1
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Truhn D, Tayebi Arasteh S, Saldanha OL, Müller-Franzes G, Khader F, Quirke P, West NP, Gray R, Hutchins GGA, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Brobeil A, Yuan T, Chang-Claude J, Hoffmeister M, Foersch S, Han T, Keil S, Schulze-Hagen M, Isfort P, Bruners P, Kaissis G, Kuhl C, Nebelung S, Kather JN. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Med Image Anal 2024; 92:103059. [PMID: 38104402 PMCID: PMC10804934 DOI: 10.1016/j.media.2023.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/28/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.
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Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Oliver Lester Saldanha
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Nicholas P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Richard Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Jenny Chang-Claude
- Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Tianyu Han
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Sebastian Keil
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Maximilian Schulze-Hagen
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Peter Isfort
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philipp Bruners
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Georgios Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany; Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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2
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Wagner SJ, Reisenbüchler D, West NP, Niehues JM, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN, Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell 2023; 41:1650-1661.e4. [PMID: 37652006 PMCID: PMC10507381 DOI: 10.1016/j.ccell.2023.08.002] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/18/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Daniel Reisenbüchler
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany
| | - Nicholas P West
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Jan Moritz Niehues
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jiefu Zhu
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Piet A van den Brandt
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Susan D Richman
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rupert Langer
- Institute of Pathology und Molecular Pathology, Johannes Kepler University Hospital Linz, Linz, Österreich
| | - Josien C A Jenniskens
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Kelly Offermans
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | | | - Richard Gray
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Stephen B Gruber
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joel K Greenson
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Gad Rennert
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Steve and Cindy Rasmussen Institute for Genomic Medicine, Lady Davis Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Joseph D Bonner
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Daniel Schmolze
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Nicholas J Hawkins
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Robyn L Ward
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Dion Morton
- University Hospital Birmingham, Birmingham, UK
| | | | - Laura Magill
- University of Birmingham Clinical Trials Unit, Birmingham, UK
| | - Marta Nowak
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jennifer Hay
- Glasgow Tissue Research Facility, University of Glasgow, Queen Elizabeth University Hospital, Glasgow, UK
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Oncology, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK
| | - David N Church
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK; Oxford NIHR Comprehensive Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Christian Matek
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Carol Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Chaolong Peng
- Medical School, Jianggang Shan University, Jiangxi, China
| | - Cheng Zhi
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoming Ouyang
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK; Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; Integrated Pathology Unit, Institute for Cancer Research and Royal Marsden Hospital, London, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Julia A Schnabel
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Tingying Peng
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg.
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3
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Niehues JM, Quirke P, West NP, Grabsch HI, van Treeck M, Schirris Y, Veldhuizen GP, Hutchins GGA, Richman SD, Foersch S, Brinker TJ, Fukuoka J, Bychkov A, Uegami W, Truhn D, Brenner H, Brobeil A, Hoffmeister M, Kather JN. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study. Cell Rep Med 2023; 4:100980. [PMID: 36958327 PMCID: PMC10140458 DOI: 10.1016/j.xcrm.2023.100980] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/28/2022] [Accepted: 02/24/2023] [Indexed: 03/25/2023]
Abstract
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.
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Affiliation(s)
- Jan Moritz Niehues
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Philip Quirke
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK
| | - Heike I Grabsch
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK; Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, the Netherlands
| | - Marko van Treeck
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Yoni Schirris
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; University of Amsterdam, 1012 WP Amsterdam, the Netherlands
| | - Gregory P Veldhuizen
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Gordon G A Hutchins
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK
| | - Susan D Richman
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, 55131 Mainz, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8523, Japan; Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan
| | - Wataru Uegami
- Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK; Department of Medicine I, University Hospital Dresden, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120 Heidelberg, Germany.
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4
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Echle A, Ghaffari Laleh N, Quirke P, Grabsch HI, Muti HS, Saldanha OL, Brockmoeller SF, van den Brandt PA, Hutchins GGA, Richman SD, Horisberger K, Galata C, Ebert MP, Eckardt M, Boutros M, Horst D, Reissfelder C, Alwers E, Brinker TJ, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Chang-Claude J, Brenner H, Trautwein C, Boor P, Jaeger D, Gaisa NT, Hoffmeister M, West NP, Kather JN. Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application. ESMO Open 2022; 7:100400. [PMID: 35247870 PMCID: PMC9058894 DOI: 10.1016/j.esmoop.2022.100400] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds. METHOD We trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities. RESULTS Classifiers achieved clinical-grade performance, yielding an area under the receiver operating curve (AUROC) of up to 0.96 without using any manual annotations. Subsequently, we show that the AI system can be applied as a rule-out test: by using cohort-specific thresholds, on average 52.73% of tumors in each surgical cohort [total number of MSI/dMMR = 1020, microsatellite stable (MSS)/ proficient mismatch repair (pMMR) = 7323 patients] could be identified as MSS/pMMR with a fixed sensitivity at 95%. In an additional cohort of N = 1530 (MSI/dMMR = 211, MSS/pMMR = 1319) endoscopy biopsy samples, the system achieved an AUROC of 0.89, and the cohort-specific threshold ruled out 44.12% of tumors with a fixed sensitivity at 95%. As a more robust alternative to cohort-specific thresholds, we showed that with a fixed threshold of 0.25 for all the cohorts, we can rule-out 25.51% in surgical specimens and 6.10% in biopsies. INTERPRETATION When applied in a clinical setting, this means that the AI system can rule out MSI/dMMR in a quarter (with global thresholds) or half of all CRC patients (with local fine-tuning), thereby reducing cost and turnaround time for molecular profiling.
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Affiliation(s)
- A Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - N Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - P Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - H I Grabsch
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - H S Muti
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - O L Saldanha
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - S F Brockmoeller
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - P A van den Brandt
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - G G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - S D Richman
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - K Horisberger
- Department of Abdominal and Transplantation Surgery, University Hospital of Zurich, Zurich, Switzerland
| | - C Galata
- Department of Surgery, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Division of Thoracic Surgery, Academic Thoracic Center Mainz, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
| | - M P Ebert
- Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Innate Immunoscience (MI3) and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Cancer Center, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - M Eckardt
- Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - M Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - D Horst
- Institut für Pathologie Charité, Berlin, Germany
| | - C Reissfelder
- Department of Surgery, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - E Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - T J Brinker
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - R Langer
- Institute of Pathology, Inselspital, University of Bern, Bern, Switzerland
| | - J C A Jenniskens
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - K Offermans
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - W Mueller
- Gemeinschaftspraxis Pathologie, Starnberg, Germany
| | - R Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, UK
| | - S B Gruber
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, USA
| | - J K Greenson
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, USA
| | - G Rennert
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Steve and Cindy Rasmussen Institute for Genomic Medicine, Lady Davis Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - J D Bonner
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, USA
| | - D Schmolze
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, USA
| | - J Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - H Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - C Trautwein
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - P Boor
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany; Department of Nephrology and Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - D Jaeger
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - N T Gaisa
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - M Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - N P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - J N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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5
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Mitrovic B, Handley K, Assarzadegan N, Chang HL, Dawson HAE, Grin A, Hutchins GGA, Magill L, Quirke P, Riddell RH, Gray RG, Kirsch R. Prognostic and Predictive Value of Tumor Budding in Colorectal Cancer. Clin Colorectal Cancer 2021; 20:256-264. [PMID: 34099382 DOI: 10.1016/j.clcc.2021.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 01/20/2021] [Revised: 05/03/2021] [Accepted: 05/05/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Tumor budding (TB) is an adverse prognostic factor in colorectal cancer (CRC). International consensus on a standardized assessment method has led to its wider reporting. However, uncertainty regarding its clinical value persists. This study aimed to (1) confirm the prognostic significance of TB, particularly in stage II CRC; (2) to determine optimum thresholds for TB risk grouping; and (3) to determine whether TB influences responsiveness to chemotherapy. METHODS TB was assessed in CRC sections from 1575 QUASAR trial patients randomized between adjuvant chemotherapy and observation. Optimal risk group cutoffs were determined by maximum likelihood methods, with their influence on recurrence and mortality investigated in stratified log-rank analyses on exploratory (n = 504), hypothesis-testing (n = 478), and final (n = 593) data sets. RESULTS The optimal threshold for high-grade TB (HGTB) was ≥ 10 buds per 1.23 mm2. High-grade TB tumors had significantly worse outcomes than those with lower TB: 10-year recurrence 36% versus 22% (risk ratio, 2.00 [95% CI, 1.62-2.45]; 2P < .0001) and 10-year mortality 50% vs. 37% (risk ratio, 1.53 [95% CI, 1.34-1.76]; 2P < .0001). The prognostic significance remained equally strong after allowance for other pathological risk factors, including stage, grade, lymphovascular invasion, and mismatch repair status. There was a nonsignificant trend toward increasing chemotherapy efficacy with increasing bud counts. CONCLUSIONS TB is a strong independent predictor of recurrence. Chemotherapy efficacy is comparable in patients with higher and lower TB; hence, absolute reductions in recurrence and death with chemotherapy should be about twice as large in patients with ≥ 10 than < 10 TB counts.
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Affiliation(s)
- Bojana Mitrovic
- Department of Pathology and Laboratory Medicine, Health Sciences North, Sudbury, ON, Canada; University of Toronto, Toronto, Canada.
| | - Kelly Handley
- Birmingham Clinical Trials Unit, Birmingham, United Kingdom
| | | | | | | | | | - Gordon G A Hutchins
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Laura Magill
- Birmingham Clinical Trials Unit, Birmingham, United Kingdom
| | - Philip Quirke
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Robert H Riddell
- University of Toronto, Toronto, Canada; Mount Sinai Hospital, Toronto, ON, Canada
| | - Richard G Gray
- Clinical Trial Service Unit & Epidemiological Studies, University of Oxford, United Kingdom
| | - Richard Kirsch
- University of Toronto, Toronto, Canada; Mount Sinai Hospital, Toronto, ON, Canada
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6
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West NP, Gallop N, Kaye D, Glover A, Young C, Hutchins GGA, Brockmoeller SF, Westwood AC, Rossington H, Quirke P. Lynch syndrome screening in colorectal cancer: results of a prospective 2-year regional programme validating the NICE diagnostics guidance pathway throughout a 5.2-million population. Histopathology 2021; 79:690-699. [PMID: 33872400 DOI: 10.1111/his.14390] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/07/2021] [Accepted: 04/15/2021] [Indexed: 11/27/2022]
Abstract
AIMS Screening all patients newly diagnosed with colorectal cancer (CRC) for possible Lynch syndrome (LS) has been recommended in the United Kingdom since the National Institute for Health and Care Excellence (NICE) released new diagnostics guidance in February 2017. We sought to validate the NICE screening pathway through a prospective regional programme throughout a 5.2-million population during a 2-year period. METHODS AND RESULTS Pathology departments at 14 hospital trusts in the Yorkshire and Humber region of the United Kingdom were invited to refer material from patients with newly diagnosed CRC aged 50 years or over between 1 April 2017 and 31 March 2019 for LS screening. Testing consisted of immunohistochemistry for MLH1, PMS2, MSH2 and MSH6 followed by BRAF mutation analysis ± MLH1 promoter methylation testing in cases showing MLH1 loss. A total of 3141 individual specimens were submitted for testing from 12 departments consisting of 3061 unique tumours and 2791 prospectively acquired patients with CRC. Defective mismatch repair (dMMR) was observed in 15% of cases. In cases showing MLH1 loss, 76% contained a detectable BRAF mutation and, of the remainder, 77% showed MLH1 promoter hypermethylation. Of the patients included in the final analysis, 81 (2.9%) had an indication for germline testing. CONCLUSION LS screening using the NICE diagnostics guidance pathway is deliverable at scale identifying significant numbers of patients with dMMR. This information is used to refer patients to regional clinical genetics services in addition to informing treatment pathways including the use of adjuvant/neoadjuvant chemotherapy and immunotherapy.
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Affiliation(s)
- Nicholas P West
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Niall Gallop
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Danny Kaye
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Amy Glover
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Caroline Young
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Gordon G A Hutchins
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Scarlet F Brockmoeller
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Alice C Westwood
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Hannah Rossington
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Philip Quirke
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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7
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Guiter GE, Sapia S, Wright AI, Hutchins GGA, Arayssi T. Development of a Remote Online Collaborative Medical School Pathology Curriculum with Clinical Correlations, across Several International Sites, through the Covid-19 Pandemic. Med Sci Educ 2021; 31:549-556. [PMID: 33495717 PMCID: PMC7815444 DOI: 10.1007/s40670-021-01212-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/08/2021] [Indexed: 05/30/2023]
Abstract
INTRODUCTION Due to the Covid-19 social distancing restrictions, in March 2020, Weill Cornell Medicine-Qatar decided to replace students' clinical instruction with novel online electives. Hence, we implemented an innovative online and remote pathology curriculum, anchored on virtual microscopy and Zoom videoconferencing: ideal tools to support online teaching. OBJECTIVE To assess a new curriculum implementation at Weill Cornell Medicine-Qatar. MATERIALS AND METHODS This for-credit, 2-week elective included 6 synchronous Zoom sessions where complex clinicopathological cases were discussed in small groups. We used open access digital microscopy slides from the University of Leeds' Virtual Pathology Library (http://www.virtualpathology.leeds.ac.uk/slides/library/). Students independently prepared for these sessions by reviewing cases, slides, readings, and questions in advance (asynchronous self-directed learning anchored on a flipped classroom model), and wrote a final review of a case. An assessment and feedback were given to each student. RESULTS Four elective iterations were offered to a total of 29 students, with learners and faculty spread over 4 countries. During the Zoom sessions, students controlled the digital slides and offered their own diagnoses, followed by group discussions to strengthen autonomy and confidence. We surveyed learners about the elective's performance (program evaluation). Students conveyed high levels of satisfaction about the elective's overall quality, their pathology learning and online interactions, with minimal challenges related to the remote nature of the course. DISCUSSION AND CONCLUSIONS Technological innovations mitigate sudden disruptions in medical education. A remote curriculum allows instruction at any distance, at any time, from anywhere, enhancing educational exchanges, flexibility and globalization in medical education.
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Affiliation(s)
- Gerardo E. Guiter
- Division of Medical Education, Weill Cornell Medicine-Qatar, 445 East 69 Street, RM 432, New York, NY 10021 USA
| | - Sandra Sapia
- Division of Medical Education, Weill Cornell Medicine- Qatar, Qatar Foundation - Education City, P.O. Box 24144, Doha, Qatar
| | - Alexander I. Wright
- Section of Pathology, Leeds Institute of Medical Research, University of Leeds, 4.11 Wellcome Trust Brenner Building, St James’s University Hospital, Beckett Street, Leeds, LS9 7TF UK
| | - Gordon G. A. Hutchins
- Leeds Teaching Hospitals NHS Trust/University of Leeds. Histopathology and Molecular Pathology, St James’ University Hospital, Beckett Street, Leeds, LS9 7TF UK
| | - Thurayya Arayssi
- Division of Medical Education, Weill Cornell Medicine-Qatar, 445 East 69 Street, RM 432, New York, NY 10021 USA
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Wright AI, Dunn CM, Hale M, Hutchins GGA, Treanor DE. The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis. IEEE J Biomed Health Inform 2021; 25:307-314. [PMID: 33347418 DOI: 10.1109/jbhi.2020.3046094] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in detail, the results from a previously published algorithm automating the generation of tumor:stroma ratio (TSR) in colorectal clinical trial datasets. One dataset consisting of 2,211 cases and 106,268 expert-labelled images is used to identify quality issues, by visually inspecting cases where algorithm-pathologist agreement is lowest. Twelve categories are identified and used to analyze pathologist-algorithm agreement in relation to these categories. Of the 2,211 cases, 701 were found to be free from any image quality issues. Algorithm performance was then assessed, comparing pathologist agreement with image quality classification. It was found that agreement was lowest on poorly differentiated tissue, with a mean TSR difference of 0.25 (sd = 0.24). Removing images that contained quality issues increased accuracy from 80% to 83%, at the expense of reducing the dataset to 33,736 images (32%). Training the algorithm on the optimized dataset, prior to testing on all images saw a decrease in accuracy of 4%, indicating that the optimized dataset did not contain enough variation to generate a fully representative model. The results provide an in-depth perspective on image quality, highlighting the importance of the effects on downstream image analysis.
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9
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Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GGA, Heij LR, Tan X, Richman SD, Krause J, Alwers E, Jenniskens J, Offermans K, Gray R, Brenner H, Chang-Claude J, Trautwein C, Pearson AT, Boor P, Luedde T, Gaisa NT, Hoffmeister M, Kather JN. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. Gastroenterology 2020; 159:1406-1416.e11. [PMID: 32562722 PMCID: PMC7578071 DOI: 10.1053/j.gastro.2020.06.021] [Citation(s) in RCA: 164] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/22/2020] [Accepted: 06/08/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed. METHODS We collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). RESULTS The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization. CONCLUSIONS We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.
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Affiliation(s)
- Amelie Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Heike Irmgard Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Philip Quirke
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Piet A van den Brandt
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Nicholas P West
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Gordon G A Hutchins
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Lara R Heij
- Visceral and Transplant Surgery, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Xiuxiang Tan
- Visceral and Transplant Surgery, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Susan D Richman
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Jeremias Krause
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Josien Jenniskens
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Kelly Offermans
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Richard Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Trautwein
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Peter Boor
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Tom Luedde
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Division of Gastroenterology, Hepatology, and Hepatobiliary Oncology, Aachen, Germany
| | | | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany; Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
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10
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Davarzani N, Hewitt LC, Hale MD, Melotte V, Nankivell M, Hutchins GGA, Cunningham D, Allum WH, Langley RE, Jolani S, Grabsch HI. Histological intratumoral heterogeneity in pretreatment esophageal cancer biopsies predicts survival benefit from neoadjuvant chemotherapy: results from the UK MRC OE02 trial. Dis Esophagus 2020; 33:5863449. [PMID: 32591823 PMCID: PMC7397482 DOI: 10.1093/dote/doaa058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/16/2020] [Accepted: 05/28/2020] [Indexed: 12/24/2022]
Abstract
Despite the use of multimodal treatment, survival of esophageal cancer (EC) patients remains poor. One proposed explanation for the relatively poor response to cytotoxic chemotherapy is intratumor heterogeneity. The aim was to establish a statistical model to objectively measure intratumor heterogeneity of the proportion of tumor (IHPoT) and to use this newly developed method to measure IHPoT in the pretreatment biopsies from from EC patients recruited to the OE02 trial. A statistical mixed effect model (MEM) was established for estimating IHPoT based on variation in hematoxylin/eosin (HE) stained pretreatment biopsy pieces from the same individual in 218 OE02 trial patients (103 treated by chemotherapy and surgery (chemo+surgery); 115 patients treated by surgery alone). The relationship between IHPoT, prognosis, chemotherapy survival benefit, and clinicopathological variables was assessed. About 97 (44.5%) and 121 (55.5%) ECs showed high and low IHPoT, respectively. There was no significant difference in IHPoT between surgery (median [range], 0.1637 [0-3.17]) and chemo+surgery (median [range], 0.1692 [0-2.69]) patients (P = 0.43). Chemo+surgery patients with low IHPoT had a significantly longer survival than surgery patients (HR = 1.81, 95% CI: 1.20-2.75, P = 0.005). There was no survival difference between chemo+surgery and surgery patients with high IHPoT (HR = 1.15, 95% CI: 0.72-1.81, P = 0.566). This is the first study suggesting that IHPoT measured in the pretreatment biopsy can predict chemotherapy survival benefit in EC patients. IHPoT may represent a clinically useful biomarker for patient treatment stratification. Future studies should determine if pathologists can reliably estimate IHPoT.
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Affiliation(s)
- Naser Davarzani
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center + Maastricht, The Netherlands,Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Amsterdam University, Amsterdam, The Netherlands
| | - Lindsay C Hewitt
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center + Maastricht, The Netherlands,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Matthew D Hale
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Veerle Melotte
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center + Maastricht, The Netherlands,Department of Clinical Genetics, University of Rotterdam, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Matthew Nankivell
- Medical Research Council Clinical Trials Unit at University College, London, UK
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - David Cunningham
- Gastrointestinal and Lymphoma Unit, Royal Marsden Hospital, London, UK
| | | | - Ruth E Langley
- Medical Research Council Clinical Trials Unit at University College, London, UK
| | - Shahab Jolani
- Department of Methodology and Statistics, CAPHRI, Maastricht University, Maastricht, The Netherlands
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center + Maastricht, The Netherlands,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK,Address correspondence to: Professor Heike I. Grabsch, Department of Pathology, Maastricht University Medical Center+, P. Debyelaan, 256229 HX Maastricht, The Netherlands.
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11
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Barrow P, Richman SD, Wallace AJ, Handley K, Hutchins GGA, Kerr D, Magill L, Evans DG, Gray R, Quirke P, Hill J. Confirmation that somatic mutations of beta-2 microglobulin correlate with a lack of recurrence in a subset of stage II mismatch repair deficient colorectal cancers from the QUASAR trial. Histopathology 2019; 75:236-246. [PMID: 31062389 PMCID: PMC6772160 DOI: 10.1111/his.13895] [Citation(s) in RCA: 10] [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: 02/17/2019] [Revised: 04/16/2019] [Accepted: 05/01/2019] [Indexed: 01/05/2023]
Abstract
Aims Beta2‐microglobulin (B2M) forms part of the HLA class I complex and plays a role in metastatic biology. B2M mutations occur frequently in mismatch repair‐deficient colorectal cancer (dMMR CRC), with limited data suggesting they may protect against recurrence. Our experimental study tested this hypothesis by investigating B2M mutation status and B2M protein expression and recurrence in patients in the stage II QUASAR clinical trial. Methods and results Sanger sequencing was performed for the three coding exons of B2M on 121 dMMR and a subsample of 108 pMMR tumours; 52 with recurrence and 56 without. B2M protein expression was assessed by immunohistochemistry. Mutation status and protein expression were correlated with recurrence and compared to proficient mismatch repair (pMMR) CRCs. Deleterious B2M mutations were detected in 39 of 121 (32%) dMMR tumours. Five contained missense B2M‐variants of unknown significance, so were excluded from further analyses. With median follow‐up of 7.4 years, none of the 39 B2M‐mutant tumours recurred, compared with 14 of 77 (18%) B2M‐wild‐type tumours (P = 0.005); six at local and eight at distant sites. Sensitivity and specificity of IHC in detecting B2M mutations was 87 and 71%, respectively. Significantly (P < 0.0001) fewer (three of 104, 2.9%) of the 108 pMMR CRCs demonstrated deleterious B2M mutations. One pMMR tumour, containing a frameshift mutation, later recurred. Conclusion B2M mutations were detected in nearly one‐third of dMMR cancers, none of which recurred. B2M mutation status has potential clinical utility as a prognostic biomarker in stage II dMMR CRC. The mechanism of protection against recurrence and whether this protection extends to stage III disease remains unclear.
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Affiliation(s)
- Paul Barrow
- Department of Surgery, Manchester Royal Infirmary, Central Manchester University Hospitals NHS Trust, Manchester, UK
| | - Susan D Richman
- Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, St James' University Hospital, Leeds, UK
| | - Andrew J Wallace
- Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University Hospitals NHS Trust, Manchester, UK
| | - Kelly Handley
- Birmingham Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Gordon G A Hutchins
- Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, St James' University Hospital, Leeds, UK
| | - David Kerr
- Cancer Medicine, University of Oxford, Oxford, UK
| | - Laura Magill
- Birmingham Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - D Gareth Evans
- Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University Hospitals NHS Trust, Manchester, UK
| | | | - Phil Quirke
- Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, St James' University Hospital, Leeds, UK
| | - James Hill
- Department of Surgery, Manchester Royal Infirmary, Central Manchester University Hospitals NHS Trust, Manchester, UK
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12
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Davarzani N, Hutchins GGA, West NP, Hewitt LC, Nankivell M, Cunningham D, Allum WH, Smyth E, Valeri N, Langley RE, Grabsch HI. Prognostic value of pathological lymph node status and primary tumour regression grading following neoadjuvant chemotherapy - results from the MRC OE02 oesophageal cancer trial. Histopathology 2018; 72:1180-1188. [PMID: 29465751 PMCID: PMC5969086 DOI: 10.1111/his.13491] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 02/14/2018] [Indexed: 12/19/2022]
Abstract
AIMS Neoadjuvant chemotherapy (NAC) remains an important therapeutic option for advanced oesophageal cancer (OC). Pathological tumour regression grade (TRG) may offer additional information by directing adjuvant treatment and/or follow-up but its clinical value remains unclear. We analysed the prognostic value of TRG and associated pathological factors in OC patients enrolled in the Medical Research Council (MRC) OE02 trial. METHODS AND RESULTS Histopathology was reviewed in 497 resections from OE02 trial participants randomised to surgery (S group; n = 244) or NAC followed by surgery [chemotherapy plus surgery (CS) group; n = 253]. The association between TRG groups [responders (TRG1-3) versus non-responders (TRG4-5)], pathological lymph node (LN) status and overall survival (OS) was analysed. One hundred and ninety-five of 253 (77%) CS patients were classified as 'non-responders', with a significantly higher mortality risk compared to responders [hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.05-2.24, P = 0.026]. OS was significantly better in patients without LN metastases irrespective of TRG [non-responders HR = 1.87, 95% CI = 1.33-2.63, P < 0.001 versus responders HR = 2.21, 95% CI = 1.11-4.10, P = 0.024]. In multivariate analyses, LN status was the only independent factor predictive of OS in CS patients (HR = 1.93, 95% CI = 1.42-2.62, P < 0.001). Exploratory subgroup analyses excluding radiotherapy-exposed patients (n = 48) showed similar prognostic outcomes. CONCLUSION Lymph node status post-NAC is the most important prognostic factor in patients with resectable oesophageal cancer, irrespective of TRG. Potential clinical implications, e.g. adjuvant treatment or intensified follow-up, reinforce the importance of LN dissection for staging and prognostication.
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Affiliation(s)
- Nasser Davarzani
- GROW School for Oncology and Developmental BiologyDepartment of PathologyMaastricht University Medical CentreMaastrichtthe Netherlands
- Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtthe Netherlands
| | - Gordon G A Hutchins
- Section of Pathology and Tumour BiologyLeeds Institute of Cancer and PathologyUniversity of LeedsLeedsUK
| | - Nicholas P West
- Section of Pathology and Tumour BiologyLeeds Institute of Cancer and PathologyUniversity of LeedsLeedsUK
| | - Lindsay C Hewitt
- GROW School for Oncology and Developmental BiologyDepartment of PathologyMaastricht University Medical CentreMaastrichtthe Netherlands
| | | | - David Cunningham
- Gastrointestinal and Lymphoma UnitRoyal Marsden HospitalLondonUK
| | | | - Elizabeth Smyth
- Gastrointestinal and Lymphoma UnitRoyal Marsden HospitalLondonUK
| | - Nicola Valeri
- Gastrointestinal and Lymphoma UnitRoyal Marsden HospitalLondonUK
- Department of Molecular PathologyThe Institute of Cancer ResearchLondonUK
| | - Ruth E Langley
- MRC Clinical Trials UnitUniversity College LondonLondonUK
| | - Heike I Grabsch
- GROW School for Oncology and Developmental BiologyDepartment of PathologyMaastricht University Medical CentreMaastrichtthe Netherlands
- Section of Pathology and Tumour BiologyLeeds Institute of Cancer and PathologyUniversity of LeedsLeedsUK
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Hutchins GGA, Treanor D, Wright A, Handley K, Magill L, Tinkler-Hundal E, Southward K, Seymour M, Kerr D, Gray R, Quirke P. Intratumoral stromal morphometry predicts disease recurrence but not response to 5-fluorouracil-results from the QUASAR trial of colorectal cancer. Histopathology 2017; 72:391-404. [PMID: 28746977 DOI: 10.1111/his.13326] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 07/24/2017] [Indexed: 12/12/2022]
Abstract
AIMS The biological importance of tumour-associated stroma is becoming increasingly apparent, but its clinical utility remains ill-defined. For stage II/Dukes B colorectal cancer (CRC), clinical biomarkers are urgently required to direct therapeutic options. We report here prognostic/predictive analyses, and molecular associations, of stromal morphometric quantification in the Quick and Simple and Reliable (QUASAR) trial of CRC. METHODS AND RESULTS Relative proportions of tumour epithelium (PoT) or stroma (PoS) were morphometrically quantified on digitised haematoxylin and eosin (H&E) sections derived from 1800 patients enrolled in QUASAR, which randomised 3239 (91% stage II) CRC patients between adjuvant fluorouracil/folinic acid (FUFA) chemotherapy and observation. The prognostic and predictive values of PoT/PoS measurements were determined by the use of stratified log-rank analyses. A high proportion of tumour stroma (≥50%) was associated with an increased recurrence risk: 31.3% (143/457) recurrence for ≥50% versus 21.9% (294/1343) for <50% [rate ratio (RR) 1.62; 95% confidence interval (CI) 1.30-2.02; P < 0.0001]. Of patients with stromal proportions of ≥65%, 40% (46/115) had recurrent disease within 10 years. The adverse prognostic effect of a high stromal proportion was independent of established prognostic variables, and was maintained in stage II/Dukes B patients (RR 1.62; 95% CI 1.26-2.08; P = 0.0002). KRAS mutation in the presence of a high stromal proportion augmented recurrence risk (RR 2.93; 95% CI 1.87-4.59; P = 0.0005). Stromal morphometry did not predict response to FUFA chemotherapy. CONCLUSIONS Simple digital morphometry applied to a single representative H&E section identifies CRC patients with a >50% higher risk of disease recurrence. This technique can reliably partition patients into subpopulations with different risks of tumour recurrence in a simple and cost-effective manner. Further prospective validation is warranted.
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Affiliation(s)
- Gordon G A Hutchins
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Darren Treanor
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Alexander Wright
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Kelly Handley
- Birmingham Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Laura Magill
- Birmingham Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Emma Tinkler-Hundal
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Katie Southward
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Matthew Seymour
- National Cancer Research Network Coordinating Centre, University of Leeds, Leeds, UK
| | - David Kerr
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Richard Gray
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philip Quirke
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
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14
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Quirke P, Hutchins GGA, West NP. Commentary on Demetter et al. Colorectal Dis 2013; 15:1358-60. [PMID: 24192257 DOI: 10.1111/codi.12433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- P Quirke
- Department of Pathology, Anatomy and Tumour Biology, Leeds Institute of Cancer and Pathology, School of Medicine, University of Leeds, Leeds, UK.
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15
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Wu Y, Grabsch H, Ivanova T, Tan IB, Murray J, Ooi CH, Wright AI, West NP, Hutchins GGA, Wu J, Lee M, Lee J, Koo JH, Yeoh KG, van Grieken N, Ylstra B, Rha SY, Ajani JA, Cheong JH, Noh SH, Lim KH, Boussioutas A, Lee JS, Tan P. Comprehensive genomic meta-analysis identifies intra-tumoural stroma as a predictor of survival in patients with gastric cancer. Gut 2013; 62:1100-11. [PMID: 22735568 DOI: 10.1136/gutjnl-2011-301373] [Citation(s) in RCA: 128] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Gastric adenocarcinoma (gastric cancer, GC) is a major cause of global cancer mortality. Identifying molecular programmes contributing to GC patient survival may improve our understanding of GC pathogenesis, highlight new prognostic factors and reveal novel therapeutic targets. The authors aimed to produce a comprehensive inventory of gene expression programmes expressed in primary GCs, and to identify those expression programmes significantly associated with patient survival. DESIGN Using a network-modelling approach, the authors performed a large-scale meta-analysis of GC transcriptome data integrating 940 gastric transcriptomes from multiple independent patient cohorts. The authors analysed a training set of 428 GCs and 163 non-malignant gastric samples, and a validation set of 288 GCs and 61 non-malignant gastric samples. RESULTS The authors identified 178 gene expression programmes ('modules') expressed in primary GCs, which were associated with distinct biological processes, chromosomal location patterns, cis-regulatory motifs and clinicopathological parameters. Expression of a transforming growth factor β (TGF-β) signalling associated 'super-module' of stroma-related genes consistently predicted patient survival in multiple GC validation cohorts. The proportion of intra-tumoural stroma, quantified by morphometry in tissue sections from gastrectomy specimens, was also significantly associated with stromal super-module expression and GC patient survival. CONCLUSION Stromal gene expression predicts GC patient survival in multiple independent cohorts, and may be closely related to the intra-tumoural stroma proportion, a specific morphological GC phenotype. These findings suggest that therapeutic approaches targeting the GC stroma may merit evaluation.
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Affiliation(s)
- Yonghui Wu
- Cellular and Molecular Research, National Cancer Centre, Singapore
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Abstract
The pathological examination of material removed from patients with colorectal neoplasia is important. It provides a wide range of information on, for example, the quality and completeness of excision, the stage and biological aggressiveness, the need for further therapy, and response to therapy. Molecular testing adds valuable information on genetic risk and is required before treatment with anti-EGF-r antibodies. This article highlights the value derived from macroscopic inspection of surgical specimens, careful microscopy and excellent reporting according to national guidelines. Increasing use of a number of preoperative therapies and combinations in rectal cancer change the pathological features found and a standardised approach to the diagnosis of complete response is required. It touches upon the issues with frequent changes in TNM staging and the difficulties these changes are causing. The widespread introduction of bowel cancer screening is changing the stage of presentation of colorectal cancer leading to increasing numbers of local excisions and polyp cancers.
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Affiliation(s)
- Philip Quirke
- Department of Histopathology, Pathology and Tumour Biology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK.
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Abstract
As a consequence of new innovative therapies and therapeutic combinations, the treatment of advanced colorectal cancer is becoming increasingly complex. Validated molecular biomarkers could contribute to patient management, but until recently, none has been routinely used. With the recognition that activating mutations of the KRAS oncogene can predict resistance to anti-epidermal growth factor receptor agents, the clinical value of biomarkers in advanced colorectal cancer has been brought to the fore. Prognostic and predictive biomarkers that reflect the molecular and therapeutic complexities of advanced colorectal cancer may provide valuable information regarding likely clinical outcome and therapeutic response and thus may improve patient management and therapeutic agent selection. Such biomarkers are discussed herein.
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Affiliation(s)
- S D Richman
- Pathology and Tumour Biology, Leeds Institute of Molecular Medicine, Leeds, UK
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