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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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Hossain I, Fanfani V, Fischer J, Quackenbush J, Burkholz R. Biologically informed NeuralODEs for genome-wide regulatory dynamics. bioRxiv 2024:2023.02.24.529835. [PMID: 36909563 PMCID: PMC10002636 DOI: 10.1101/2023.02.24.529835] [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] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Modeling dynamics of gene regulatory networks using ordinary differential equations (ODEs) allow a deeper understanding of disease progression and response to therapy, thus aiding in intervention optimization. Although there exist methods to infer regulatory ODEs, these are generally limited to small networks, rely on dimensional reduction, or impose non-biological parametric restrictions - all impeding scalability and explainability. PHOENIX is a neural ODE framework incorporating prior domain knowledge as soft constraints to infer sparse, biologically interpretable dynamics. Extensive experiments - on simulated and real data - demonstrate PHOENIX's unique ability to learn key regulatory dynamics while scaling to the whole genome.
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Zdrenka M, Kowalewski A, Ahmadi N, Sadiqi RU, Chmura Ł, Borowczak J, Maniewski M, Szylberg Ł. Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review. Biomol Biomed 2024; 24:14-29. [PMID: 37877810 PMCID: PMC10787614 DOI: 10.17305/bb.2023.9265] [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] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 10/26/2023]
Abstract
Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.
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Affiliation(s)
- Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Adam Kowalewski
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Navid Ahmadi
- Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK
| | | | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Kraków, Poland
| | - Jędrzej Borowczak
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Mateusz Maniewski
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
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Conte M, Woodall RT, Gutova M, Chen BT, Shiroishi MS, Brown CE, Munson JM, Rockne RC. Structural and practical identifiability of contrast transport models for DCE-MRI. bioRxiv 2023:2023.12.19.572294. [PMID: 38187554 PMCID: PMC10769233 DOI: 10.1101/2023.12.19.572294] [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] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
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Mungra N, Biteghe FAN, Malindi Z, Huysamen AM, Karaan M, Hardcastle NS, Bunjun R, Chetty S, Naran K, Lang D, Richter W, Hunter R, Barth S. CSPG4 as a target for the specific killing of triple-negative breast cancer cells by a recombinant SNAP-tag-based antibody-auristatin F drug conjugate. J Cancer Res Clin Oncol 2023; 149:12203-12225. [PMID: 37432459 PMCID: PMC10465649 DOI: 10.1007/s00432-023-05031-3] [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: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 07/12/2023]
Abstract
PURPOSE Triple-negative breast cancer (TNBC) is phenotypic of breast tumors lacking expression of the estrogen receptor (ER), the progesterone receptor (PgR), and the human epidermal growth factor receptor 2 (HER2). The paucity of well-defined molecular targets in TNBC, coupled with the increasing burden of breast cancer-related mortality, emphasizes the need to develop targeted diagnostics and therapeutics. While antibody-drug conjugates (ADCs) have emerged as revolutionary tools in the selective delivery of drugs to malignant cells, their widespread clinical use has been hampered by traditional strategies which often give rise to heterogeneous mixtures of ADC products. METHODS Utilizing SNAP-tag technology as a cutting-edge site-specific conjugation method, a chondroitin sulfate proteoglycan 4 (CSPG4)-targeting ADC was engineered, encompassing a single-chain antibody fragment (scFv) conjugated to auristatin F (AURIF) via a click chemistry strategy. RESULTS After showcasing the self-labeling potential of the SNAP-tag component, surface binding and internalization of the fluorescently labeled product were demonstrated on CSPG4-positive TNBC cell lines through confocal microscopy and flow cytometry. The cell-killing ability of the novel AURIF-based recombinant ADC was illustrated by the induction of a 50% reduction in cell viability at nanomolar to micromolar concentrations on target cell lines. CONCLUSION This research underscores the applicability of SNAP-tag in the unambiguous generation of homogeneous and pharmaceutically relevant immunoconjugates that could potentially be instrumental in the management of a daunting disease like TNBC.
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Affiliation(s)
- Neelakshi Mungra
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
- Centre for Immunity and Immunotherapies, Seattle Children’s Research Institute, Washington, 98101 USA
| | - Fleury A. N. Biteghe
- Department of Radiation Oncology and Biomedical Sciences, Cedars-Sinai Medical, Los Angeles, USA
| | - Zaria Malindi
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
- Faculty of Health Sciences, Laser Research Centre, University of Johannesburg, Doornfontein, Johannesburg, 2028 South Africa
| | - Allan M. Huysamen
- Department of Chemistry, PD Hahn Building, University of Cape Town, Cape Town, 7700 South Africa
| | - Maryam Karaan
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
| | - Natasha S. Hardcastle
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
| | - Rubina Bunjun
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, 7700 South Africa
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7700 South Africa
| | - Shivan Chetty
- Faculty of Health Sciences, School of Clinical Medicine, University of Witwatersrand, Braamfontein, Johannesburg, 2000 South Africa
| | - Krupa Naran
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
| | - Dirk Lang
- Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, 7700 South Africa
| | | | - Roger Hunter
- Department of Chemistry, PD Hahn Building, University of Cape Town, Cape Town, 7700 South Africa
| | - Stefan Barth
- Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology and Immunotherapy Research Unit, University of Cape Town, Cape Town, 7700 South Africa
- Faculty of Health Sciences, Department of Integrative Biomedical Sciences, South African Research Chair in Cancer Biotechnology, University of Cape Town, Cape Town, 7700 South Africa
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6
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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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7
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Harms PW, Frankel TL, Moutafi M, Rao A, Rimm DL, Taube JM, Thomas D, Chan MP, Pantanowitz L. Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists. Mod Pathol 2023; 36:100197. [PMID: 37105494 DOI: 10.1016/j.modpat.2023.100197] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 09/20/2022] [Revised: 03/07/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023]
Abstract
Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.
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Affiliation(s)
- Paul W Harms
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan.
| | - Timothy L Frankel
- Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Surgery, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Myrto Moutafi
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Janis M Taube
- Department of Oncology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Dermatology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland
| | - Dafydd Thomas
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - May P Chan
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Liron Pantanowitz
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
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8
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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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9
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Wang G, Yao Y, Huang H, Zhou J, Ni C. Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy. Front Oncol 2023; 13:1131259. [PMID: 37284197 PMCID: PMC10239824 DOI: 10.3389/fonc.2023.1131259] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/08/2023] [Indexed: 06/08/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.
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Affiliation(s)
- Gang Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yao Yao
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Huanhuan Huang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Jun Zhou
- Department of Breast Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chao Ni
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
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10
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Gu J, Jian H, Wei C, Shiu J, Ganesan A, Zhao W, Hedde PN. A Low-Cost Modular Imaging System for Rapid, Multiplexed Immunofluorescence Detection in Clinical Tissues. Int J Mol Sci 2023; 24:7008. [PMID: 37108170 PMCID: PMC10138925 DOI: 10.3390/ijms24087008] [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: 02/25/2023] [Revised: 04/03/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
To image 4-plex immunofluorescence-stained tissue samples at a low cost with cellular level resolution and sensitivity and dynamic range required to detect lowly and highly abundant targets, here we describe a robust, inexpensive (<$9000), 3D printable portable imaging device (Tissue Imager). The Tissue Imager can immediately be deployed on benchtops for in situ protein detection in tissue samples. Applications for this device are broad, ranging from answering basic biological questions to clinical pathology, where immunofluorescence can detect a larger number of markers than the standard H&E or chromogenic immunohistochemistry (CIH) staining, while the low cost also allows usage in classrooms. After characterizing our platform's specificity and sensitivity, we demonstrate imaging of a 4-plex immunology panel in human cutaneous T-cell lymphoma (CTCL) formalin-fixed paraffin-embedded (FFPE) tissue samples. From those images, positive cells were detected using CellProfiler, a popular open-source software package, for tumor marker profiling. We achieved a performance on par with commercial epifluorescence microscopes that are >10 times more expensive than our Tissue Imager. This device enables rapid immunofluorescence detection in tissue sections at a low cost for scientists and clinicians and can provide students with a hands-on experience to understand engineering and instrumentation. We note that for using the Tissue Imager as a medical device in clinical settings, a comprehensive review and approval processes would be required.
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Affiliation(s)
- Joshua Gu
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA 92697, USA
| | - Hannah Jian
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
| | - Christine Wei
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
| | - Jessica Shiu
- Department of Dermatology, University of California, Irvine, CA 92697, USA
| | - Anand Ganesan
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
- Department of Dermatology, University of California, Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, CA 92697, USA
| | - Weian Zhao
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, CA 92697, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, CA 92697, USA
- Edwards Life Sciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- Institute for Immunology, University of California, Irvine, CA 92697, USA
| | - Per Niklas Hedde
- Beckman Laser Institute and Medical Clinic, University of California, Irvine, CA 92697, USA
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11
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Wrobel J, Harris C, Vandekar S. Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data. Methods Mol Biol 2023; 2629:141-168. [PMID: 36929077 DOI: 10.1007/978-1-0716-2986-4_8] [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] [Indexed: 03/18/2023]
Abstract
Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.
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Affiliation(s)
- Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Coleman Harris
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
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12
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Wang LC, Wang YL, He B, Zheng YJ, Yu HC, Liu ZY, Fan RR, Zan X, Liang RC, Wu ZP, Tang X, Wang GQ, Xu JG, Zhou LX. Expression and clinical significance of VISTA, B7-H3, and PD-L1 in glioma. Clin Immunol 2022; 245:109178. [DOI: 10.1016/j.clim.2022.109178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/15/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022]
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13
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Guo A, Zhang J, Tian Y, Peng Y, Luo P, Zhang J, Liu Z, Wu W, Zhang H, Cheng Q. Identify the immune characteristics and immunotherapy value of CD93 in the pan-cancer based on the public data sets. Front Immunol 2022; 13:907182. [PMID: 36389798 PMCID: PMC9646793 DOI: 10.3389/fimmu.2022.907182] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
CD93 is a transmembrane receptor that is mainly expressed on endothelial cells. A recent study found that upregulated CD93 in tumor vessels is essential for tumor angiogenesis in several cancers. However, the underlying mechanisms are largely unexplored. Our present research systematically analyzed the characteristics of CD93 in tumor immunotherapy among 33 cancers. CD93 levels and co-expression of CD93 on cancer and stromal cells were detected using public databases and multiple immunofluorescence staining. The Kaplan-Meier (KM) analysis identified the predictive role of CD93 in these cancer types. The survival differences between CD93 mutants and WT, CNV groups, and methylation were also investigated. The immune landscape of CD93 in the tumor microenvironment was analyzed using the SangerBox, TIMER 2.0, and single-cell sequencing. The immunotherapy value of CD93 was predicted through public databases. CD93 mRNA and protein levels differed significantly between cancer samples and adjacent control tissues in multiply cancer types. CD93 mRNA expression associated with patient prognosis in many cancers. The correlation of CD93 levels with mutational status of other gene in these cancers was also analyzed. CD93 levels significantly positively related to three scores (immune, stromal, and extimate), immune infiltrates, immune checkpoints, and neoantigen expression.. Additionally, single-cell sequencing revealed that CD93 is predominantly co-expressed on tumor and stromal cells, such as endothelial cells, cancer-associated fibroblasts (CAFs), neutrophils, T cells, macrophages, M1 and M2 macrophages. Several immune-related signaling pathways were enriched based on CD93 expression, including immune cells activation and migration, focal adhesion, leukocyte transendothelial migration, oxidative phosphorylation, and complement. Multiple immunofluorescence staining displayed the relationship between CD93 expression and CD8, CD68, and CD163 in these cancers. Finally, the treatment response of CD93 in many immunotherapy cohorts and sensitive small molecules was predicted from the public datasets. CD93 expression is closely associated with clinical prognosis and immune infiltrates in a variety of tumors. Targeting CD93-related signaling pathways in the tumor microenvironment may be a novel therapeutic strategy for tumor immunotherapy.
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Affiliation(s)
- Aiyuan Guo
- Department of Dermatology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jingwei Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yuqiu Tian
- Department of Infectious Disease, Zhuzhou Central Hospital, Zhuzhou, China
| | - Yun Peng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
- *Correspondence: Quan Cheng, ; Hao Zhang,
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Diagnosis and Therapy Center for Glioma of Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Quan Cheng, ; Hao Zhang,
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14
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Lopez DC, Robbins YL, Kowalczyk JT, Lassoued W, Gulley JL, Miettinen MM, Gallia GL, Allen CT, Hodge JW, London NR. Multi-spectral immunofluorescence evaluation of the myeloid, T cell, and natural killer cell tumor immune microenvironment in chordoma may guide immunotherapeutic strategies. Front Oncol 2022; 12:1012058. [PMID: 36338744 PMCID: PMC9634172 DOI: 10.3389/fonc.2022.1012058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/27/2022] [Indexed: 11/21/2022] Open
Abstract
Background Chordoma is a rare, invasive, and devastating bone malignancy of residual notochord tissue that arises at the skull base, sacrum, or spine. In order to maximize immunotherapeutic approaches as a potential treatment strategy in chordoma it is important to fully characterize the tumor immune microenvironment (TIME). Multispectral immunofluorescence (MIF) allows for comprehensive evaluation of tumor compartments, molecular co-expression, and immune cell spatial relationships. Here we implement MIF to define the myeloid, T cell, and natural killer (NK) cell compartments in an effort to guide rational design of immunotherapeutic strategies for chordoma. Methods Chordoma tumor tissue from 57 patients was evaluated using MIF. Three panels were validated to assess myeloid cell, T cell, and NK cell populations. Slides were stained using an automated system and HALO software objective analysis was utilized for quantitative immune cell density and spatial comparisons between tumor and stroma compartments. Results Chordoma TIME analysis revealed macrophage infiltration of the tumor parenchyma at a significantly higher density than stroma. In contrast, helper T cells, cytotoxic T cells, and T regulatory cells were significantly more abundant in stroma versus tumor. T cell compartment infiltration more commonly demonstrated a tumor parenchymal exclusion pattern, most markedly among cytotoxic T cells. NK cells were sparsely found within the chordoma TIME and few were in an activated state. No immune composition differences were seen in chordomas originating from diverse anatomic sites or between those resected at primary versus advanced disease stage. Conclusion This is the first comprehensive evaluation of the chordoma TIME including myeloid, T cell, and NK cell appraisal using MIF. Our findings demonstrate that myeloid cells significantly infiltrate chordoma tumor parenchyma while T cells tend to be tumor parenchymal excluded with high stromal infiltration. On average, myeloid cells are found nearer to target tumor cells than T cells, potentially resulting in restriction of T effector cell function. This study suggests that future immunotherapy combinations for chordoma should be aimed at decreasing myeloid cell suppressive function while enhancing cytotoxic T cell and NK cell killing.
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Affiliation(s)
- Diana C. Lopez
- Sinonasal and Skull Base Tumor Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
- Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Cleveland, OH, United States
| | - Yvette L. Robbins
- Section on Translational Tumor Immunology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - Joshua T. Kowalczyk
- Center for Immuno-Oncology, National Cancer Institute, Center for Cancer Research, National Institutes of Health (CCR, NIH), Bethesda, MD, United States
| | - Wiem Lassoued
- Center for Immuno-Oncology, National Cancer Institute, Center for Cancer Research, National Institutes of Health (CCR, NIH), Bethesda, MD, United States
| | - James L. Gulley
- Center for Immuno-Oncology, National Cancer Institute, Center for Cancer Research, National Institutes of Health (CCR, NIH), Bethesda, MD, United States
| | - Markku M. Miettinen
- Laboratory for Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, United States
| | - Gary L. Gallia
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Clint T. Allen
- Section on Translational Tumor Immunology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
| | - James W. Hodge
- Center for Immuno-Oncology, National Cancer Institute, Center for Cancer Research, National Institutes of Health (CCR, NIH), Bethesda, MD, United States
| | - Nyall R. London
- Sinonasal and Skull Base Tumor Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Nyall R. London Jr., ;
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15
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Massa D, Tosi A, Rosato A, Guarneri V, Dieci MV. Multiplexed In Situ Spatial Protein Profiling in the Pursuit of Precision Immuno-Oncology for Patients with Breast Cancer. Cancers (Basel) 2022; 14:4885. [PMID: 36230808 PMCID: PMC9562913 DOI: 10.3390/cancers14194885] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 09/29/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many solid tumors. In breast cancer (BC), immunotherapy is currently approved in combination with chemotherapy, albeit only in triple-negative breast cancer. Unfortunately, most patients only derive limited benefit from ICIs, progressing either upfront or after an initial response. Therapeutics must engage with a heterogeneous network of complex stromal-cancer interactions that can fail at imposing cancer immune control in multiple domains, such as in the genomic, epigenomic, transcriptomic, proteomic, and metabolomic domains. To overcome these types of heterogeneous resistance phenotypes, several combinatorial strategies are underway. Still, they can be predicted to be effective only in the subgroups of patients in which those specific resistance mechanisms are effectively in place. As single biomarker predictive performances are necessarily suboptimal at capturing the complexity of this articulate network, precision immune-oncology calls for multi-omics tumor microenvironment profiling in order to identify unique predictive patterns and to proactively tailor combinatorial treatments. Multiplexed single-cell spatially resolved tissue analysis, through precise epitope colocalization, allows one to infer cellular functional states in view of their spatial organization. In this review, we discuss-through the lens of the cancer-immunity cycle-selected, established, and emerging markers that may be evaluated in multiplexed spatial protein panels to help identify prognostic and predictive patterns in BC.
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Affiliation(s)
- Davide Massa
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padova, Italy
- Division of Oncology 2, Istituto Oncologico Veneto IRCCS, 35128 Padova, Italy
| | - Anna Tosi
- Immunology and Molecular Oncology Diagnostics, Istituto Oncologico Veneto IRCCS, 35128 Padova, Italy
| | - Antonio Rosato
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padova, Italy
- Immunology and Molecular Oncology Diagnostics, Istituto Oncologico Veneto IRCCS, 35128 Padova, Italy
| | - Valentina Guarneri
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padova, Italy
- Division of Oncology 2, Istituto Oncologico Veneto IRCCS, 35128 Padova, Italy
| | - Maria Vittoria Dieci
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35128 Padova, Italy
- Division of Oncology 2, Istituto Oncologico Veneto IRCCS, 35128 Padova, Italy
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16
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Sun Y, Dong Y, Liu X, Zhang Y, Bai H, Duan J, Tian Z, Yan X, Wang J, Wang Z. Blockade of STAT3/IL-4 overcomes EGFR T790M-cis-L792F-induced resistance to osimertinib via suppressing M2 macrophages polarization. EBioMedicine 2022; 83:104200. [PMID: 35932642 DOI: 10.1016/j.ebiom.2022.104200] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 04/17/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The mechanism of missense alteration at EGFR L792F in patients with non-small cell lung cancer resistant to osimertinib has not been sufficiently clarified. We aimed to explore the critical molecular events and coping strategies in osimertinib resistance due to acquired L792F mutation. METHODS Circulating tumor DNA-based sequencing data of 1153 patients with osimertinib resistance were collected to illustrate the prevalence of EGFR L792F mutation. Sensitivity to osimertinib was tested with constructed EGFR 19Del/T790M-cis-L792F cell lines in vitro and in vivo. The correlation and linked pathways between M2 macrophage polarization and EGFR L792Fcis-induced osimertinib resistance were investigated. Possible interventions to suppress osimertinib resistance by targeting IL-4 or STAT3 were explored. FINDINGS The concomitant EGFR L792F was identified as an independent mutation following the acquisition of T790M after osimertinib resistance, in that 5 of the 946 patients with osimertinib resistance harbored EGFR T790M-cis-L792F mutation. Transfected EGFR 19Del/T790M-cis-L792F in cell lines had decreased sensitivity to osimertinib and enhanced infiltrating macrophage with M2 polarization. Silico analyses confirmed the role of M2 polarization in osimertinib resistance induced by EGFR T790M-cis-L792F mutation. EGFR T790M-cis-L792F mutation upregulated phosphorylation of STAT3 Tyr705 and promoted its specific binding to IL4 promoter, enhancing IL-4 expression and secretion and inducing macrophage M2 polarization. Furthermore, blockade of STAT3/IL-4 (SH-4-54 or dupilumab) suppressed macrophage M2 polarization and regressed tumor sensitivity to osimertinib. INTERPRETATION Our results proved that targeting EGFR T790M-cis-L792F/STAT3 Tyr705/IL-4 pathway could be a potential strategy to suppress osimertinib resistance in NSCLC. FUNDING This work was supported by the National Natural Science Foundation of China (81871889, 82072586, 81902910), Beijing Natural Science Foundation (7212084, 7214249), the China National Natural Science Foundation Key Program (81630071), the National Key Research and Development Project (2019YFC1315704), CAMS Innovation Fund for Medical Sciences (CIFMS 2021-1-I2M-012), Aiyou Foundation (KY201701) and CAMS Key Laboratory of translational research on lung cancer (2018PT31035).
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Tashireva LA, Popova NO, Kalinchuk AY, Goldberg VE, Kovalenko EI, Artamonova EV, Manikhas AG, Ponomarenko DM, Levchenko NV, Rossokha EI, Krasilnikova SY, Zafirova MA, Choynzonov EL, Perelmuter VM. B Lymphocytes Are a Predictive Marker of Eribulin Response and Overall Survival in Locally Advanced or Metastatic Breast Cancer: A Multicenter, Two-Cohort, Non-Randomized, Open-Label, Retrospective Study. Front Oncol 2022; 12:909505. [PMID: 35814376 PMCID: PMC9260581 DOI: 10.3389/fonc.2022.909505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Triple-negative breast cancer has no specific treatment and unfavorable prognosis. Eribulin is one of the drugs widely used in this cohort of patients. In addition to its antimitotic effect, eribulin has an immunomodulant effect on the tumor microenvironment. In this study, we discover immunological markers, such as tumor-infiltrating lymphocytes, CD8+, CD4+, FoxP3+, CD20+ lymphocytes, and their PD1 positivity or negativity, with the ability to predict benefits from eribulin within locally advanced or metastatic triple-negative breast cancer. The primary objective was to explore the association of composition of immune cells in the microenvironment with response to eribulin. The key secondary objective was overall survival. Seven-color multiplex immunofluorescence was used to phenotype lymphocytes in the primary tumor. It has been shown that the PD1-negative-to-PD1-positive B cells ratio in primary tumors more than 3 is an independent predictor of the short-term effectiveness of eribulin [OR (95%CI) 14.09 (1.29-153.35), p=0.0029] and worse overall survival [HR (95%CI) 11.25 (1.37-70.25), p=0.0009] in patients with locally advanced or metastatic triple-negative breast cancer.
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Affiliation(s)
- Liubov A. Tashireva
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
- *Correspondence: Liubov A. Tashireva,
| | - Nataliya O. Popova
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Anna Yu. Kalinchuk
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Viktor E. Goldberg
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Elena I. Kovalenko
- Federal State Budgetary Institution National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation (NN Blokhin NMRCO), Moscow, Russia
| | - Elena V. Artamonova
- Federal State Budgetary Institution National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation (NN Blokhin NMRCO), Moscow, Russia
| | - Aleksey G. Manikhas
- Saint-Petersburg State Budgetary Healthcare Institution “City Clinical Oncology Center” (1st Oncology (Surgery) Department, Saint-Petersburg, Russia
| | - Dmitriy M. Ponomarenko
- Chemotherapy Department, Irkutsk Regional Cancer Center, Irkutsk, Russia
- Department of Oncology, Irkutsk State Medical Academy of Postgraduate Education, Irkutsk, Russia
| | - Nataliya V. Levchenko
- Chemotherapy Department, State Budgetary Healthcare Institution “St. Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological)”, St. Petersburg, Russia
| | - Elena I. Rossokha
- Chemotherapy Department, Regional State Budgetary Healthcare Institution “Altai Regional Oncology Center”, Barnaul, Russia
| | | | - Marina A. Zafirova
- Federal State Budget Educational Institution of Higher Education, Ural State Medical University of the Ministry of Health of the Russian Federation, Ekaterinburg, Russia
| | - Evgeniy L. Choynzonov
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Vladimir M. Perelmuter
- Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
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18
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Abstract
The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.
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Affiliation(s)
- Sunil S Badve
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
| | - Yesim Gökmen-Polar
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
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19
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Sun X, Zhai J, Sun B, Parra ER, Jiang M, Ma W, Wang J, Kang AM, Kannan K, Pandurengan R, Zhang S, Solis LM, Haymaker CL, Raso MG, Mendoza Perez J, Sahin AA, Wistuba II, Yam C, Litton JK, Yang F. Effector memory cytotoxic CD3(+)/CD8(+)/CD45RO(+) T cells are predictive of good survival and a lower risk of recurrence in triple-negative breast cancer. Mod Pathol 2022; 35:601-8. [PMID: 34839351 DOI: 10.1038/s41379-021-00973-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 12/24/2022]
Abstract
Triple-negative breast cancer (TNBC) with high tumour-infiltrating lymphocytes (TILs) has been associated with a promising prognosis. To better understand the prognostic value of immune cell subtypes in TNBC, we characterised TILs and the interaction between tumour cells and immune cell subtypes. A total of 145 breast cancer tissues were stained by multiplex immunofluorescence (mIF), including panel 1 (PD-L1, PD-1, CD3, CD8, CD68 and CK) and panel 2 (Foxp3, Granzyme B, CD45RO, CD3, CD8 and CK). Phenotypes were analysed and quantified by pathologists using InForm software. We found that in the ER-negative (ER <1% and HER2-negative) group and the ER/PR-low positive (ER 1-9% and HER2-negative) group, 11.2% and 7.1% of patients were PD-L1+ by the tumour cell score, 29.0% and 28.6% were PD-L1+ by the modified immune cell score and 30.8% and 32.1% were PD-L1+ by the combined positive score. We combined ER-negative and ER/PR-low positive cases for the survival analysis since a 10% cut-off is often used in clinical practice for therapeutic purposes. The densities of PD-L1+ tumour cells (HR: 0.366, 95% CI: 0.138-0.970; p = 0.043) within the tumour compartment and CD3+ immune cells in the total area (tumour and stromal compartments combined) (HR: 0.213, 95% CI: 0.070-0.642; p = 0.006) were favourable prognostic biomarkers for overall survival (OS) in TNBC. The density of effector/memory cytotoxic T cells (CD3+CD8+CD45RO+) in the tumour compartment was an independent prognostic biomarker for OS (HR: 0.232, 95% CI: 0.086-0.628; p = 0.004) and DFS (HR: 0.183, 95% CI: 0.1301-0.744; p = 0.009) in TNBC. Interestingly, spatial data suggested that patients with a higher density of PD-L1+ tumour cells had shorter cell-cell distances from tumour cells to cytotoxic T cells (p < 0.01). In conclusion, we found that phenotyping tumour immune cells by mIF is highly informative in understanding the immune microenvironment in TNBC. PD-L1+ tumour cells, total T cells and effector/memory cytotoxic T cells are promising prognostic biomarkers in TNBC.
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20
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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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21
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Vanguri RS, Fenn KM, Kearney MR, Wang Q, Guo H, Marks DK, Chin C, Alcus CF, Thompson JB, Leu C, Hibshoosh H, Kalinsky KM, Mathews JC, Nadeem S, Hollmann TJ, Connolly EP. Tumor immune microenvironment and response to neoadjuvant chemotherapy in hormone receptor/HER2+ early stage breast cancer. Clin Breast Cancer 2022. [PMID: 35610143 PMCID: PMC10266131 DOI: 10.1016/j.clbc.2022.04.002] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Pathologic response at the time of surgery after neoadjuvant therapy for HER2 positive early breast cancer impacts both prognosis and subsequent adjuvant therapy. Comprehensive descriptions of the tumor microenvironment (TME) in patients with HER2 positive early breast cancer is not well described. We utilized standard stromal pathologist-assessed tumor infiltrating lymphocyte (TIL) quantification, quantitative multiplex immunofluorescence, and RNA-based gene pathway signatures to assess pretreatment TME characteristics associated pathologic complete response in patients with hormone receptor positive, HER2 positive early breast cancer treated in the neoadjuvant setting. METHODS We utilized standard stromal pathologist-assessed TIL quantification, quantitative multiplex immunofluorescence, and RNA-based gene pathway signatures to assess pretreatment TME characteristics associated pathologic complete response in 28 patients with hormone receptor positive, HER2 positive early breast cancer treated in the neoadjuvant setting. RESULTS Pathologist-assessed stromal TILs were significantly associated with pathologic complete response (pCR). By quantitative multiplex immunofluorescence, univariate analysis revealed significant increases in CD3+, CD3+CD8-FOXP3-, CD8+ and FOXP3+ T-cell densities as well as increased immune cell aggregates in pCR patients. In subsets of paired pre/post-treatment samples, we observed significant changes in gene expression signatures in non-pCR patients and significant decreases in CD8+ densities after treatment in pCR patients. No RNA based pathway signature was associated with pCR. CONCLUSION TME characterization HER2 positive breast cancer patients revealed several stromal T-cell densities and immune cell aggregates associated with pCR. These results demonstrate the feasibility of these novel methods in TME evaluation and contribute to ongoing investigations of the TME in HER2+ early breast cancer to identify robust biomarkers to best identify patients eligible for systemic de-escalation strategies.
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22
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Ouyang W, Bowman RW, Wang H, Bumke KE, Collins JT, Spjuth O, Carreras-Puigvert J, Diederich B. An Open-Source Modular Framework for Automated Pipetting and Imaging Applications. Adv Biol (Weinh) 2022; 6:e2101063. [PMID: 34693668 DOI: 10.1002/adbi.202101063] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 06/23/2021] [Revised: 09/14/2021] [Indexed: 01/27/2023]
Abstract
The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.
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Affiliation(s)
- Wei Ouyang
- W. Ouyang, Science for Life Laboratory School of Engineering Sciences in Chemistry, Biotechnology and Health KTH - Royal Institute of Technology, Stockholm, 114 28, Sweden
| | - Richard W Bowman
- R. W. Bowman, K. E. Bumke, J. T. Collins, Department of Physics, University of Bath, Bath, BA2 7AY, UK
| | - Haoran Wang
- H. Wang, B. Diederich, Leibniz Institute for Photonic Technology, Albert-Einstein-Str. 9, 07749, Jena, Germany.,H. Wang, B. Diederich, Institute of Physical Chemistry, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Kaspar E Bumke
- R. W. Bowman, K. E. Bumke, J. T. Collins, Department of Physics, University of Bath, Bath, BA2 7AY, UK
| | - Joel T Collins
- R. W. Bowman, K. E. Bumke, J. T. Collins, Department of Physics, University of Bath, Bath, BA2 7AY, UK
| | - Ola Spjuth
- O. Spjuth, J. Carreras-Puigvert, Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, Uppsala, SE-75124, Sweden
| | - Jordi Carreras-Puigvert
- O. Spjuth, J. Carreras-Puigvert, Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, Uppsala, SE-75124, Sweden
| | - Benedict Diederich
- H. Wang, B. Diederich, Leibniz Institute for Photonic Technology, Albert-Einstein-Str. 9, 07749, Jena, Germany.,H. Wang, B. Diederich, Institute of Physical Chemistry, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
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23
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Giugliano F, Antonarelli G, Tarantino P, Cortes J, Rugo HS, Curigliano G. Harmonizing PD-L1 testing in metastatic triple negative breast cancer. Expert Opin Biol Ther 2021; 22:345-348. [PMID: 34930070 DOI: 10.1080/14712598.2022.2021180] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Federica Giugliano
- European Institute of Oncology, IRCCS, Milan, Italy.,Department of Oncology and Hematology (DIPO), University of Milan, Milan, Italy
| | - Gabriele Antonarelli
- European Institute of Oncology, IRCCS, Milan, Italy.,Department of Oncology and Hematology (DIPO), University of Milan, Milan, Italy
| | - Paolo Tarantino
- European Institute of Oncology, IRCCS, Milan, Italy.,Department of Oncology and Hematology (DIPO), University of Milan, Milan, Italy
| | - Javier Cortes
- International Breast Cancer Center (IBCC), Quironsalud Group, Madrid & Barcelona, Vall d´Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Hope S Rugo
- University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA
| | - Giuseppe Curigliano
- European Institute of Oncology, IRCCS, Milan, Italy.,Department of Oncology and Hematology (DIPO), University of Milan, Milan, Italy
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24
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Wang J, Browne L, Slapetova I, Shang F, Lee K, Lynch J, Beretov J, Whan R, Graham PH, Millar EKA. Multiplexed immunofluorescence identifies high stromal CD68(+)PD-L1(+) macrophages as a predictor of improved survival in triple negative breast cancer. Sci Rep 2021; 11:21608. [PMID: 34732817 DOI: 10.1038/s41598-021-01116-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 08/09/2021] [Accepted: 10/15/2021] [Indexed: 12/14/2022] Open
Abstract
Triple negative breast cancer (TNBC) comprises 10-15% of all breast cancers and has a poor prognosis with a high risk of recurrence within 5 years. PD-L1 is an important biomarker for patient selection for immunotherapy but its cellular expression and co-localization within the tumour immune microenvironment and associated prognostic value is not well defined. We aimed to characterise the phenotypes of immune cells expressing PD-L1 and determine their association with overall survival (OS) and breast cancer-specific survival (BCSS). Using tissue microarrays from a retrospective cohort of TNBC patients from St George Hospital, Sydney (n = 244), multiplexed immunofluorescence (mIF) was used to assess staining for CD3, CD8, CD20, CD68, PD-1, PD-L1, FOXP3 and pan-cytokeratin on the Vectra Polaris™ platform and analysed using QuPath. Cox multivariate analyses showed high CD68+PD-L1+ stromal cell counts were associated with improved prognosis for OS (HR 0.56, 95% CI 0.33-0.95, p = 0.030) and BCSS (HR 0.47, 95% CI 0.25-0.88, p = 0.018) in the whole cohort and in patients receiving chemotherapy, improving incrementally upon the predictive value of PD-L1+ alone for BCSS. These data suggest that CD68+PD-L1+ status can provide clinically useful prognostic information to identify sub-groups of patients with good or poor prognosis and guide treatment decisions in TNBC.
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25
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Zerdes I, Karafousia V, Mezheyeuski A, Stogiannitsi M, Kuiper R, Moreno Ruiz P, Rassidakis G, Bergh J, Hatschek T, Foukakis T, Matikas A. Discordance of PD-L1 Expression at the Protein and RNA Levels in Early Breast Cancer. Cancers (Basel) 2021; 13:4655. [PMID: 34572882 DOI: 10.3390/cancers13184655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 07/18/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 01/12/2023] Open
Abstract
Simple Summary Despite the increasing use of checkpoint inhibitors for early and metastatic breast cancer, Programmed Death Ligand 1 (PD-L1) remains the only validated albeit imperfect predictive biomarker. Significant discordance in PD-L1 protein expression depending on the antibody used has been demonstrated, while the weak correlation and discordant prognostic information between protein and gene expression underscore its biologic heterogeneity. In this study, we use material from two patient cohorts of early breast cancer and multiple methodologies (immunohistochemistry, RNA fluorescent in situ hybridization, immunofluorescence, bulk gene expression, and multiplex fluorescent immunohistochemistry) to demonstrate the significant discordance in PD-L1 expression among various methods and between different areas of the same tumor, which hints toward the presence of spatial, intratumoral and biological heterogeneity. Abstract We aimed to assess if the discrepant prognostic information between Programmed Death Ligand 1 (PD-L1) protein versus mRNA expression in early breast cancer (BC) could be attributed to heterogeneity in its expression. PD-L1 protein and mRNA expression in BC tissue microarrays from two clinical patient cohorts were evaluated (105 patients; cohort 1: untreated; cohort 2: neoadjuvant chemotherapy-treated). Immunohistochemistry (IHC) with SP142, SP263 was performed. PD-L1 mRNA was evaluated using bulk gene expression and RNA-FISH RNAscope®, the latter scored in a semi-quantitative manner and combined with immunofluorescence (IF) staining for the simultaneous detection of PD-L1 protein expression. PD-L1 expression was assessed in cores as a whole and in two regions of interest (ROI) from the same core. The cell origin of PD-L1 expression was evaluated using multiplex fluorescent IHC. IHC PD-L1 expression between SP142 and SP263 was concordant in 86.7% of cores (p < 0.001). PD-L1 IF/IHC was weakly correlated with spatial mRNA expression (concordance 54.6–71.2%). PD-L1 was mostly expressed by lymphocytes intra-tumorally, while its stromal expression was mostly observed in macrophages. Our results demonstrate only moderate concordance between the various methods of assessing PD-L1 expression at the protein and mRNA levels, which may be attributed to both analytical performance and spatial heterogeneity.
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26
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Lazcano R, Rojas F, Laberiano C, Hernandez S, Parra ER. Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies. Front Mol Biosci 2021; 8:661222. [PMID: 34395517 PMCID: PMC8363080 DOI: 10.3389/fmolb.2021.661222] [Citation(s) in RCA: 6] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 07/19/2021] [Indexed: 11/28/2022] Open
Abstract
Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.
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Affiliation(s)
- Rossana Lazcano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Frank Rojas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Caddie Laberiano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sharia Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Edwin Roger Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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27
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Wilson CM, Ospina OE, Townsend MK, Nguyen J, Moran Segura C, Schildkraut JM, Tworoger SS, Peres LC, Fridley BL. Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data. Cancers (Basel) 2021; 13:3031. [PMID: 34204319 DOI: 10.3390/cancers13123031] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 05/14/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Immune modulation is considered a hallmark of cancer initiation and progression, and has offered promising opportunities for therapeutic manipulation. Multiplex immunofluorescence (mIF) technology has enabled the tumor immune microenvironment (TIME) to be studied at an increased scale, in terms of both the number of markers and the number of samples. Another benefit of mIF technology is the ability to measure not only the abundance but also the spatial location of multiple cells types within a tissue sample simultaneously, allowing for assessment of the co-localization of different types of immune markers. Thus, the use of mIF technologies have enable researchers to characterize patient, clinical, and tumor characteristics in the hope of identifying patients whom might benefit from immunotherapy treatments. In this review we outline some of the challenges and opportunities in the statistical analyses of mIF data to study the TIME. Abstract Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or “zero-inflated” data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.
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28
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Parra ER. Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment. Front Mol Biosci 2021; 8:668340. [PMID: 34179080 PMCID: PMC8226163 DOI: 10.3389/fmolb.2021.668340] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 06/02/2021] [Indexed: 12/13/2022] Open
Abstract
Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.
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Affiliation(s)
- Edwin Roger Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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29
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Solorzano L, Wik L, Olsson Bontell T, Wang Y, Klemm AH, Öfverstedt J, Jakola AS, Östman A, Wählby C. Machine learning for cell classification and neighborhood analysis in glioma tissue. Cytometry A 2021; 99:1176-1186. [PMID: 34089228 DOI: 10.1002/cyto.a.24467] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 02/25/2021] [Revised: 04/28/2021] [Accepted: 05/25/2021] [Indexed: 12/21/2022]
Abstract
Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.
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Affiliation(s)
- Leslie Solorzano
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Lina Wik
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Thomas Olsson Bontell
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Yuyu Wang
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Anna H Klemm
- Department of Information Technology, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, Sweden
| | - Johan Öfverstedt
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Asgeir S Jakola
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Arne Östman
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Carolina Wählby
- Department of Information Technology, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, Sweden
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30
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Yeong J, Suteja L, Simoni Y, Lau KW, Tan AC, Li HH, Lim S, Loh JH, Wee FYT, Nerurkar SN, Takano A, Tan EH, Lim TKH, Newell EW, Tan DSW. Intratumoral CD39 +CD8 + T Cells Predict Response to Programmed Cell Death Protein-1 or Programmed Death Ligand-1 Blockade in Patients With NSCLC. J Thorac Oncol 2021; 16:1349-1358. [PMID: 33975004 DOI: 10.1016/j.jtho.2021.04.016] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [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: 10/06/2020] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Programmed cell death protein-1 (PD-1) and programmed death-ligand 1 (PD-L1) blockade is currently widely used in the treatment of metastatic NSCLC. Despite available biomarker stratification, clinical responses vary. Thus, the search for novel biomarkers with improved response prediction is ongoing. Previously, using mass cytometry or cytometry by time-of-flight (CyTOF), our group demonstrated that CD39+CD8+ immune cells represent tumor antigen-specific, cytotoxic T cells in treatment-naive NSCLC. We hypothesized that accurate quantitation of this T cell subset would predict immunotherapy outcome. METHODS To translate this to a clinical setting, the present study compared CyTOF data with a range of clinically relevant methods, including conventional immunohistochemistry (IHC), multiplex IHC or immunofluorescence (mIHC), and gene expression assay by NanoString. RESULTS Quantification using mIHC but not conventional IHC or NanoString correlated with the CyTOF results. The specificity and sensitivity of mIHC were then evaluated in a separate retrospective NSCLC cohort. CD39+CD8+ T cell proportion, as determined by mIHC, successfully stratified responders and nonresponders to PD-1 or PD-L1 inhibitors (objective response rate of 63.6%, compared with 0% for the negative group). This predictive capability was independent from other confounding factors, such as total CD8+ T cell proportion, CD39+ lymphocyte proportion, PD-L1 positivity, EGFR mutation status, and other clinicopathologic parameters. CONCLUSIONS Our results suggest that the mIHC platform is a clinically relevant method to evaluate CD39+CD8+ T cell proportion and that this marker can serve as a potential biomarker that predicts response to PD-1 or PD-L1 blockade in patients with NSCLC. Further validation in additional NSCLC cohorts is warranted.
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Affiliation(s)
- Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore; Division of Pathology, Singapore General Hospital, Singapore
| | - Lisda Suteja
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Yannick Simoni
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Kah Weng Lau
- Institute of Molecular Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), Singapore; Division of Pathology, Singapore General Hospital, Singapore
| | - Aaron C Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Hui Hua Li
- Division of Medicine, Singapore General Hospital, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Sherlly Lim
- Institute of Molecular Cell Biology (IMCB), Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Jie Hua Loh
- Division of Pathology, Singapore General Hospital, Singapore
| | - Felicia Y T Wee
- Division of Pathology, Singapore General Hospital, Singapore
| | | | - Angela Takano
- Division of Pathology, Singapore General Hospital, Singapore
| | - Eng Huat Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Tony K H Lim
- Division of Pathology, Singapore General Hospital, Singapore
| | - Evan W Newell
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Daniel S W Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore.
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Sun Z, Nyberg R, Wu Y, Bernard B, Redmond WL. Developing an enhanced 7-color multiplex IHC protocol to dissect immune infiltration in human cancers. PLoS One 2021; 16:e0247238. [PMID: 33596250 DOI: 10.1371/journal.pone.0247238] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 02/03/2021] [Indexed: 01/04/2023] Open
Abstract
The TSA Opal multiplex immunohistochemistry (mIHC) protocol (PerkinElmer) has been used to characterize immune infiltration in human cancers. This technique allows multiple biomarkers to be simultaneously stained in a single tissue section, which helps to elucidate the spatial relationship among individual cell types. We developed and optimized two improved mIHC protocols for a 7-color panel containing 6 biomarkers (CD3, CD8, CD163, PD-L1, FoxP3, and cytokeratin (CK)) and DAPI. The only difference between these two protocols was the staining sequence of those 6 biomarkers as the first sequence is PD-L1/CD163/CD8/CK/CD3/FoxP3/DAPI and the second sequence is FoxP3/CD163/CD8/CK/CD3/PD-L1/DAPI. By comparing PD-L1/FoxP3 staining in mIHC and singleplex PD-L1/FoxP3 staining on the adjacent slide, we demonstrated that the staining sequence does not affect the staining intensity of individual biomarkers as long as a proper antigen retrieval method was used. Our study suggests that use of an antigen retrieval buffer with higher pH value (such as Tris-EDTA pH9.0) than that of the stripping buffers (such as citrate buffer pH6.0) is helpful when using this advanced mIHC method to develop panels with multiple biomarkers. Otherwise, individual biomarkers may exhibit different intensities when the staining sequence is changed. By using this protocol, we characterized immune infiltration and PD-L1 expression in head and neck squamous cell carcinoma (HNSCC), breast cancer (BCa), and non-small cell lung cancer (NSCLC) specimens. We observed a statistically significant increase in CD3+ cell populations within the stroma of NSCLC as compared to BCa and increased PD-L1+ tumor cells in HNSCC as opposed to BCa.
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