General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiuping Mo
- General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Mengjie Jiang
- Department of Radiotherapy, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Dandan Guan
- General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hongjun Yuan
- General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wuzhen Chen
- Department of Oncology, Lanxi People’s Hospital, Jinhua, China
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Reznitsky FM, Jensen JD, Knoop A, Jensen MB, Laenkholm AV. Evaluation of tumor-infiltrating lymphocytes, PD-L1, and PIK3CA mutations and association with prognosis in HER2-positive early stage breast cancer. Acta Oncol 2023; 62:1913-1920. [PMID: 37961947 DOI: 10.1080/0284186x.2023.2279685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic potential in HER2-positive breast cancer (HER2+ BC). Programmed death-ligand 1 (PD-L1) is an immune checkpoint protein, with important roles in the tumor microenvironment, possibly in both tumor and immune cells (ICs), providing rationale for targeting with immune-checkpoint therapy. PIK3CA mutations are oncogenic, activating mutations, which are also of relevance in breast cancer. Herein, we investigate the frequency of TILs, PD-L1 and PIK3CA mutations, and whether these factors influence outcome, in early HER2+ BC. MATERIALS AND METHODS Stromal TILs (sTILs) and PD-L1 expressions were assessed using full tumor-sections and TMA, respectively, from 236 patients with HER2+ BC. TILs were assessed, according to a standardized method, as continuous measurement and according to three predefined categories: low (0-10%), intermediate (11-59%), and high (60-100%). PD-L1 immunohistochemistry (Ventana SP263) was evaluated and positivity defined as ≥1% expression in tumor and ICs. PIK3CA mutations (exons 9 and 20) were determined by pyrosequencing. RESULTS Fourteen percent of patients had high sTILs and 25% had a PIK3CA mutation. PD-L1 expression was more frequent in ICs (68%) than tumor cells (24%). Patients with low sTILs had a significantly worse overall survival (multivariate: HR 2.80; 95% CI 1.36-5.78; p = .02). DISCUSSION Patients with low sTILs had a significantly poorer survival, despite adequate treatment with adjuvant therapy.
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Affiliation(s)
- Frances M Reznitsky
- Department of Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
- Department of Pathology, Herlev and Gentofte Hospital, Herlev, Denmark
| | | | - Ann Knoop
- Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maj-Britt Jensen
- Danish Breast Cancer Group, Copenhagen University Hospital, Copenhagen, Denmark
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7
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Landén AH, Chin K, Kovács A, Holmberg E, Molnar E, Stenmark Tullberg A, Wärnberg F, Karlsson P. Evaluation of tumor-infiltrating lymphocytes and mammographic density as predictors of response to neoadjuvant systemic therapy in breast cancer. Acta Oncol 2023; 62:1862-1872. [PMID: 37934084 DOI: 10.1080/0284186x.2023.2274483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD. MATERIAL AND METHODS We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a-d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS. RESULTS Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59-5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99-5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78-8.04). We found no correlation between TILs and MD (0.02; p = .80). CONCLUSION TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated.
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Affiliation(s)
- Amalia H Landén
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Kian Chin
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Erik Holmberg
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Molnar
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Axel Stenmark Tullberg
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Wärnberg
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Per Karlsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Aswolinskiy W, Munari E, Horlings HM, Mulder L, Bogina G, Sanders J, Liu YH, van den Belt-Dusebout AW, Tessier L, Balkenhol M, Stegeman M, Hoven J, Wesseling J, van der Laak J, Lips EH, Ciompi F. PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning. Breast Cancer Res 2023; 25:142. [PMID: 37957667 PMCID: PMC10644597 DOI: 10.1186/s13058-023-01726-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/02/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. METHODS In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). RESULTS We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. CONCLUSION The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.
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Affiliation(s)
- Witali Aswolinskiy
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Enrico Munari
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Hugo M Horlings
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Lennart Mulder
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Giuseppe Bogina
- Pathology Unit, IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, Verona, Italy
| | - Joyce Sanders
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Yat-Hee Liu
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | | | - Leslie Tessier
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Center for Integrated Oncology (Institut du cancer de l'Ouest), Angers, France
| | - Maschenka Balkenhol
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michelle Stegeman
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeffrey Hoven
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jelle Wesseling
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Esther H Lips
- The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
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9
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Makhlouf S, Wahab N, Toss M, Ibrahim A, Lashen AG, Atallah NM, Ghannam S, Jahanifar M, Lu W, Graham S, Mongan NP, Bilal M, Bhalerao A, Snead D, Minhas F, Raza SEA, Rajpoot N, Rakha E. Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. Br J Cancer 2023; 129:1747-1758. [PMID: 37777578 PMCID: PMC10667537 DOI: 10.1038/s41416-023-02451-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 09/08/2023] [Accepted: 09/20/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.
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Affiliation(s)
- Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Michael Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Histopathology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Nehal M Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Pathology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Suzan Ghannam
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Histology and cell biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | | | - Wenqi Lu
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Nigel P Mongan
- Biodiscovery Institute, School of Veterinary Medicine and Sciences, University of Nottingham, Nottingham, UK
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | - David Snead
- University Hospital Coventry and Warwickshire, Coventry, UK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | | | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK.
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar.
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10
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Steenbruggen TG, Wolf DM, Campbell MJ, Sanders J, Cornelissen S, Thijssen B, Salgado RA, Yau C, O-Grady N, Basu A, Bhaskaran R, Mittempergher L, Hirst GL, Coppe JP, Kok M, Sonke GS, van 't Veer LJ, Horlings HM. B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer. Breast Cancer Res 2023; 25:117. [PMID: 37794508 PMCID: PMC10552219 DOI: 10.1186/s13058-023-01717-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/21/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome. METHODS We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome. RESULTS A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole. CONCLUSIONS In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.
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Affiliation(s)
- Tessa G Steenbruggen
- Department of Medical Oncology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands.
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA.
| | - Denise M Wolf
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Michael J Campbell
- Department of Surgery, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Joyce Sanders
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
| | - Sten Cornelissen
- Core Facility Molecular Pathology and Biobanking, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
| | - Bram Thijssen
- Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
| | - Roberto A Salgado
- Department of Pathology, GZA-ZNA Hospitals, 2020, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - Christina Yau
- Department of Surgery, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Nick O-Grady
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Amrita Basu
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Rajith Bhaskaran
- Research and Development, Agendia N.V, 1043 NT, Amsterdam, North Holland, The Netherlands
| | - Lorenza Mittempergher
- Research and Development, Agendia N.V, 1043 NT, Amsterdam, North Holland, The Netherlands
| | - Gillian L Hirst
- Department of Surgery, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Jean-Philippe Coppe
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Marleen Kok
- Department of Medical Oncology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
- Department of Clinical Oncology, University of Amsterdam, 1012 WX, Amsterdam, North Holland, The Netherlands
| | - Laura J van 't Veer
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Hugo M Horlings
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, North Holland, The Netherlands
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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Choi S, Cho SI, Jung W, Lee T, Choi SJ, Song S, Park G, Park S, Ma M, Pereira S, Yoo D, Shin S, Ock CY, Kim S. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer 2023; 9:71. [PMID: 37648694 PMCID: PMC10469174 DOI: 10.1038/s41523-023-00577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | | | | | - Su Jin Choi
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | | | | | | | - Minuk Ma
- Lunit Inc, Seoul, Republic of Korea
| | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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Saastad SA, Skjervold AH, Ytterhus B, Engstrøm MJ, Bofin AM. PD-L1 protein expression in breast cancer. J Clin Pathol 2023:jcp-2023-208942. [PMID: 37553245 DOI: 10.1136/jcp-2023-208942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/26/2023] [Indexed: 08/10/2023]
Abstract
AIMS The immune checkpoint marker, Programmed cell death-ligand 1 (PD-L1), is expressed by both cancer epithelial cells and tumour-infiltrating immune cells (TICs) thus constituting a potential target for immunotherapy. This is of particular interest in triple negative breast cancer. In this study, we assessed the prognostic value of PD-L1 expression in tumour epithelial cells and TICs in a series of patients with breast cancer with long-term follow-up, and associations between PD-L1 expression and histopathological type and grade, proliferation and molecular subtype. METHODS Using immunohistochemistry for PD-L1 in tissue microarrays, we assessed PD-L1 expression in 821 tumours. Expression of PD-L1 was assessed separately in the epithelial and stromal compartments and classified as <1%, ≥1% to <10% or ≥10% positive staining cells. We correlated PD-L1 expression in tumour epithelial cells and TICs with tumour characteristics using Pearson's χ2 test, and prognosis by cumulative incidence of death from breast cancer and Cox regression analyses. RESULTS We found membranous staining in ≥1% of tumour epithelial cells in 53/821 cases (6.5%). Of these, 21 (2.6%) were ≥10%. Among TICs, staining (≥1%) was seen in 144/821 cases (17.6%). Of these, 62 were ≥10% (7.6%). PD-L1 was associated with high histopathological grade and proliferation, and the medullary and metaplastic patterns. In TICs, PD-L1 ≥1% found in 22/34 (34.4%) human epidermal growth factor receptor 2 type and 29/58 (50%) basal phenotype. An independent association between PD-L1 expression and prognosis was not observed. CONCLUSIONS PD-L1 is expressed more frequently in TICs than tumour epithelial cells. Expression in TICs is associated with aggressive tumour characteristics and non-luminal tumours but not with prognosis.
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Affiliation(s)
- Sigurd A Saastad
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anette H Skjervold
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Borgny Ytterhus
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Monica Jernberg Engstrøm
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Sugrery, St. Olav's Hospital Trondheim University Hospital, Trondheim, Norway
| | - Anna M Bofin
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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14
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Thagaard J, Broeckx G, Page DB, Jahangir CA, Verbandt S, Kos Z, Gupta R, Khiroya R, Abduljabbar K, Acosta Haab G, Acs B, Akturk G, Almeida JS, Alvarado‐Cabrero I, Amgad M, Azmoudeh‐Ardalan F, Badve S, Baharun NB, Balslev E, Bellolio ER, Bheemaraju V, Blenman KRM, Botinelly Mendonça Fujimoto L, Bouchmaa N, Burgues O, Chardas A, Chon U Cheang M, Ciompi F, Cooper LAD, Coosemans A, Corredor G, Dahl AB, Dantas Portela FL, Deman F, Demaria S, Doré Hansen J, Dudgeon SN, Ebstrup T, Elghazawy M, Fernandez‐Martín C, Fox SB, Gallagher WM, Giltnane JM, Gnjatic S, Gonzalez‐Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hart SN, Hartman J, Hauberg S, Hewitt S, Hida AI, Horlings HM, Husain Z, Hytopoulos E, Irshad S, Janssen EAM, Kahila M, Kataoka TR, Kawaguchi K, Kharidehal D, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Kovács A, Laenkholm A, Lang‐Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Ly A, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault‐Llorca F, Perera RD, Pinard CJ, Pinto‐Cardenas JC, Pruneri G, Pusztai L, Rahman A, Rajpoot NM, Rapoport BL, Rau TT, Reis‐Filho JS, Ribeiro JM, Rimm D, Roslind A, Vincent‐Salomon A, Salto‐Tellez M, Saltz J, Sayed S, Scott E, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Fineberg S, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, van Diest PJ, Verghese GE, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Zin RM, Adams S, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Salgado R, Specht Stovgaard E. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer. J Pathol 2023; 260:498-513. [PMID: 37608772 PMCID: PMC10518802 DOI: 10.1002/path.6155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/07/2023] [Indexed: 08/24/2023]
Abstract
The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Jeppe Thagaard
- Technical University of DenmarkKongens LyngbyDenmark
- Visiopharm A/SHørsholmDenmark
| | - Glenn Broeckx
- Department of PathologyGZA‐ZNA HospitalsAntwerpBelgium
- Centre for Oncological Research (CORE), MIPPRO, Faculty of MedicineAntwerp UniversityAntwerpBelgium
| | - David B Page
- Earle A Chiles Research InstituteProvidence Cancer InstitutePortlandORUSA
| | - Chowdhury Arif Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | - Sara Verbandt
- Digestive Oncology, Department of OncologyKU LeuvenLeuvenBelgium
| | - Zuzana Kos
- Department of Pathology and Laboratory MedicineBC Cancer Vancouver Centre, University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Rajarsi Gupta
- Department of Biomedical InformaticsStony Brook UniversityStony BrookNYUSA
| | - Reena Khiroya
- Department of Cellular PathologyUniversity College Hospital LondonLondonUK
| | | | | | - Balazs Acs
- Department of Oncology and PathologyKarolinska InstitutetStockholmSweden
- Department of Clinical Pathology and Cancer DiagnosticsKarolinska University HospitalStockholmSweden
| | - Guray Akturk
- Translational Molecular Biomarkers, Merck & Co IncRahwayNJUSA
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG)National Cancer Institute (NCI)Rockville, MDUSA
| | | | - Mohamed Amgad
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | | | - Sunil Badve
- Department of Pathology and Laboratory Medicine, Emory University School of MedicineEmory University Winship Cancer InstituteAtlantaGAUSA
| | | | - Eva Balslev
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
| | - Enrique R Bellolio
- Departamento de Anatomía Patológica, Facultad de MedicinaUniversidad de La FronteraTemucoChile
| | | | - Kim RM Blenman
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer CenterYale School of MedicineNew HavenCTUSA
- Department of Computer ScienceYale School of Engineering and Applied ScienceNew HavenCTUSA
| | | | - Najat Bouchmaa
- Institute of Biological Sciences, Faculty of Medical SciencesMohammed VI Polytechnic University (UM6P)Ben‐GuerirMorocco
| | - Octavio Burgues
- Pathology DepartmentHospital Cliníco Universitario de Valencia/InclivaValenciaSpain
| | - Alexandros Chardas
- Department of Pathobiology & Population SciencesThe Royal Veterinary CollegeLondonUK
| | - Maggie Chon U Cheang
- Head of Integrative Genomics Analysis in Clinical Trials, ICR‐CTSU, Division of Clinical StudiesThe Institute of Cancer ResearchLondonUK
| | - Francesco Ciompi
- Radboud University Medical CenterDepartment of PathologyNijmegenThe Netherlands
| | - Lee AD Cooper
- Department of PathologyNorthwestern Feinberg School of MedicineChicagoILUSA
| | - An Coosemans
- Department of Oncology, Laboratory of Tumor Immunology and ImmunotherapyKU LeuvenLeuvenBelgium
| | - Germán Corredor
- Biomedical Engineering DepartmentEmory UniversityAtlantaGAUSA
| | - Anders B Dahl
- Technical University of DenmarkKongens LyngbyDenmark
| | | | | | - Sandra Demaria
- Department of Radiation OncologyWeill Cornell MedicineNew YorkNYUSA
- Department of Pathology and Laboratory MedicineWeill Cornell MedicineNew YorkNYUSA
| | | | - Sarah N Dudgeon
- Conputational Biology and BioinformaticsYale UniversityNew HavenCTUSA
| | | | | | - Claudio Fernandez‐Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN‐techUniversitat Politècnica de ValènciaValenciaSpain
| | - Stephen B Fox
- Pathology, Peter MacCallum Cancer Centre and Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | | | - Sacha Gnjatic
- Department of Oncological Sciences, Medicine Hem/Onc, and Pathology, Tisch Cancer Institute – Precision Immunology InstituteIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | | | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Niels Halama
- Department of Translational ImmunotherapyGerman Cancer Research CenterHeidelbergGermany
| | - Matthew G Hanna
- Department of PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
| | | | - Steven N Hart
- Department of Laboratory Medicine and PathologyMayo ClinicRochester, MNUSA
| | - Johan Hartman
- Department of Oncology and PathologyKarolinska InstitutetStockholmSweden
- Department of Clinical Pathology and Cancer DiagnosticsKarolinska University HospitalStockholmSweden
| | - Søren Hauberg
- Technical University of DenmarkKongens LyngbyDenmark
| | - Stephen Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer InstituteNational Institutes of HealthBethesdaMDUSA
| | - Akira I Hida
- Department of PathologyMatsuyama Shimin HospitalMatsuyamaJapan
| | - Hugo M Horlings
- Division of PathologyNetherlands Cancer Institute (NKI)AmsterdamThe Netherlands
| | | | | | - Sheeba Irshad
- King's College London & Guy's & St Thomas’ NHS TrustLondonUK
| | - Emiel AM Janssen
- Department of PathologyStavanger University HospitalStavangerNorway
- Department of Chemistry, Bioscience and Environmental TechnologyUniversity of StavangerStavangerNorway
| | | | | | - Kosuke Kawaguchi
- Department of Breast SurgeryKyoto University Graduate School of MedicineKyotoJapan
| | | | - Andrey I Khramtsov
- Department of Pathology and Laboratory MedicineAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoILUSA
| | - Umay Kiraz
- Department of PathologyStavanger University HospitalStavangerNorway
- Department of Chemistry, Bioscience and Environmental TechnologyUniversity of StavangerStavangerNorway
| | - Pawan Kirtani
- Department of HistopathologyAakash Healthcare Super Speciality HospitalNew DelhiIndia
| | - Liudmila L Kodach
- Department of PathologyNetherlands Cancer Institute – Antoni van Leeuwenhoek HospitalAmsterdamThe Netherlands
| | - Konstanty Korski
- Data, Analytics and Imaging, Product DevelopmentF. Hoffmann‐La Roche AGBaselSwitzerland
| | - Anikó Kovács
- Department of Clinical PathologySahlgrenska University HospitalGothenburgSweden
- Institute of Biomedicine, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Anne‐Vibeke Laenkholm
- Department of Surgical PathologyZealand University HospitalRoskildeDenmark
- Department of Surgical PathologyUniversity of CopenhagenCopenhagenDenmark
| | - Corinna Lang‐Schwarz
- Institute of Pathology, Klinikum Bayreuth GmbHFriedrich‐Alexander‐University Erlangen‐NurembergBayreuthGermany
| | - Denis Larsimont
- Institut Jules BordetUniversité Libre de BruxellesBrusselsBelgium
| | - Jochen K Lennerz
- Center for Integrated DiagnosticsMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Marvin Lerousseau
- Centre for Computational Biology (CBIO)Mines Paris, PSL UniversityParisFrance
- Institut CuriePSL UniversityParisFrance
- INSERMParisFrance
| | - Xiaoxian Li
- Department of Pathology and Laboratory MedicineEmory UniversityAtlantaGAUSA
| | - Amy Ly
- Department of PathologyMassachusetts General HospitalBostonMAUSA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics, PathologyGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
| | - Sai K Maley
- NRG Oncology/NSABP FoundationPittsburghPAUSA
| | | | | | - Elizabeth S McDonald
- Breast Cancer Translational Research GroupUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Ravi Mehrotra
- Indian Cancer Genomic AtlasPuneIndia
- Centre for Health, Innovation and Policy FoundationNoidaIndia
| | - Stefan Michiels
- Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, InsermUniversity Paris‐Saclay, Ligue Contre le Cancer labeled TeamVillejuifFrance
| | - Fayyaz ul Amir Afsar Minhas
- Tissue Image Analytics Centre, Warwick Cancer Research Centre, PathLAKE Consortium, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Shachi Mittal
- Department of Chemical Engineering, Department of Laboratory Medicine and PathologyUniversity of WashingtonSeattle, WAUSA
| | - David A Moore
- CRUK Lung Cancer Centre of Excellence, UCL and Cellular Pathology DepartmentUCLHLondonUK
| | - Shamim Mushtaq
- Department of BiochemistryZiauddin UniversityKarachiPakistan
| | - Hussain Nighat
- Pathology and Laboratory MedicineAll India Institute of Medical sciencesRaipurIndia
| | - Thomas Papathomas
- Institute of Metabolism and Systems ResearchUniversity of BirminghamBirminghamUK
- Department of Clinical PathologyDrammen Sykehus, Vestre Viken HFDrammenNorway
| | - Frederique Penault‐Llorca
- Centre Jean Perrin, Université Clermont Auvergne, INSERM, U1240 Imagerie Moléculaire et Stratégies ThéranostiquesClermont FerrandFrance
| | - Rashindrie D Perera
- School of Electrical, Mechanical and Infrastructure EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Christopher J Pinard
- Radiogenomics LaboratorySunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Clinical Studies, Ontario Veterinary CollegeUniversity of GuelphGuelphOntarioCanada
- Department of OncologyLakeshore Animal Health PartnersMississaugaOntarioCanada
- Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE‐AI)University of GuelphGuelphOntarioCanada
| | | | - Giancarlo Pruneri
- Department of Pathology and Laboratory MedicineFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
- Faculty of Medicine and SurgeryUniversity of MilanMilanItaly
| | - Lajos Pusztai
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of Medical Oncology, Yale School of MedicineYale UniversityNew HavenCTUSA
| | - Arman Rahman
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | | | - Bernardo Leon Rapoport
- The Medical Oncology Centre of RosebankJohannesburgSouth Africa
- Department of Immunology, Faculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
| | - Tilman T Rau
- Institute of PathologyUniversity Hospital Düsseldorf and Heinrich‐Heine‐University DüsseldorfDüsseldorfGermany
| | - Jorge S Reis‐Filho
- Department of Pathology and Laboratory MedicineMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Joana M Ribeiro
- Département de Médecine OncologiqueGustave RoussyVillejuifFrance
| | - David Rimm
- Department of PathologyYale University School of MedicineNew HavenCTUSA
- Department of MedicineYale University School of MedicineNew HavenCTUSA
| | - Anne Roslind
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
| | - Anne Vincent‐Salomon
- Department of Diagnostic and Theranostic Medicine, Institut CurieUniversity Paris‐Sciences et LettresParisFrance
| | - Manuel Salto‐Tellez
- Integrated Pathology UnitThe Institute of Cancer ResearchLondonUK
- Precision Medicine CentreQueen's University BelfastBelfastUK
| | - Joel Saltz
- Department of Biomedical InformaticsStony Brook UniversityStony BrookNYUSA
| | - Shahin Sayed
- Department of PathologyAga Khan UniversityNairobiKenya
| | - Ely Scott
- Translational PathologyTranslational Sciences and Diagnostics/Translational Medicine/R&D, Bristol Myers SquibbPrincetonNJUSA
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.‐C. Heuson, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB)Université Libre de Bruxelles (ULB)BrusselsBelgium
- Medical Oncology Department, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB)Université Libre de Bruxelles (ULB)BrusselsBelgium
| | - Albrecht Stenzinger
- Institute of PathologyUniversity Hospital HeidelbergHeidelbergGermany
- Centers for Personalized Medicine (ZPM)HeidelbergGermany
| | | | - Daniel Sur
- Department of Medical OncologyUniversity of Medicine and Pharmacy “Iuliu Hatieganu”Cluj‐NapocaRomania
| | - Susan Fineberg
- Montefiore Medical CenterBronxNYUSA
- Albert Einstein College of MedicineBronxNYUSA
| | - Fraser Symmans
- University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | | | | | - Sabine Tejpar
- Digestive Oncology, Department of OncologyKU LeuvenLeuvenBelgium
| | - Jonas Teuwen
- AI for Oncology Lab, The Netherlands Cancer InstituteAmsterdamThe Netherlands
| | | | - Trine Tramm
- Department of PathologyAarhus University HospitalAarhusDenmark
- Institute of Clinical MedicineAarhus UniversityAarhusDenmark
| | - William T Tran
- Department of Radiation OncologyUniversity of Toronto and Sunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Jeroen van der Laak
- Department of PathologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Paul J van Diest
- Department of PathologyUniversity Medical Center UtrechtThe Netherlands
- Johns Hopkins Oncology CenterBaltimoreMDUSA
| | - Gregory E Verghese
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Giuseppe Viale
- Department of PathologyEuropean Institute of OncologyMilanItaly
- Department of PathologyUniversity of MilanMilanItaly
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbHFriedrich‐Alexander‐University Erlangen‐NurembergBayreuthGermany
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Thomas Walter
- Centre for Computational Biology (CBIO)Mines Paris, PSL UniversityParisFrance
- Institut CuriePSL UniversityParisFrance
- INSERMParisFrance
| | | | - Hannah Y Wen
- Department of PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
| | - Wentao Yang
- Fudan Medical University Shanghai Cancer CenterShanghaiPR China
| | - Yinyin Yuan
- Department of Translational Molecular Pathology, Division of Pathology and Laboratory MedicineThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Reena Md Zin
- Department of Pathology, Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
| | - Sylvia Adams
- Perlmutter Cancer CenterNYU Langone HealthNew YorkNYUSA
- Department of MedicineNYU Grossman School of MedicineManhattanNYUSA
| | | | - Sibylle Loibl
- Department of Medicine and ResearchGerman Breast GroupNeu‐IsenburgGermany
| | - Carsten Denkert
- Institut für PathologiePhilipps‐Universität Marburg und Universitätsklinikum MarburgMarburgGermany
| | - Peter Savas
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- The Sir Peter MacCallum Department of Medical OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - Sherene Loi
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- The Sir Peter MacCallum Department of Medical OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - Roberto Salgado
- Department of PathologyGZA‐ZNA HospitalsAntwerpBelgium
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Elisabeth Specht Stovgaard
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
- Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
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15
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Page DB, Broeckx G, Jahangir CA, Verbandt S, Gupta RR, Thagaard J, Khiroya R, Kos Z, Abduljabbar K, Acosta Haab G, Acs B, Akturk G, Almeida JS, Alvarado-Cabrero I, Azmoudeh-Ardalan F, Badve S, Baharun NB, Bellolio ER, Bheemaraju V, Blenman KR, Botinelly Mendonça Fujimoto L, Bouchmaa N, Burgues O, Cheang MCU, Ciompi F, Cooper LA, Coosemans A, Corredor G, Dantas Portela FL, Deman F, Demaria S, Dudgeon SN, Elghazawy M, Ely S, Fernandez-Martín C, Fineberg S, Fox SB, Gallagher WM, Giltnane JM, Gnjatic S, Gonzalez-Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hardas A, Hart SN, Hartman J, Hewitt S, Hida AI, Horlings HM, Husain Z, Hytopoulos E, Irshad S, Janssen EA, Kahila M, Kataoka TR, Kawaguchi K, Kharidehal D, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Kovács A, Laenkholm AV, Lang-Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Ly A, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault-Llorca F, Perera RD, Pinard CJ, Pinto-Cardenas JC, Pruneri G, Pusztai L, Rahman A, Rajpoot NM, Rapoport BL, Rau TT, Reis-Filho JS, Ribeiro JM, Rimm D, Vincent-Salomon A, Salto-Tellez M, Saltz J, Sayed S, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, van Diest PJ, Verghese GE, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Adams S, Bartlett JMS, Loibl S, Denkert C, Savas P, Loi S, Salgado R, Specht Stovgaard E. Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer. J Pathol 2023; 260:514-532. [PMID: 37608771 DOI: 10.1002/path.6165] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/19/2023] [Indexed: 08/24/2023]
Abstract
Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- David B Page
- Earle A Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Glenn Broeckx
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Centre for Oncological Research (CORE), MIPPRO, Faculty of Medicine, Antwerp University, Antwerp, Belgium
| | - Chowdhury Arif Jahangir
- UCD School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Sara Verbandt
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Rajarsi R Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Jeppe Thagaard
- Technical University of Denmark, Kongens Lyngby, Denmark
- Visiopharm A/S, Hørsholm, Denmark
| | - Reena Khiroya
- Department of Cellular Pathology, University College Hospital, London, UK
| | - Zuzana Kos
- Department of Pathology and Laboratory Medicine, BC Cancer Vancouver Centre, University of British Columbia, Vancouver, BC, Canada
| | - Khalid Abduljabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | | | - Balazs Acs
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Guray Akturk
- Translational Molecular Biomarkers, Merck & Co Inc, Kenilworth, NJ, USA
| | - Jonas S Almeida
- National Cancer Institute, Division of Cancer Epidemiology and Genetics (DCEG), Rockville, MD, USA
| | | | | | - Sunil Badve
- Pathology and Laboratory Medicine, Emory University School of Medicine, Emory University Winship Cancer Institute, Atlanta, GA, USA
| | | | - Enrique R Bellolio
- Departamento de Anatomía Patológica, Facultad de Medicina, Universidad de La Frontera, Temuco, Chile
| | | | - Kim Rm Blenman
- Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | | | - Najat Bouchmaa
- Institute of Biological Sciences, Faculty of Medical Sciences, Mohammed VI Polytechnic University (UM6P), Ben-Guerir, Morocco
| | - Octavio Burgues
- Pathology Department, Hospital Cliníco Universitario de Valencia/Incliva, Valencia, Spain
| | - Maggie Chon U Cheang
- Head of Integrative Genomics Analysis in Clinical Trials, ICR-CTSU, Division of Clinical Studies, Institute of Cancer Research, London, UK
| | - Francesco Ciompi
- Radboud University Medical Center, Department of Pathology, Nijmegen, The Netherlands
| | - Lee Ad Cooper
- Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - An Coosemans
- Department of Oncology, Laboratory of Tumor Immunology and Immunotherapy, KU Leuven, Leuven, Belgium
| | - Germán Corredor
- Biomedical Engineering Department, Emory University, Atlanta, GA, USA
| | | | - Frederik Deman
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Sandra Demaria
- Department of Radiation Oncology, Weill Cornell Medical College, New York, NY, USA
- Department of Pathology, Weill Cornell Medicine, New York, NY, USA
| | - Sarah N Dudgeon
- Conputational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Mahmoud Elghazawy
- University of Surrey, Guildford, UK
- Ain Shams University, Cairo, Egypt
| | - Scott Ely
- Translational Pathology, Translational Sciences and Diagnostics/Translational Medicine/R&D, Bristol Myers Squibb, Princeton, NJ, USA
| | - Claudio Fernandez-Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
| | - Susan Fineberg
- Montefiore Medical Center and the Albert Einstein College of Medicine, New York, NY, USA
| | - Stephen B Fox
- Department of Pathology, Peter MacCallum Cancer Centre and Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | | | - Sacha Gnjatic
- Department of Oncological Sciences, Medicine Hem/Onc, and Pathology, Tisch Cancer Institute - Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Niels Halama
- Translational Immunotherapy, German Cancer Research Center, Heidelberg, Germany
| | | | | | - Alexandros Hardas
- Pathobiology & Population Sciences, The Royal Veterinary College, London, UK
| | - Steven N Hart
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Johan Hartman
- Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Stephen Hewitt
- Department of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Akira I Hida
- Department of Pathology, Matsuyama Shimin Hospital, Matsuyama, Japan
| | - Hugo M Horlings
- Division of Pathology, Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | | | | | - Sheeba Irshad
- King's College London & Guy's & St Thomas' NHS Trust, London, UK
| | - Emiel Am Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | - Mohamed Kahila
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Kosuke Kawaguchi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Durga Kharidehal
- Department of Pathology, Narayana Medical College, Nellore, India
| | - Andrey I Khramtsov
- Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | - Pawan Kirtani
- Department of Histopathology, Aakash Healthcare Super Speciality Hospital, New Delhi, India
| | - Liudmila L Kodach
- Department of Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Konstanty Korski
- Data, Analytics and Imaging, Product Development, F.Hoffmann-La Roche AG, Basel, Switzerland
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anne-Vibeke Laenkholm
- Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
- Surgical Pathology, University of Copenhagen, Copenhagen, Denmark
| | - Corinna Lang-Schwarz
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Denis Larsimont
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Marvin Lerousseau
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM, U900, Paris, France
| | - Xiaoxian Li
- Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Anant Madabhushi
- Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics, Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sai K Maley
- NRG Oncology/NSABP Foundation, Pittsburgh, PA, USA
| | | | - Douglas K Marks
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Elizabeth S McDonald
- Breast Cancer Translational Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Mehrotra
- Indian Cancer Genome Atlas, Pune, India
- Centre for Health, Innovation and Policy Foundation, Noida, India
| | - Stefan Michiels
- Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, Inserm, University Paris-Saclay, Ligue Contre le Cancer labeled Team, Villejuif, France
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, Warwick Cancer Research Centre, PathLAKE Consortium, Department of Computer Science, University of Warwick, Coventry, UK
| | - Shachi Mittal
- Department of Chemical Engineering, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - David A Moore
- CRUK Lung Cancer Centre of Excellence, UCLH, London, UK
| | - Shamim Mushtaq
- Department of Biochemistry, Ziauddin University, Karachi, Pakistan
| | - Hussain Nighat
- Pathology and Laboratory Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Drammen Sykehus, Vestre Viken HF, Drammen, Norway
| | - Frederique Penault-Llorca
- Centre Jean Perrin, INSERM U1240, Imagerie Moléculaire et Stratégies Théranostiques, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Rashindrie D Perera
- School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Christopher J Pinard
- Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
- Department of Oncology, Lakeshore Animal Health Partners, Mississauga, ON, Canada
- Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), University of Guelph, Guelph, ON, Canada
| | | | - Giancarlo Pruneri
- Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Lajos Pusztai
- Yale Cancer Center, New Haven, CT, USA
- Department of Medical Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Arman Rahman
- UCD School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | | | - Bernardo Leon Rapoport
- The Medical Oncology Centre of Rosebank, Johannesburg, South Africa
- Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Tilman T Rau
- Institute of Pathology, University Hospital Düsseldorf and Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Jorge S Reis-Filho
- Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joana M Ribeiro
- Département de Médecine Oncologique, Institute Gustave Roussy, Villejuif, France
| | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | - Manuel Salto-Tellez
- Integrated Pathology Unit, Institute of Cancer Research, London, UK
- Precision Medicine Centre, Queen's University Belfast, Belfast, UK
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, New York, NY, USA
| | - Shahin Sayed
- Department of Pathology, Aga Khan University, Nairobi, Kenya
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Brussels, Belgium
- Medical Oncology Department, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles, Brussels, Belgium
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| | | | - Daniel Sur
- Department of Medical Oncology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania
| | - Fraser Symmans
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sabine Tejpar
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jonas Teuwen
- AI for Oncology Lab, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Trine Tramm
- Pathology, and Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - William T Tran
- Department of Radiation Oncology, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
- Johns Hopkins Oncology Center, Baltimore, MD, USA
| | - Gregory E Verghese
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Giuseppe Viale
- Department of Pathology, European Institute of Oncology & University of Milan, Milan, Italy
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Thomas Walter
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM, U900, Paris, France
| | | | - Hannah Y Wen
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wentao Yang
- Fudan Medical University Shanghai Cancer Center, Shanghai, PR China
| | - Yinyin Yuan
- Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sylvia Adams
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
- Department of Medicine, NYU Grossman School of Medicine, Manhattan, NY, USA
| | | | - Sibylle Loibl
- Department of Medicine and Research, German Breast Group, Neu-Isenburg, Germany
| | - Carsten Denkert
- Institut für Pathologie, Philipps-Universität Marburg und Universitätsklinikum Marburg, Marburg, Germany
| | - Peter Savas
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Elisabeth Specht Stovgaard
- Department of Pathology, Herlev and Gentofte Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
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Choi H, Ahn SG, Bae SJ, Kim JH, Eun NL, Lee Y, Nahm JH, Jeong J, Cha YJ. Comparison of Programmed Cell Death Ligand 1 Status between Core Needle Biopsy and Surgical Specimens of Triple-Negative Breast Cancer. Yonsei Med J 2023; 64:518-525. [PMID: 37488704 PMCID: PMC10375241 DOI: 10.3349/ymj.2023.0090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Pembrolizumab is currently used to treat advanced triple-negative breast cancer (TNBC) and high-risk early TNBC with neoadjuvant chemotherapy (NAC). The tumor-infiltrating lymphocyte (TIL) level and programmed cell death ligand 1 (PD-L1) status are predictors of response to NAC and immune checkpoint inhibitor treatment. We aimed to investigate whether the PD-L1 status in core needle biopsies (CNBs) could represent the whole tumor in TNBC. MATERIALS AND METHODS A total of 49 patients diagnosed with TNBC who received upfront surgery without NAC between January 2018 and March 2021 were included. The PD-L1 expression (SP142 and 22C3 clones) and TIL were evaluated in paired CNBs and resected specimens. The concordance PD-L1 status and TIL levels between CNBs and resected specimens were analyzed. RESULTS PD-L1 positivity was more frequently observed in resected specimens. The overall reliability of TIL level in the CNB was good [intraclass correlation coefficient (ICC)=0.847, p<0.001]. The agreements of PD-L1 status were good and fair, respectively (SP142, κ=0.503, p<0.001; 22C3, κ=0.380, p=0.010). As the core number of CNB increased, the reliability and agreement also improved, especially from five tumor cores (TIL, ICC=0.911, p<0.001; PD-L1 [22C3], κ=0.750, p=0.028). Regarding PD-L1 (SP142), no further improvement was observed with ≥5 tumor cores (κ=0.600, p=0.058). CONCLUSION CNBs with ≥5 tumor cores were sufficient to represent the TIL level and PD-L1 (22C3) status in TNBC.
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Affiliation(s)
- Hyungwook Choi
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Gwe Ahn
- Department of Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Soong Joon Bae
- Department of Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Jee Hung Kim
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea
- Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Na Lae Eun
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - Yangkyu Lee
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Hae Nahm
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Joon Jeong
- Department of Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Yoon Jin Cha
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, Korea.
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Porta FM, Sajjadi E, Venetis K, Frascarelli C, Cursano G, Guerini-Rocco E, Fusco N, Ivanova M. Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods. J Pers Med 2023; 13:1176. [PMID: 37511789 PMCID: PMC10381494 DOI: 10.3390/jpm13071176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Triple-negative breast cancer (TNBC) poses a significant challenge in terms of prognosis and disease recurrence. The limited treatment options and the development of resistance to chemotherapy make it particularly difficult to manage these patients. However, recent research has been shifting its focus towards biomarker-based approaches for TNBC, with a particular emphasis on the tumor immune landscape. Immune biomarkers in TNBC are now a subject of great interest due to the presence of tumor-infiltrating lymphocytes (TILs) in these tumors. This characteristic often coincides with the presence of PD-L1 expression on both neoplastic cells and immune cells within the tumor microenvironment. Furthermore, a subset of TNBC harbor mismatch repair deficient (dMMR) TNBC, which is frequently accompanied by microsatellite instability (MSI). All of these immune biomarkers hold actionable potential for guiding patient selection in immunotherapy. To fully capitalize on these opportunities, the identification of additional or complementary biomarkers and the implementation of highly customized testing strategies are of paramount importance in TNBC. In this regard, this article aims to provide an overview of the current state of the art in immune-related biomarkers for TNBC. Specifically, it focuses on the various testing methodologies available and sheds light on the immediate future perspectives for patient selection. By delving into the advancements made in understanding the immune landscape of TNBC, this study aims to contribute to the growing body of knowledge in the field. The ultimate goal is to pave the way for the development of more personalized testing strategies, ultimately improving outcomes for TNBC patients.
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Affiliation(s)
- Francesca Maria Porta
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Elham Sajjadi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Konstantinos Venetis
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giulia Cursano
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
| | - Elena Guerini-Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mariia Ivanova
- Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy
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18
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Kumarguru BN, Ramaswamy AS, Arathi CA, Swathi D. Utility of Indigenously Developed Square Grid Method for Evaluation of Tumor-Stroma Ratio and Stromal Tumor-Infiltrating Lymphocytes in Invasive Breast Carcinoma: A Pilot Study. Iran J Pathol 2023; 18:335-346. [PMID: 37942205 PMCID: PMC10628375 DOI: 10.30699/ijp.2023.1989528.3063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/01/2023] [Indexed: 11/10/2023]
Abstract
Background & Objective Invasive breast carcinoma (IBC) is the most commonly diagnosed cancer among women in India. The conventional visual method of evaluation of Tumor-Stroma Ratio (TSR) and Stromal Tumor-Infiltrating Lymphocytes (sTIL) appears to be subjective. The present study aims to evaluate the utility of the indigenously designed square grid method for the evaluation of tumor-stroma ratio and stromal tumor-infiltrating lymphocytes in invasive breast carcinoma by assessing the inter-observer variability. Methods This was a retrospective study conducted at a rural tertiary care referral institute from July 2018 to June 2020. In each case, microphotographs were taken from 10 representative fields in H&E-stained sections for evaluating TSR in low-power and sTIL in high-power. Both the parameters were evaluated employing an indigenously designed square grid applied onto microphotographs in the power-point slides by making use of principles of the Pythagorean theorem. Both parameters were separately evaluated by two pathologists. Cohen kappa statistics was the statistical tool used to analyze inter-observer variability. Results Thirty cases were analyzed. Invasive breast carcinoma of no special type (IBC-NST) was the most common histopathological type (26 cases (86.67%)). For TRS evaluation, a Kappa value of 0.78 suggested substantial agreement with an agreement of 91.67%. For sTIL evaluation, a Kappa value of 0.51 suggested moderate agreement with an agreement of 88.33%. The P-values were statistically highly significant (P<0.001). Conclusion Square grid method is a novel technique for evaluating TSR and sTIL in invasive breast carcinoma. It can be considered an example of the application of Pythagoras' theorem in Pathology.
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Affiliation(s)
- B N Kumarguru
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - A S Ramaswamy
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - C A Arathi
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - D Swathi
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
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19
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Yosofvand M, Khan SY, Dhakal R, Nejat A, Moustaid-Moussa N, Rahman RL, Moussa H. Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides. Cancers (Basel) 2023; 15:3635. [PMID: 37509295 PMCID: PMC10377197 DOI: 10.3390/cancers15143635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.
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Affiliation(s)
- Mohammad Yosofvand
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Sonia Y Khan
- Breast Center of Excellence, Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Rabin Dhakal
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Ali Nejat
- Department of Civil, Environmental, & Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Naima Moustaid-Moussa
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA
- Obesity Research Institute, Texas Tech University, Lubbock, TX 79409, USA
| | - Rakhshanda Layeequr Rahman
- Breast Center of Excellence, Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Hanna Moussa
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA
- Obesity Research Institute, Texas Tech University, Lubbock, TX 79409, USA
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20
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Page DB, Pucilowska J, Chun B, Kim I, Sanchez K, Moxon N, Mellinger S, Wu Y, Koguchi Y, Conrad V, Redmond WL, Martel M, Sun Z, Campbell MB, Conlin A, Acheson A, Basho R, McAndrew P, El-Masry M, Park D, Bennetts L, Seitz RS, Nielsen TJ, McGregor K, Rajamanickam V, Bernard B, Urba WJ, McArthur HL. A phase Ib trial of pembrolizumab plus paclitaxel or flat-dose capecitabine in 1st/2nd line metastatic triple-negative breast cancer. NPJ Breast Cancer 2023; 9:53. [PMID: 37344474 DOI: 10.1038/s41523-023-00541-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/21/2023] [Indexed: 06/23/2023] Open
Abstract
Chemoimmunotherapy with anti-programmed cell death 1/ligand 1 and cytotoxic chemotherapy is a promising therapeutic modality for women with triple-negative breast cancer, but questions remain regarding optimal chemotherapy backbone and biomarkers for patient selection. We report final outcomes from a phase Ib trial evaluating pembrolizumab (200 mg IV every 3 weeks) with either weekly paclitaxel (80 mg/m2 weekly) or flat-dose capecitabine (2000 mg orally twice daily for 7 days of every 14-day cycle) in the 1st/2nd line setting. The primary endpoint is safety (receipt of 2 cycles without grade III/IV toxicities requiring discontinuation or ≥21-day delays). The secondary endpoint is efficacy (week 12 objective response). Exploratory aims are to characterize immunologic effects of treatment over time, and to evaluate novel biomarkers. The trial demonstrates that both regimens meet the pre-specified safety endpoint (paclitaxel: 87%; capecitabine: 100%). Objective response rate is 29% for pembrolizumab/paclitaxel (n = 4/13, 95% CI: 10-61%) and 43% for pembrolizumab/capecitabine (n = 6/14, 95% CI: 18-71%). Partial responses are observed in two subjects with chemo-refractory metaplastic carcinoma (both in capecitabine arm). Both regimens are associated with significant peripheral leukocyte contraction over time. Response is associated with clinical PD-L1 score, non-receipt of prior chemotherapy, and the H&E stromal tumor-infiltrating lymphocyte score, but also by a novel 27 gene IO score and spatial biomarkers (lymphocyte spatial skewness). In conclusion, pembrolizumab with paclitaxel or capecitabine is safe and clinically active. Both regimens are lymphodepleting, highlighting the competing immunostimulatory versus lymphotoxic effects of cytotoxic chemotherapy. Further exploration of the IO score and spatial TIL biomarkers is warranted. The clinical trial registration is NCT02734290.
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Affiliation(s)
- David B Page
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA.
| | - Joanna Pucilowska
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Brie Chun
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Isaac Kim
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Katherine Sanchez
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Nicole Moxon
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Staci Mellinger
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Yaping Wu
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Yoshinobu Koguchi
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Valerie Conrad
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - William L Redmond
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Maritza Martel
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Zhaoyu Sun
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Mary B Campbell
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Alison Conlin
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Anupama Acheson
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Reva Basho
- Cedars Sinai Medical Center, Los Angeles, CA, USA
- Ellison Institute for Transformative Medicine, Los Angeles, CA, USA
| | | | | | - Dorothy Park
- Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Laura Bennetts
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | | | | | | | | | - Brady Bernard
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Walter J Urba
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Heather L McArthur
- Cedars Sinai Medical Center, Los Angeles, CA, USA
- UT Southwestern Medical Center, Dallas, TX, USA
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21
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Fanucci KA, Bai Y, Pelekanou V, Nahleh ZA, Shafi S, Burela S, Barlow WE, Sharma P, Thompson AM, Godwin AK, Rimm DL, Hortobagyi GN, Liu Y, Wang L, Wei W, Pusztai L, Blenman KRM. Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial. NPJ Breast Cancer 2023; 9:38. [PMID: 37179362 PMCID: PMC10182981 DOI: 10.1038/s41523-023-00535-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/11/2023] [Indexed: 05/15/2023] Open
Abstract
We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm2)/stromal area(mm2)] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.
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Affiliation(s)
- Kristina A Fanucci
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06520, USA
| | - Yalai Bai
- Department of Pathology, Yale School of Medicine, 310 Cedar St, New Haven, CT, 06520, USA
| | - Vasiliki Pelekanou
- Department of Pathology, Yale School of Medicine, 310 Cedar St, New Haven, CT, 06520, USA
- Bayer Pharmaceuticals, 245 First St Cambridge Science Center 100 and 200 Floors 1 and 2, Cambridge, MA, 02142, USA
| | - Zeina A Nahleh
- Department of Hematology/Oncology, Cleveland Clinic Florida, Maroone Cancer Center, 2950 Cleveland Clinic Blvd, Weston, FL, 33331, USA
| | - Saba Shafi
- Department of Pathology, Yale School of Medicine, 310 Cedar St, New Haven, CT, 06520, USA
- Department of Pathology, Ohio State University, 6100 Optometry Clinic & Health Sciences Faculty Office Building, 1664 Neil Avenue, Columbus, OH, 43210, USA
| | - Sneha Burela
- Department of Pathology, Yale School of Medicine, 310 Cedar St, New Haven, CT, 06520, USA
| | - William E Barlow
- SWOG Statistics and Data Management Center, 1730 Minor Avenue Suite 1900, Seattle, WA, 98101, USA
| | - Priyanka Sharma
- Department of Medical Oncology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS, 66160, USA
| | - Alastair M Thompson
- Section of Breast Surgery, 1 Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Andrew K Godwin
- Department of Medical Oncology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS, 66160, USA
| | - David L Rimm
- Department of Pathology, Yale School of Medicine, 310 Cedar St, New Haven, CT, 06520, USA
| | - Gabriel N Hortobagyi
- Department of Breast Medical Oncology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Yihan Liu
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Leona Wang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Wei Wei
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Lajos Pusztai
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06520, USA
| | - Kim R M Blenman
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, 333 Cedar St, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale School of Engineering and Applied Science, 17 Hillhouse Avenue, New Haven, CT, 06520, USA.
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22
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Verdicchio M, Brancato V, Cavaliere C, Isgrò F, Salvatore M, Aiello M. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon 2023; 9:e14371. [PMID: 36950640 PMCID: PMC10025040 DOI: 10.1016/j.heliyon.2023.e14371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Background and objectives The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.
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Affiliation(s)
| | - Valentina Brancato
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
- Corresponding author.
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Claudio 21, Naples, 80125, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
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23
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Cserni B, Kilmartin D, O’Loughlin M, Andreu X, Bagó-Horváth Z, Bianchi S, Chmielik E, Figueiredo P, Floris G, Foschini MP, Kovács A, Heikkilä P, Kulka J, Laenkholm AV, Liepniece-Karele I, Marchiò C, Provenzano E, Regitnig P, Reiner A, Ryška A, Sapino A, Stovgaard ES, Quinn C, Zolota V, Webber M, Glynn SA, Bori R, Csörgő E, Oláh-Németh O, Pancsa T, Sejben A, Sejben I, Vörös A, Zombori T, Nyári T, Callagy G, Cserni G. ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations. Cancers (Basel) 2023; 15:cancers15041199. [PMID: 36831541 PMCID: PMC9954449 DOI: 10.3390/cancers15041199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation. This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists. In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2-11) are most commonly needed to give a robust estimate of reproducibility. In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses.
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Affiliation(s)
- Bálint Cserni
- TNG Technology Consulting GmbH, Király u. 26., 1061 Budapest, Hungary
| | - Darren Kilmartin
- Discipline of Pathology, Lambe Institute for Translational Research, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Mark O’Loughlin
- Discipline of Pathology, Lambe Institute for Translational Research, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Xavier Andreu
- Pathology Department, Atryshealth Co., Ltd., 08039 Barcelona, Spain
| | - Zsuzsanna Bagó-Horváth
- Department of Pathology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Simonetta Bianchi
- Division of Pathological Anatomy, Department of Health Sciences, University of Florence, 50134 Florence, Italy
| | - Ewa Chmielik
- Tumor Pathology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland
| | - Paulo Figueiredo
- Laboratório de Anatomia Patológica, IPO Coimbra, 3000-075 Coimbra, Portugal
| | - Giuseppe Floris
- Laboratory of Translational Cell & Tissue Research and KU Leuven, Department of Imaging and Pathology, Department of Pathology, University Hospitals Leuven, University of Leuven, Oude Market 13, 3000 Leuven, Belgium
| | - Maria Pia Foschini
- Unit of Anatomic Pathology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bellaria Hospital, 40139 Bologna, Italy
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
| | - Päivi Heikkilä
- Department of Pathology, Helsinki University Central Hospital, 00029 Helsinki, Finland
| | - Janina Kulka
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University Budapest, Üllői út 93, 1091 Budapest, Hungary
| | - Anne-Vibeke Laenkholm
- Department of Surgical Pathology, Zealand University Hospital, 4000 Roskilde, Denmark
| | - Inta Liepniece-Karele
- Department of Pathology, Riga Stradins University, Riga East Clinical University Hospital, LV-1038 Riga, Latvia
| | - Caterina Marchiò
- Unit of Pathology, Candiolo Cancer Institute FPO-IRCCS, 10060 Candiolo, Italy
- Department of Medical Sciences, University of Turin, 10126 Turin, Italy
| | - Elena Provenzano
- Department of Histopathology, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge CB2 0QQ, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University of Graz, 8010 Graz, Austria
| | - Angelika Reiner
- Department of Pathology, Klinikum Donaustadt, 1090 Vienna, Austria
| | - Aleš Ryška
- The Fingerland Department of Pathology, Charles University Medical Faculty and University Hospital, 50003 Hradec Kralove, Czech Republic
| | - Anna Sapino
- Unit of Pathology, Candiolo Cancer Institute FPO-IRCCS, 10060 Candiolo, Italy
- Department of Medical Sciences, University of Turin, 10126 Turin, Italy
| | | | - Cecily Quinn
- Department of Histopathology, Irish National Breast Screening Programme, BreastCheck, St. Vincent’s University Hospital and School of Medicine, University College Dublin, D04 T6F4 Dublin, Ireland
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Vasiliki Zolota
- Department of Pathology, School of Medicine, University of Patras, 26504 Rion, Greece
| | - Mark Webber
- Discipline of Pathology, Lambe Institute for Translational Research, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Sharon A. Glynn
- Discipline of Pathology, Lambe Institute for Translational Research, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Rita Bori
- Department of Pathology, Bács-Kiskun County Teaching Hospital, 6000 Kecskemét, Hungary
| | - Erika Csörgő
- Department of Pathology, Bács-Kiskun County Teaching Hospital, 6000 Kecskemét, Hungary
| | | | - Tamás Pancsa
- Department of Pathology, University of Szeged, 6720 Szeged, Hungary
| | - Anita Sejben
- Department of Pathology, University of Szeged, 6720 Szeged, Hungary
| | - István Sejben
- Department of Pathology, Bács-Kiskun County Teaching Hospital, 6000 Kecskemét, Hungary
| | - András Vörös
- Department of Pathology, University of Szeged, 6720 Szeged, Hungary
| | - Tamás Zombori
- Department of Pathology, University of Szeged, 6720 Szeged, Hungary
| | - Tibor Nyári
- Department of Medical Physics and Informatics, University of Szeged, 6720 Szeged, Hungary
| | - Grace Callagy
- Discipline of Pathology, Lambe Institute for Translational Research, School of Medicine, University of Galway, H91 TK33 Galway, Ireland
| | - Gábor Cserni
- Department of Pathology, Bács-Kiskun County Teaching Hospital, 6000 Kecskemét, Hungary
- Department of Pathology, University of Szeged, 6720 Szeged, Hungary
- Correspondence:
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24
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Miyagawa C, Nakai H, Otani T, Murakami R, Takamura S, Takaya H, Murakami K, Mandai M, Matsumura N. Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging. J Gynecol Oncol 2023:34.e47. [PMID: 36807749 DOI: 10.3802/jgo.2023.34.e47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/02/2023] [Accepted: 01/18/2023] [Indexed: 02/16/2023] Open
Abstract
OBJECTIVE We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization. METHODS Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis. RESULTS After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type. CONCLUSION We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.
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Affiliation(s)
- Chiho Miyagawa
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Hidekatsu Nakai
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan.
| | - Tomoyuki Otani
- Department of Pathology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Ryusuke Murakami
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shiki Takamura
- Department of Immunology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Hisamitsu Takaya
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Kosuke Murakami
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Noriomi Matsumura
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
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Wang X, Collet L, Rediti M, Debien V, De Caluwé A, Venet D, Romano E, Rothé F, Sotiriou C, Buisseret L. Predictive Biomarkers for Response to Immunotherapy in Triple Negative Breast Cancer: Promises and Challenges. J Clin Med 2023; 12. [PMID: 36769602 DOI: 10.3390/jcm12030953] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/20/2023] [Accepted: 01/22/2023] [Indexed: 01/28/2023] Open
Abstract
Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.
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Valenza C, Taurelli Salimbeni B, Santoro C, Trapani D, Antonarelli G, Curigliano G. Tumor Infiltrating Lymphocytes across Breast Cancer Subtypes: Current Issues for Biomarker Assessment. Cancers (Basel) 2023; 15:cancers15030767. [PMID: 36765724 PMCID: PMC9913599 DOI: 10.3390/cancers15030767] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/17/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) represent a surrogate biomarker of anti-tumor, lymphocyte-mediated immunity. In early, triple-negative breast cancer, TILs have level 1B of evidence to predict clinical outcomes. TILs represent a promising biomarker to select patients who can experience a better prognosis with de-intensified cancer treatments and derive larger benefits from immune checkpoint inhibitors. However, the assessment and the validation of TILs as a biomarker require a prospective and rigorous demonstration of its clinical validity and utility, provided reproducible analytical performance. With pending data about the prospective validation of TILs' clinical validity to modulate treatments in early breast cancer, this review summarizes the most important current issues and future challenges related to the implementation of TILs assessments across all breast cancer subtypes and their potential integration into clinical practice.
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Affiliation(s)
- Carmine Valenza
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Beatrice Taurelli Salimbeni
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Celeste Santoro
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Dario Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Gabriele Antonarelli
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
- Correspondence: ; Tel.: +39-02-5748-9599
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Onkar SS, Carleton NM, Lucas PC, Bruno TC, Lee AV, Vignali DAA, Oesterreich S. The Great Immune Escape: Understanding the Divergent Immune Response in Breast Cancer Subtypes. Cancer Discov 2023; 13:23-40. [PMID: 36620880 PMCID: PMC9833841 DOI: 10.1158/2159-8290.cd-22-0475] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/30/2022] [Accepted: 09/26/2022] [Indexed: 12/12/2022]
Abstract
Breast cancer, the most common type of cancer affecting women, encompasses a collection of histologic (mainly ductal and lobular) and molecular subtypes exhibiting diverse clinical presentation, disease trajectories, treatment options, and outcomes. Immunotherapy has revolutionized treatment for some solid tumors but has shown limited promise for breast cancers. In this review, we summarize recent advances in our understanding of the complex interactions between tumor and immune cells in subtypes of breast cancer at the cellular and microenvironmental levels. We aim to provide a perspective on opportunities for future immunotherapy agents tailored to specific features of each subtype of breast cancer. SIGNIFICANCE Although there are currently over 200 ongoing clinical trials testing immunotherapeutics, such as immune-checkpoint blockade agents, these are largely restricted to the triple-negative and HER2+ subtypes and primarily focus on T cells. With the rapid expansion of new in vitro, in vivo, and clinical data, it is critical to identify and highlight the challenges and opportunities unique for each breast cancer subtype to drive the next generation of treatments that harness the immune system.
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Affiliation(s)
- Sayali S. Onkar
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Graduate Program of Microbiology and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Neil M. Carleton
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peter C Lucas
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Tullia C Bruno
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Adrian V Lee
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Dario AA Vignali
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Steffi Oesterreich
- Women’s Cancer Research Center, Magee-Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Cancer Biology Program, UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
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Rakaee M, Adib E, Ricciuti B, Sholl LM, Shi W, Alessi JV, Cortellini A, Fulgenzi CAM, Viola P, Pinato DJ, Hashemi S, Bahce I, Houda I, Ulas EB, Radonic T, Väyrynen JP, Richardsen E, Jamaly S, Andersen S, Donnem T, Awad MM, Kwiatkowski DJ. Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC. JAMA Oncol 2023; 9:51-60. [PMID: 36394839 PMCID: PMC9673028 DOI: 10.1001/jamaoncol.2022.4933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/10/2022] [Indexed: 11/18/2022]
Abstract
Importance Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy. Objective To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC). Design, Setting, and Participants This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022. Exposures All patients received anti-PD-(L)1 monotherapy. Main Outcomes and Measures Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR. Results Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65). Conclusions and Relevance In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.
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Affiliation(s)
- Mehrdad Rakaee
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Department of Clinical Pathology, University Hospital of North Norway, Tromso, Norway
| | - Elio Adib
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Biagio Ricciuti
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Lynette M. Sholl
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Weiwei Shi
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Joao V. Alessi
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Alessio Cortellini
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Claudia A. M. Fulgenzi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Medical Oncology, University Campus Bio-Medico, Rome, Italy
| | - Patrizia Viola
- Department of Cellular Pathology, Imperial College London NHS Trust, London, United Kingdom
| | - David J. Pinato
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Sayed Hashemi
- Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands
| | - Idris Bahce
- Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ilias Houda
- Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ezgi B. Ulas
- Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands
| | - Teodora Radonic
- Department of Pathology, Amsterdam UMC, Amsterdam, the Netherlands
| | - Juha P. Väyrynen
- Cancer and Translational Medicine Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Elin Richardsen
- Department of Clinical Pathology, University Hospital of North Norway, Tromso, Norway
| | - Simin Jamaly
- Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway
| | - Sigve Andersen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Department of Oncology, University Hospital of North Norway, Tromso, Norway
| | - Tom Donnem
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Department of Oncology, University Hospital of North Norway, Tromso, Norway
| | - Mark M. Awad
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - David J. Kwiatkowski
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
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Yi M, Wu Y, Niu M, Zhu S, Zhang J, Yan Y, Zhou P, Dai Z, Wu K. Anti-TGF-β/PD-L1 bispecific antibody promotes T cell infiltration and exhibits enhanced antitumor activity in triple-negative breast cancer. J Immunother Cancer 2022; 10:jitc-2022-005543. [PMID: 36460337 PMCID: PMC9723957 DOI: 10.1136/jitc-2022-005543] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Agents blocking programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) have been approved for triple-negative breast cancer (TNBC). However, the response rate of anti-PD-1/PD-L1 is still unsatisfactory, partly due to immunosuppressive factors such as transforming growth factor-beta (TGF-β). In our previous pilot study, the bispecific antibody targeting TGF-β and murine PD-L1 (termed YM101) showed potent antitumor effect. In this work, we constructed a bispecific antibody targeting TGF-β and human PD-L1 (termed BiTP) and explored the antitumor effect of BiTP in TNBC. METHODS BiTP was developed using Check-BODYTM bispecific platform. The binding affinity of BiTP was measured by surface plasmon resonance, ELISA, and flow cytometry. The bioactivity was assessed by Smad and NFAT luciferase reporter assays, immunofluorescence, western blotting, and superantigen stimulation assays. The antitumor activity of BiTP was explored in humanized epithelial-mesenchymal transition-6-hPDL1 and 4T1-hPDL1 murine TNBC models. Immunohistochemical staining, flow cytometry, and bulk RNA-seq were used to investigate the effect of BiTP on immune cell infiltration. RESULTS BiTP exhibited high binding affinity to dual targets. In vitro experiments verified that BiTP effectively counteracted TGF-β-Smad and PD-L1-PD-1-NFAT signaling. In vivo animal experiments demonstrated that BiTP had superior antitumor activity relative to anti-PD-L1 and anti-TGF-β monotherapy. Mechanistically, BiTP decreased collagen deposition, enhanced CD8+ T cell penetration, and increased tumor-infiltrating lymphocytes. This improved tumor microenvironment contributed to the potent antitumor activity of BiTP. CONCLUSION BiTP retains parent antibodies' binding affinity and bioactivity, with superior antitumor activity to parent antibodies in TNBC. Our data suggest that BiTP might be a promising agent for TNBC treatment.
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Affiliation(s)
- Ming Yi
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuze Wu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengke Niu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuangli Zhu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Zhang
- Wuhan YZY Biopharma Co Ltd, Wuhan, China
| | | | | | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Kongming Wu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
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Capobianco E. Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research. Ann Transl Med 2022; 10:1300. [PMID: 36660729 PMCID: PMC9843365 DOI: 10.21037/atm-22-5477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/15/2022] [Indexed: 01/01/2023]
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Zheng Q, Yang R, Ni X, Yang S, Jiao P, Wu J, Xiong L, Wang J, Jian J, Jiang Z, Wang L, Chen Z, Liu X. Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer. J Clin Med 2022; 11:jcm11237081. [PMID: 36498655 PMCID: PMC9739988 DOI: 10.3390/jcm11237081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software. We defined four unique TILs variables based on ML to analyze TILs measurements. Pathological slide images from 133 MIBC patients were retrospectively collected as the discovery set to determine the optimal association of ML-read TILs variables with patient survival outcomes. For validation, we evaluated an independent external validation set consisting of 247 MIBC patients. (3) Results: We found that all four TILs variables had significant prognostic associations with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TILs score being associated with better prognosis. Univariate and multivariate Cox regression analyses demonstrated that electronic TILs (eTILs) variables were independently associated with overall survival after adjustment for clinicopathological factors including age, sex, and pathological stage (p < 0.001 for all analyses). Results analyzed in different subgroups showed that the eTILs variable was a strong prognostic factor that was not redundant with pre-existing clinicopathological features (p < 0.05 for all analyses). (4) Conclusion: ML-driven cell classifier-defined TILs variables were robust and independent prognostic factors in two independent cohorts of MIBC patients. eTILs have the potential to identify a subset of high-risk stage II or stage III-IV MIBC patients who might benefit from adjuvant immunotherapy.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lin Xiong
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingsong Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jun Jian
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhengyu Jiang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
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Cho HG, Cho SI, Choi S, Jung W, Shin J, Park G, Moon J, Ma M, Song H, Mostafavi M, Kang M, Pereira S, Paeng K, Yoo D, Ock CY, Kim S. Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms. Diagnostics (Basel) 2022; 12. [PMID: 36292028 DOI: 10.3390/diagnostics12102340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 12/03/2022] Open
Abstract
Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.
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Kolberg-Liedtke C, Feuerhake F, Garke M, Christgen M, Kates R, Grischke EM, Forstbauer H, Braun M, Warm M, Hackmann J, Uleer C, Aktas B, Schumacher C, Kuemmel S, Wuerstlein R, Graeser M, Nitz U, Kreipe H, Gluz O, Harbeck N. Impact of stromal tumor-infiltrating lymphocytes (sTILs) on response to neoadjuvant chemotherapy in triple-negative early breast cancer in the WSG-ADAPT TN trial. Breast Cancer Res 2022; 24:58. [PMID: 36056374 PMCID: PMC9438265 DOI: 10.1186/s13058-022-01552-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/25/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Higher density of stromal tumor-infiltrating lymphocytes (sTILs) at baseline has been associated with increased rates of pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). While evidence supports favorable association of pCR with survival in TNBC, an independent impact of sTILs (after adjustment for pCR) on survival is not yet established. Moreover, the impact of sTIL dynamics during NACT on pCR and survival in TNBC is unknown. METHODS The randomized WSG-ADAPT TN phase II trial compared efficacy of 12-week nab-paclitaxel with gemcitabine versus carboplatin. This preplanned translational analysis assessed impacts of sTIL measurements at baseline (sTIL-0) and after 3 weeks of chemotherapy (sTIL-3) on pCR and invasive disease-free survival (iDFS). Predictive performance of sTIL-0 and sTIL-3 for pCR was quantified by ROC analysis and logistic regression; Kaplan-Meier estimation and Cox regression (with mediation analysis) were used to determine their impact on iDFS. RESULTS For prediction of pCR, the AUC statistics for sTIL-0 and sTIL-3 were 0.60 and 0.63, respectively, in all patients; AUC for sTIL-3 was higher in NP/G. The positive predictive value (PPV) of "lymphocyte-predominant" status (sTIL-0 ≥ 60%) at baseline was 59.3%, though only 13.0% of patients had this status. To predict non-pCR, the cut point sTIL-0 ≤ 10% yielded PPV = 69.5% while addressing 33.8% of patients. Higher sTIL levels (particularly at 3 weeks) were independently and favorably associated with better iDFS, even after adjusting for pCR. For example, the adjusted hazard ratio for 3-week sTILs ≥ 60% (vs. < 60%) was 0.48 [0.23-0.99]. Low cellularity in 3-week biopsies was the strongest individual predictor for pCR (in both therapy arms), but not for iDFS. CONCLUSION The independent impact of sTILs on iDFS suggests that favorable immune response can influence key tumor biological processes for long-term survival. The results suggest that the reliability of pCR following neoadjuvant therapy as a surrogate for survival could vary among subgroups in TNBC defined by immune response or other factors. Dynamic measurements of sTILs under NACT could support immune response-guided patient selection for individualized therapy approaches for both very low levels (more effective therapies) and very high levels (de-escalation concepts). TRIAL REGISTRATION Clinical trials No: NCT01815242, retrospectively registered January 25, 2013.
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Affiliation(s)
- Cornelia Kolberg-Liedtke
- Department of Gynecology and Obstetrics, University Hospital Essen, Hufelandstrasse 55, 45147, Essen, Germany.
| | | | | | | | - Ronald Kates
- West German Study Group, Mönchengladbach, Germany
| | | | | | - Michael Braun
- Breast Center, Rotkreuz Clinics Munich, Munich, Germany
| | - Mathias Warm
- Breast Center, City Hospital Holweide, Cologne, Germany
| | | | | | - Bahriye Aktas
- Department of Gynecology, University Hospital Leipzig, Leipzig, Germany
| | | | - Sherko Kuemmel
- West German Study Group, Mönchengladbach, Germany.,Breast Unit, Kliniken Essen-Mitte, Essen, Germany.,Department of Gynecology with Breast Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Rachel Wuerstlein
- West German Study Group, Mönchengladbach, Germany.,Breast Center, LMU University Hospital, Munich, Germany
| | - Monika Graeser
- West German Study Group, Mönchengladbach, Germany.,University Hospital Hamburg-Eppendorf, Hamburg, Germany.,Breast Center Niederrhein, Ev. Hospital Bethesda, Mönchengladbach, Germany
| | - Ulrike Nitz
- West German Study Group, Mönchengladbach, Germany.,Breast Center Niederrhein, Ev. Hospital Bethesda, Mönchengladbach, Germany
| | - Hans Kreipe
- Institute of Pathology, Medical School Hannover, Hannover, Germany
| | - Oleg Gluz
- West German Study Group, Mönchengladbach, Germany.,Breast Center Niederrhein, Ev. Hospital Bethesda, Mönchengladbach, Germany
| | - Nadia Harbeck
- West German Study Group, Mönchengladbach, Germany.,Breast Center, LMU University Hospital, Munich, Germany
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Su T, Wang S, Huang S, Cai H, McKinley ET, Beeghly-Fadiel A, Zheng W, Shu XO, Cai Q. Multiplex immunohistochemistry and high-throughput image analysis for evaluation of spatial tumor immune cell markers in human breast cancer. Cancer Biomark 2022; 35:193-206. [PMID: 36093688 DOI: 10.3233/cbm-220071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The clinicopathological significance of spatial tumor-infiltrating lymphocytes (TILs) subpopulations is not well studied due to lack of high-throughput scalable methodology for studies with large human sample sizes. OBJECTIVE Establishing a cyclic fluorescent multiplex immunohistochemistry (mIHC/IF) method coupled with computer-assisted high-throughput quantitative analysis to evaluate associations of six TIL markers (CD3, CD8, CD20, CD56, FOXP3, and PD-L1) with clinicopathological factors of breast cancer. METHODS Our 5-plex mIHC/IF staining was shown to be reliable and highly sensitive for labeling three biomarkers per tissue section. Through repetitive cycles of 5-plex mIHC/IF staining, more than 12 biomarkers could be detected per single tissue section. Using open-source software CellProfiler, the measurement pipelines were successfully developed for high-throughput multiplex evaluation of intratumoral and stromal TILs. RESULTS In analyses of 188 breast cancer samples from the Nashville Breast Health Study, high-grade tumors showed significantly increased intratumoral CD3+CD8+ CTL density (P= 0.0008, false discovery rate (FDR) adjusted P= 0.0168) and intratumoral PD-L1 expression (P= 0.0061, FDR adjusted P= 0.0602) compared with low-grade tumors. CONCLUSIONS The high- and low-grade breast cancers exhibit differential immune responses which may have clinical significances. The multiplexed imaging quantification strategies established in this study are reliable, cost-efficient and applicable in regular laboratory settings for high-throughput tissue biomarker studies, especially retrospective and population-based studies using archived paraffin tissues.
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Affiliation(s)
- Timothy Su
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA.,Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Shuyang Wang
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA.,Department of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.,Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Shuya Huang
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA.,Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Hui Cai
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Cell and Development Biology, Vanderbilt University, Nashville, TN, USA
| | - Alicia Beeghly-Fadiel
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Wei Zheng
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Xiao-Ou Shu
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Qiuyin Cai
- Department of Medicine, Division of Epidemiology, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
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Sandarenu P, Millar EKA, Song Y, Browne L, Beretov J, Lynch J, Graham PH, Jonnagaddala J, Hawkins N, Huang J, Meijering E. Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images. Sci Rep 2022; 12:14527. [PMID: 36008541 PMCID: PMC9411153 DOI: 10.1038/s41598-022-18647-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/17/2022] [Indexed: 11/09/2022] Open
Abstract
Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.
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Affiliation(s)
- Piumi Sandarenu
- School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Ewan K A Millar
- Department of Anatomical Pathology, NSW Health Pathology, St. George Hospital, Kogarah, NSW, 2217, Australia.,St. George and Sutherland Clinical School, UNSW Sydney, Kensington, NSW, 2052, Australia.,Faculty of Medicine and Health Sciences, Sydney Western University, Campbelltown, NSW, 2560, Australia.,University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Yang Song
- School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Lois Browne
- Cancer Care Centre, St. George Hospital, Kogarah, NSW, 2217, Australia
| | - Julia Beretov
- Department of Anatomical Pathology, NSW Health Pathology, St. George Hospital, Kogarah, NSW, 2217, Australia.,St. George and Sutherland Clinical School, UNSW Sydney, Kensington, NSW, 2052, Australia.,Cancer Care Centre, St. George Hospital, Kogarah, NSW, 2217, Australia
| | - Jodi Lynch
- St. George and Sutherland Clinical School, UNSW Sydney, Kensington, NSW, 2052, Australia.,Cancer Care Centre, St. George Hospital, Kogarah, NSW, 2217, Australia
| | - Peter H Graham
- St. George and Sutherland Clinical School, UNSW Sydney, Kensington, NSW, 2052, Australia.,Cancer Care Centre, St. George Hospital, Kogarah, NSW, 2217, Australia
| | | | - Nicholas Hawkins
- School of Medical Sciences, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Junzhou Huang
- University of Texas at Arlington, Arlington, TX, 76019, USA
| | - Erik Meijering
- School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
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Aung TN, Shafi S, Wilmott JS, Nourmohammadi S, Vathiotis I, Gavrielatou N, Fernandez A, Yaghoobi V, Sinnberg T, Amaral T, Ikenberg K, Khosrotehrani K, Osman I, Acs B, Bai Y, Martinez-Morilla S, Moutafi M, Thompson JF, Scolyer RA, Rimm DL. Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms. EBioMedicine 2022; 82:104143. [PMID: 35810563 PMCID: PMC9272337 DOI: 10.1016/j.ebiom.2022.104143] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/12/2022] [Accepted: 06/21/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment. METHODS We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes. FINDINGS Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells. INTERPRETATION eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial. FUNDING This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.
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Affiliation(s)
- Thazin Nwe Aung
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Saba Shafi
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - James S Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Saeed Nourmohammadi
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Ioannis Vathiotis
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Niki Gavrielatou
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Aileen Fernandez
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Vesal Yaghoobi
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Tobias Sinnberg
- University Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", 72076 Tübingen, Germany
| | - Teresa Amaral
- University Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", 72076 Tübingen, Germany
| | - Kristian Ikenberg
- Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Kiarash Khosrotehrani
- University of Queensland, UQ Diamantina Institute, Brisbane, QLD, Australia; Department of Dermatology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Iman Osman
- Department of Medicine, Grossman School of Medicine, New York University, USA
| | - Balazs Acs
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA; Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
| | - Yalai Bai
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Myrto Moutafi
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - John F Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - David L Rimm
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA; Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, CT, USA.
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Laudus N, Nijs L, Nauwelaers I, Dequeker EMC. The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories. Cancers (Basel) 2022; 14:3686. [PMID: 35954349 PMCID: PMC9367251 DOI: 10.3390/cancers14153686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 11/18/2022] Open
Abstract
Simple Summary Patients and clinicians often rely on the outcome of laboratory tests, but can we really trust these test results? Good quality management is key for laboratories to guarantee reliable test results. This review focusses on external quality assessment (EQA) schemes which are a tool for laboratories to examine and improve the quality of their testing routines. In this review, an overview of the role and importance of EQA schemes for clinical laboratories is given, and different types of EQA schemes and EQA providers available on the market are discussed, as well as recent developments in the EQA landscape. Abstract External quality assessment (EQA) schemes are a tool for clinical laboratories to evaluate and manage the quality of laboratory practice with the support of an independent party (i.e., an EQA provider). Depending on the context, there are different types of EQA schemes available, as well as various EQA providers, each with its own field of expertise. In this review, an overview of the general requirements for EQA schemes and EQA providers based on international guidelines is provided. The clinical and scientific value of these kinds of schemes for clinical laboratories, clinicians and patients are highlighted, in addition to the support EQA can provide to other types of laboratories, e.g., laboratories affiliated to biotech companies. Finally, recent developments and challenges in laboratory medicine and quality management, for example, the introduction of artificial intelligence in the laboratory and the shift to a more individual-approach instead of a laboratory-focused approach, are discussed. EQA schemes should represent current laboratory practice as much as possible, which poses the need for EQA providers to introduce latest laboratory innovations in their schemes and to apply up-to-date guidelines. By incorporating these state-of-the-art techniques, EQA aims to contribute to continuous learning.
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Wu D, Hacking SM, Chavarria H, Abdelwahed M, Nasim M. Computational portraits of the tumoral microenvironment in human breast cancer. Virchows Arch 2022; 481:367-385. [PMID: 35821350 DOI: 10.1007/s00428-022-03376-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 06/21/2022] [Accepted: 06/29/2022] [Indexed: 11/24/2022]
Abstract
Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath to evaluate proportionated stromal area (PSA), myxoid stromal ratio (MSR), and immune stroma proportion (ISP) from whole slide images (WSI). The ML classifier was validated in independent training (n = 40) and validation (n = 109) cohorts finding MSR, PSA, and ISP to be associated with tumor stage, lymph node status, Nottingham grade, stromal differentiation (SD), tumor size, estrogen receptor (ER), progesterone receptor (PR), and receptor tyrosine-protein kinase erbB-2 (HER-2). Overall, MSR correlated better with the clinicopathologic profile than PSA and ISP. High MSR was found to be associated with high tumor stage, low ISP, and high Nottingham histologic score. As a computational biomarker, high MSR was more likely to be associated with luminal B like, Her-2 enriched, and triple-negative biomarker status when compared to luminal A like. The supervised ML superpixel approach demonstrated here can be performed by a trained pathologist to provide a faster and more uniformed approach to the analysis to the tumoral microenvironment (TME). The TME may be relevant for clinical decision-making, determining chemotherapeutic efficacy, and guiding a more overall precision-based breast cancer care.
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Affiliation(s)
- Dongling Wu
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Greenvale, NY, USA.
| | - Sean M Hacking
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA.,Translational Bioinformatics Lab, Brown University, Providence, RI, USA
| | - Hector Chavarria
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Greenvale, NY, USA
| | - Mohammed Abdelwahed
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Greenvale, NY, USA.,Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA.,Translational Bioinformatics Lab, Brown University, Providence, RI, USA.,Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mansoor Nasim
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
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Elfer K, Dudgeon S, Garcia V, Blenman K, Hytopoulos E, Wen S, Li X, Ly A, Werness B, Sheth MS, Amgad M, Gupta R, Saltz J, Hanna MG, Ehinger A, Peeters D, Salgado R, Gallas BD. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. J Med Imaging (Bellingham) 2022; 9:047501. [PMID: 35911208 PMCID: PMC9326105 DOI: 10.1117/1.jmi.9.4.047501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
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Affiliation(s)
- Katherine Elfer
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- National Institutes of Health, National Cancer Institute, Division of Cancer Prevention, Cancer Prevention Fellowship Program, Bethesda, Maryland, United States
| | - Sarah Dudgeon
- Yale University Computational Biology and Bioinformatics, New Haven, Connecticut, United States
- Yale New Haven Hospital, Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States
| | - Victor Garcia
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Kim Blenman
- School of Medicine, Yale Cancer Center, Department of Internal Medicine, Section of Medical Oncology, New Haven, Connecticut, United States
- Yale University, School of Engineering and Applied Science, Department of Computer Science, New Haven, Connecticut, United States
| | | | - Si Wen
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Xiaoxian Li
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Amy Ly
- Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Bruce Werness
- Inova Health System Department of Pathology, Falls Church, Virginia, United States
- Arrive Bio LLC, San Francisco, California, United States
| | - Manasi S. Sheth
- United States Food and Drug Administration (FDA), Center for Devices and Radiologic Health, Office of Product Evaluation and Quality, Office of Clinical Evidence and Analysis, Division of Biostatistics, White Oak, Maryland, United States
| | - Mohamed Amgad
- Northwestern University Feinberg School of Medicine, Department of Pathology, Chicago, Illinois, United States
| | - Rajarsi Gupta
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
| | - Joel Saltz
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
- SUNY Stony Brook Medicine, Department of Pathology, Stony Brook, New York, United States
| | - Matthew G. Hanna
- Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Anna Ehinger
- Lund University, Laboratory Medicine, Region Skåne, Department of Genetics and Pathology, Lund, Sweden
| | - Dieter Peeters
- Sint-Maarten Hospital, Department of Pathology, Mechelen, Belgium
- University of Antwerp, Department of Biomedical Sciences, Antwerp, Belgium
| | - Roberto Salgado
- Peter Mac Callum Cancer Centre, Division of Research, Melbourne, Australia
- GZA-ZNA Hospitals, Department of Pathology, Antwerp, Belgium
| | - Brandon D. Gallas
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- Address all correspondence to Brandon D. Gallas,
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Xiao Y, Gao W. Therapeutic pattern and progress of neoadjuvant treatment for triple-negative breast cancer. Oncol Lett 2022; 24:219. [PMID: 35720488 PMCID: PMC9178680 DOI: 10.3892/ol.2022.13340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 05/03/2022] [Indexed: 11/23/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is a heterogeneous disease, accounting for about 15.0-20.0% of all breast cancer cases. TNBC is associated with early recurrence and metastasis, strong invasiveness and a poor prognosis. Chemotherapy is currently the mainstay of treatment for TNBC, and achievement of a pathological complete response is closely associated with a long-term good prognosis. Improving the long-term prognosis in patients with TNBC is a challenge in breast cancer treatment, and more clinical evidence is needed to guide the choice of treatment strategies. The current study reviews the conventional treatment modality for TNBC and the selection of neoadjuvant chemotherapy (NACT) regimens available. The research progress on optimizing NACT regimens is also reviewed, and the uniqueness of the treatment of this breast cancer subtype is emphasized, in order to provide reference for the clinical practice and research with regard to TNBC treatment.
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Affiliation(s)
- Yan Xiao
- Department of Oncology, Dongguan Tungwah Hospital, Dongguan, Guangdong 523000, P.R. China
| | - Wencheng Gao
- Department of General Surgery, Dongguan Houjie Town People's Hospital, Dongguan, Guangdong 523962, P.R. China
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Čeprnja T, Mrklić I, Perić Balja M, Marušić Z, Blažićević V, Spagnoli GC, Juretić A, Čapkun V, Tečić Vuger A, Vrdoljak E, Tomić S. Prognostic Significance of Lymphocyte Infiltrate Localization in Triple-Negative Breast Cancer. J Pers Med 2022; 12:jpm12060941. [PMID: 35743725 PMCID: PMC9224650 DOI: 10.3390/jpm12060941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
High infiltration by tumor-infiltrating lymphocytes (TILs) is associated with favorable prognosis in different tumor types, but the clinical significance of their spatial localization within the tumor microenvironment is debated. To address this issue, we evaluated the accumulation of intratumoral TILs (itTILs) and stromal TILs (sTILs) in samples from 97 patients with early triple-negative breast cancer (TNBC) in the center (sTIL central) and periphery (sTIL peripheral) of tumor tissues. Moreover, the presence of primary and secondary lymphoid aggregates (LAs) and the expression levels of the cancer testis antigen (CTA), NY-ESO-1, and PD-L1 were explored. High infiltration by itTILs was observed in 12/97 samples (12.3%), unrelated to age, Ki67 expression, tumor size, histologic type and grade, and LA presence. NY-ESO-1 was expressed in tumor cells in 37 samples (38%), with a trend suggesting a correlation with itTIL infiltration (p = 0.0531). PD-L1 expression was detected in immune cells in 47 samples (49%) and was correlated with histologic grade, sTILs, and LA formation. The presence of primary LAs was significantly correlated with better disease-free survival (DFS) (p = 0.027). Moreover, no tumor progression was observed during >40 months of clinical follow up in the 12 patients with high itTILs or in the 14 patients with secondary LAs. Thus, careful evaluation of lymphoid infiltrate intratumoral localization might provide important prognostic information.
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Affiliation(s)
- Toni Čeprnja
- Department of Pathology, Forensic Medicine and Cytology, University Hospital Center Split, School of Medicine, University of Split, 21000 Split, Croatia; (I.M.); (S.T.)
- Correspondence:
| | - Ivana Mrklić
- Department of Pathology, Forensic Medicine and Cytology, University Hospital Center Split, School of Medicine, University of Split, 21000 Split, Croatia; (I.M.); (S.T.)
| | - Melita Perić Balja
- Department of Pathology, University Hospital Center “Sestre Milosrdnice”, 10000 Zagreb, Croatia;
| | - Zlatko Marušić
- Department of Pathology, Zagreb University Hospital Center, 10000 Zagreb, Croatia;
| | | | | | - Antonio Juretić
- Department of Oncology, Clinical Hospital “Sveti Duh”, School of Medicine, University of Zagreb, 10000 Zagreb, Croatia;
| | - Vesna Čapkun
- Department of Nuclear Medicine, University Hospital Centre Split, School of Medicine, University of Split, 21000 Split, Croatia;
| | - Ana Tečić Vuger
- Department of Oncology, University Hospital Center “Sestre Milosrdnice”, 10000 Zagreb, Croatia;
| | - Eduard Vrdoljak
- Department of Oncology, University Hospital Center Split, University of Split, 21000 Split, Croatia;
| | - Snježana Tomić
- Department of Pathology, Forensic Medicine and Cytology, University Hospital Center Split, School of Medicine, University of Split, 21000 Split, Croatia; (I.M.); (S.T.)
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Rapoport BL, Nayler S, Mlecnik B, Smit T, Heyman L, Bouquet I, Martel M, Galon J, Benn C, Anderson R. Tumor-Infiltrating Lymphocytes (TILs) in Early Breast Cancer Patients: High CD3+, CD8+, and Immunoscore Are Associated with a Pathological Complete Response. Cancers (Basel) 2022; 14:2525. [PMID: 35626126 PMCID: PMC9139282 DOI: 10.3390/cancers14102525] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary In 2021, the World Health Organization announced that breast cancer had overtaken lung cancer to become the most common cancer globally, accounting for 12% of all new cancer cases, with younger women resident in low-income countries having the lowest 5-year survival rates. The main aim of the current study was to evaluate the prognostic utility of an innovative, objective, computer-assisted, digital imaging procedure known as the Immunoscore for clinical research (ISCR) as a strategy to reveal the efficiency of the anti-tumor cellular immune landscape of the tumor microenvironment (TME) in biopsies taken from women diagnosed with early breast cancer prior to administration of neoadjuvant chemotherapy followed by surgical resection. Our results demonstrated the ability of the ISCR to enumerate tumor-infiltrating lymphocytes in the TME and, in particular, to illustrate the spatial arrangement of these cells, which, importantly, correlated with clinical outcome, measured as the pathological complete response. Abstract Background: Tumor-infiltrating lymphocytes are associated with a better prognosis in early triple-negative breast cancer (TNBC). These cells can be enumerated in situ by the “Immunoscore Clinical Research” (ISCR). The original Immunoscore® is a prognostic tool that categorizes the densities of CD3+ and CD8+ cells in both the invasive margin (IM) and center of the tumor (CT) in localized colon cancer, yielding a five-tiered classification (0–4). We evaluated the prognostic potential of ISCR and pathological complete response (pCR) following neoadjuvant chemotherapy (NACT). Methods: The cohort included 53 TNBC, 32 luminal BC, and 18 HER2-positive BC patients undergoing NACT. Pre-treatment tumor biopsies were immune-stained for CD3+ and CD8+ T-cell markers. Quantitative analysis of these cells in different tumor locations was performed using computer-assisted image analysis. Results: The pCR rate was 44%. Univariate analysis showed that primary tumor size, estrogen-receptor negative, progesterone-receptor negative, luminal vs. HER2-positive vs. TNBC, high Ki-67, high densities (cells/mm2) of CD3 CT, CD8+ CT, CD3+ IM, and CD8+ IM cells were associated with a high pCR. ISCR was associated with pCR following NACT. A multivariate model consisting of ISCR and the significant variables from the univariate analysis showed a significant trend for ISCR; however, the low sample size did not provide enough power for the model to be included in this study. Conclusions: These results revealed a significant prognostic role for the spatial distributions of the CD3+, and CD8+ lymphocytes, as well as the ISCR in relation to pCR following NACT.
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Garcia V, Elfer K, Peeters DJE, Ehinger A, Werness B, Ly A, Li X, Hanna MG, Blenman KRM, Salgado R, Gallas BD. Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer. Cancers (Basel) 2022; 14:2467. [PMID: 35626070 DOI: 10.3390/cancers14102467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary The High Throughput Truthing project aims to develop a dataset of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer specimens fit for a regulatory purpose. After completion of the pilot study, the analysis demonstrated inconsistencies and gaps in the provided training to pathologists. Select regions of interest (ROIs) were reviewed by an expert panel, who provided annotations and commentary on the challenges of the sTILs assessment. We used these annotations to develop a training document and reference standard for new training materials. These materials will train crowd-sourced pathologists to help create an algorithm validation dataset and contribute to sTILs evaluations in clinical practice. Abstract The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.
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Fassler DJ, Torre-Healy LA, Gupta R, Hamilton AM, Kobayashi S, Van Alsten SC, Zhang Y, Kurc T, Moffitt RA, Troester MA, Hoadley KA, Saltz J. Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression. Cancers (Basel) 2022; 14:2148. [PMID: 35565277 PMCID: PMC9105398 DOI: 10.3390/cancers14092148] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/09/2022] [Accepted: 04/15/2022] [Indexed: 12/15/2022] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.
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Affiliation(s)
- Danielle J. Fassler
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Luke A. Torre-Healy
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Alina M. Hamilton
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Soma Kobayashi
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Sarah C. Van Alsten
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Yuwei Zhang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Richard A. Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
| | - Melissa A. Troester
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (A.M.H.); (S.C.V.A.); (M.A.T.)
| | - Katherine A. Hoadley
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA; (D.J.F.); (L.A.T.-H.); (R.G.); (S.K.); (Y.Z.); (T.K.); (R.A.M.)
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Tzoras E, Zerdes I, Tsiknakis N, Manikis GC, Mezheyeuski A, Bergh J, Matikas A, Foukakis T. Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution. Cancers (Basel) 2022; 14:1999. [PMID: 35454904 PMCID: PMC9026731 DOI: 10.3390/cancers14081999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Abstract
The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.
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de Jong VMT, Wang Y, Ter Hoeve ND, Opdam M, Stathonikos N, Jóźwiak K, Hauptmann M, Cornelissen S, Vreuls W, Rosenberg EH, Koop EA, Varga Z, van Deurzen CHM, Mooyaart AL, Córdoba A, Groen EJ, Bart J, Willems SM, Zolota V, Wesseling J, Sapino A, Chmielik E, Ryska A, Broeks A, Voogd AC, Loi S, Michiels S, Sonke GS, van der Wall E, Siesling S, van Diest PJ, Schmidt MK, Kok M, Dackus GMHE, Salgado R, Linn SC. Prognostic Value of Stromal Tumor-Infiltrating Lymphocytes in Young, Node-Negative, Triple-Negative Breast Cancer Patients Who Did Not Receive (neo)Adjuvant Systemic Therapy. J Clin Oncol 2022; 40:2361-2374. [PMID: 35353548 PMCID: PMC9287283 DOI: 10.1200/jco.21.01536] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Triple-negative breast cancer (TNBC) is considered aggressive, and therefore, virtually all young patients with TNBC receive (neo)adjuvant chemotherapy. Increased stromal tumor-infiltrating lymphocytes (sTILs) have been associated with a favorable prognosis in TNBC. However, whether this association holds for patients who are node-negative (N0), young (< 40 years), and chemotherapy-naïve, and thus can be used for chemotherapy de-escalation strategies, is unknown. METHODS We selected all patients with N0 TNBC diagnosed between 1989 and 2000 from a Dutch population–based registry. Patients were age < 40 years at diagnosis and had not received (neo)adjuvant systemic therapy, as was standard practice at the time. Formalin-fixed paraffin-embedded blocks were retrieved (PALGA: Dutch Pathology Registry), and a pathology review including sTILs was performed. Patients were categorized according to sTILs (< 30%, 30%-75%, and ≥ 75%). Multivariable Cox regression was performed for overall survival, with or without sTILs as a covariate. Cumulative incidence of distant metastasis or death was analyzed in a competing risk model, with second primary tumors as competing risk. RESULTS sTILs were scored for 441 patients. High sTILs (≥ 75%; 21%) translated into an excellent prognosis with a 15-year cumulative incidence of a distant metastasis or death of only 2.1% (95% CI, 0 to 5.0), whereas low sTILs (< 30%; 52%) had an unfavorable prognosis with a 15-year cumulative incidence of a distant metastasis or death of 38.4% (32.1 to 44.6). In addition, every 10% increment of sTILs decreased the risk of death by 19% (adjusted hazard ratio: 0.81; 95% CI, 0.76 to 0.87), which are an independent predictor adding prognostic information to standard clinicopathologic variables (χ2 = 46.7, P < .001). CONCLUSION Chemotherapy-naïve, young patients with N0 TNBC with high sTILs (≥ 75%) have an excellent long-term prognosis. Therefore, sTILs should be considered for prospective clinical trials investigating (neo)adjuvant chemotherapy de-escalation strategies. Young cancer patients with TNBC and high sTILs have an excellent outcome, even without systemic treatment![]()
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Affiliation(s)
- Vincent M T de Jong
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Yuwei Wang
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Natalie D Ter Hoeve
- Division of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mark Opdam
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Nikolas Stathonikos
- Division of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Katarzyna Jóźwiak
- Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Michael Hauptmann
- Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Sten Cornelissen
- Core Facility Molecular Pathology and Biobanking, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Willem Vreuls
- Department of Pathology, Canisius Wilhelmina Ziekenhuis, Nijmegen, Netherlands
| | - Efraim H Rosenberg
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Esther A Koop
- Department of Pathology, Gelre Ziekenhuizen, Apeldoorn, Netherlands
| | - Zsuzsanna Varga
- Departement of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | | | - Antien L Mooyaart
- Department of Pathology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Alicia Córdoba
- Department of Pathology, Complejo Hospitalario de Navarra, Pamplona, Spain
| | - Emma J Groen
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Joost Bart
- University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, Netherlands
| | - Stefan M Willems
- University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, Netherlands
| | - Vasiliki Zolota
- Department of Pathology, Rion University Hospital, Patras, Greece
| | - Jelle Wesseling
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Department of Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Anna Sapino
- Department of Medical Sciences, University of Torino, Torino, Italy.,Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Ewa Chmielik
- Tumor Pathology Department, Maria Sklodowska-Curie Memorial National Research Institute of Oncology, Gliwice, Poland
| | - Ales Ryska
- Charles University Medical Faculty and University Hospital, Hradec Kralove, Czech Republic
| | - Annegien Broeks
- Core Facility Molecular Pathology and Biobanking, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Adri C Voogd
- Department of Epidemiology, Maastricht University, Maastricht, Netherlands.,Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Sherene Loi
- Division of Clinical Medicine and Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Stefan Michiels
- Service de Biostatistique et d'Epidémiologie, Gustave Roussy, Oncostat U1018, Inserm, Paris-Saclay University, labeled Ligue Contre le Cancer, Villejuif, France
| | - Gabe S Sonke
- Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | - Sabine Siesling
- Division of Clinical Medicine and Research, Peter MacCallum Cancer Centre, Melbourne, Australia.,Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Paul J van Diest
- Division of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marjanka K Schmidt
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Department of Clinical Genetics, Leiden University Medical Centre, Leiden, Netherlands
| | - Marleen Kok
- Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Gwen M H E Dackus
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Division of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Roberto Salgado
- Division of Clinical Medicine and Research, Peter MacCallum Cancer Centre, Melbourne, Australia.,Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Sabine C Linn
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Division of Pathology, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
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Kasherman L, Siu DHW, Woodford R, Harris CA. Angiogenesis Inhibitors and Immunomodulation in Renal Cell Cancers: The Past, Present, and Future. Cancers (Basel) 2022; 14:1406. [PMID: 35326557 DOI: 10.3390/cancers14061406] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary In their advanced stages, the mainstay of kidney cancer treatment is with medications such as targeted or immune therapies. Breakthroughs in scientific understanding of cancer drug development have led to substantial improvements in life expectancy. Although several combinations are available to choose from, it remains unclear which is best, and furthermore why cancers become resistant to treatment. This review article explores the scientific basis behind drug treatments in kidney cancers, with particular focus on blood vessel development and the immune system, and summarizes the available evidence supporting multi-drug treatments in this context. Abstract Angiogenesis inhibitors have been adopted into the standard armamentarium of therapies for advanced-stage renal cell carcinomas (RCC), but more recently, combination regimens with immune checkpoint inhibitors have demonstrated better outcomes. Despite this, the majority of affected patients still eventually experience progressive disease due to therapeutic resistance mechanisms, and there remains a need to develop novel therapeutic strategies. This article will review the synergistic mechanisms behind angiogenesis and immunomodulation in the tumor microenvironment and discuss the pre-clinical and clinical evidence for both clear-cell and non-clear-cell RCC, exploring opportunities for future growth in this exciting area of drug development.
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Abousamra S, Gupta R, Hou L, Batiste R, Zhao T, Shankar A, Rao A, Chen C, Samaras D, Kurc T, Saltz J. Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. Front Oncol 2022; 11:806603. [PMID: 35251953 PMCID: PMC8889499 DOI: 10.3389/fonc.2021.806603] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/31/2021] [Indexed: 12/12/2022] Open
Abstract
The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance. Code Available at:https://github.com/ShahiraAbousamra/til_classification.
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Affiliation(s)
- Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Le Hou
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Rebecca Batiste
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Tianhao Zhao
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Anand Shankar
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Arvind Rao
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
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Abuhadra N, Sun R, Litton JK, Rauch GM, Yam C, Chang JT, Seth S, Bassett R, Lim B, Thompson AM, Mittendorf E, Adrada BE, Damodaran S, White J, Ravenberg E, Candelaria R, Arun B, Ueno NT, Santiago L, Saleem S, Abouharb S, Murthy RK, Ibrahim N, Sahin AA, Valero V, Symmans WF, Tripathy D, Moulder S, Huo L. Prognostic Impact of High Baseline Stromal Tumor-Infiltrating Lymphocytes in the Absence of Pathologic Complete Response in Early-Stage Triple-Negative Breast Cancer. Cancers (Basel) 2022; 14:1323. [PMID: 35267631 PMCID: PMC8909018 DOI: 10.3390/cancers14051323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary High stromal tumor-infiltrating lymphocytes (sTILs) are associated with an improved pathologic complete response (pCR) and survival in triple-negative breast cancer (TNBC). We hypothesized that high baseline sTILs would have a favorable prognostic impact in TNBC patients without a pCR. In this study of 318 early-stage TNBC patients in a prospective clinical trial, event-free survival (EFS) in patients without a pCR was not significantly different between those with high sTILs and those with low sTILs (p = 0.7). Therefore, high baseline sTILs do not confer a benefit in EFS in the absence of a pCR. RNA-seq analysis predicted more CD8+ T cells in the high-sTIL group with favorable EFS compared with the high-sTIL group with unfavorable EFS, suggesting the type of lymphocytes within the TIL fraction may be an important parameter to consider for de-escalation strategies. The implications of our findings in the setting of immune checkpoint inhibitor therapy remain to be investigated. Abstract High stromal tumor-infiltrating lymphocytes (sTILs) are associated with an improved pathologic complete response (pCR) and survival in triple-negative breast cancer (TNBC). We hypothesized that high baseline sTILs would have a favorable prognostic impact in TNBC patients without a pCR after neoadjuvant chemotherapy (NACT). In this prospective NACT study, pretreatment biopsies from 318 patients with early-stage TNBC were evaluated for sTILs. Recursive partitioning analysis (RPA) was applied to search for the sTIL cutoff best associated with a pCR. With ≥20% sTILs identified as the optimal cutoff, 33% patients had high sTILs (pCR rate 64%) and 67% had low sTILs (pCR rate 29%). Patients were stratified according to the sTIL cutoff (low vs. high) and response to NACT (pCR vs. residual disease (RD)). The primary endpoint was event-free survival (EFS), with hazard ratios calculated using the Cox proportional hazards regression model and the 3-year restricted mean survival time (RMST) as primary measures. Within the high-sTIL group, EFS was better in patients with a pCR compared with those with RD (HR 0.05; 95% CI 0.01–0.39; p = 0.004). The difference in the 3-year RMST for EFS between the two groups was 5.6 months (95% CI 2.3–8.8; p = 0.001). However, among patients with RD, EFS was not significantly different between those with high sTILs and those with low sTILs (p = 0.7). RNA-seq analysis predicted more CD8+ T cells in the high-sTIL group with favorable EFS compared with the high-sTIL group with unfavorable EFS. This study did not demonstrate that high baseline sTILs confer a benefit in EFS in the absence of a pCR.
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Qureshi S, Chan N, George M, Ganesan S, Toppmeyer D, Omene C. Immune Checkpoint Inhibitors in Triple Negative Breast Cancer: The Search for the Optimal Biomarker. Biomark Insights 2022; 17:11772719221078774. [PMID: 35221668 PMCID: PMC8874164 DOI: 10.1177/11772719221078774] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/04/2022] [Indexed: 12/14/2022] Open
Abstract
Triple negative breast cancer (TNBC) is a high-risk and aggressive malignancy characterized by the absence of estrogen receptors (ER) and progesterone receptors (PR) on the surface of malignant cells, and by the lack of overexpression of human epidermal growth factor 2 (HER2). It has limited therapeutic options compared to other subtypes of breast cancer. There is now a growing body of evidence on the role of immunotherapy in TNBC, however much of the data from clinical trials is conflicting and thus, challenging for clinicians to integrate the data into clinical practice. Landmark phase III trials using immunotherapy in the early-stage neoadjuvant setting concluded that the addition of immunotherapy to chemotherapy improved the pathologic complete response (pCR) rate compared to chemotherapy with placebo while others found no significant improvement in pCR. Phase III trials have investigated the utility of immunotherapy in previously untreated metastatic TNBC, and these studies have similarly arrived at inconsistent conclusions. Some studies showed no benefit while others demonstrated a clinically significant improvement in overall survival in the PD-L1 positive population. It is not yet clear which biomarkers are most useful, and assays for these biomarkers have not been standardized. Given the often serious and severe side effects of immunotherapy, it is important and necessary to identify predictive biomarkers of response and resistance in order to enhance patient selection. In this review, we will discuss both the challenges of traditional biomarkers and the opportunities of emerging biomarkers for patient selection.
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Affiliation(s)
- Sadaf Qureshi
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nancy Chan
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Mridula George
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Shridar Ganesan
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Deborah Toppmeyer
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Coral Omene
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
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