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El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Brüchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun 2024; 15:1868. [PMID: 38424093 PMCID: PMC10904370 DOI: 10.1038/s41467-024-46298-5] [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] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Affiliation(s)
- Omar S M El Nahhas
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Marko van Treeck
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Fiona R Kolbinger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Katherine J Hewitt
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Mara Graziani
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
| | - Qinghe Zeng
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, F-94000, Créteil, France
| | - Nadina Ortiz-Brüchle
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Cologne, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - 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, 69120, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Pathology & 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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El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Brüchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nat Commun 2024; 15:1253. [PMID: 38341402 PMCID: PMC10858881 DOI: 10.1038/s41467-024-45589-1] [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: 04/11/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
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Grants
- P30 CA008748 NCI NIH HHS
- JNK is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max-Eder-Programme of the German Cancer Aid (grant #70113864), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (Transplant.KI, 01VSF21048) the European Union (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
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Affiliation(s)
- Omar S M El Nahhas
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Chiara M L Loeffler
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Zunamys I Carrero
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Marko van Treeck
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Fiona R Kolbinger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Katherine J Hewitt
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Mara Graziani
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Rue du Technopole 3, 3960, Sierre, Valais, Switzerland
| | - Qinghe Zeng
- Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, Paris, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, F-94000, Créteil, France
| | - Nadina Ortiz-Brüchle
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Cologne, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - 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, 69120, Heidelberg, Germany
- Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Pathology & 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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Reitsam NG, Grozdanov V, Löffler CML, Muti HS, Grosser B, Kather JN, Märkl B. Novel biomarker SARIFA in colorectal cancer: highly prognostic, not genetically driven and histologic indicator of a distinct tumor biology. Cancer Gene Ther 2024; 31:207-216. [PMID: 37990064 PMCID: PMC10874891 DOI: 10.1038/s41417-023-00695-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/24/2023] [Accepted: 11/08/2023] [Indexed: 11/23/2023]
Abstract
SARIFA (Stroma AReactive Invasion Front Areas) has recently emerged as a promising histopathological biomarker for colon and gastric cancer. To elucidate the underlying tumor biology, we assessed SARIFA-status in tissue specimens from The-Cancer-Genome-Atlas (TCGA) cohorts COAD (colonic adenocarcinoma) and READ (rectal adenocarcinoma). For the final analysis, 207 CRC patients could be included, consisting of 69 SARIFA-positive and 138 SARIFA-negative cases. In this external validation cohort, H&E-based SARIFA-positivity was strongly correlated with unfavorable overall, disease-specific, and progression-free survival, partly outperforming conventional prognostic factors. SARIFA-positivity was not associated with known high-risk genetic profiles, such as BRAF V600E mutations or microsatellite-stable status. Transcriptionally, SARIFA-positive CRCs exhibited an overlap with CRC consensus molecular subtypes CMS1 and CMS4, along with distinct differential gene expression patterns, linked to lipid metabolism and increased stromal cell infiltration scores (SIIS). Gene-expression-based drug sensitivity prediction revealed a differential treatment response in SARIFA-positive CRCs. In conclusion, SARIFA represents the H&E-based counterpart of an aggressive tumor biology, demonstrating a partial overlap with CMS1/4 and also adding a further biological layer related to lipid metabolism. Our findings underscore SARIFA-status as an ideal biomarker for refined patient stratification and novel drug developments, particularly given its cost-effective assessment based on routinely available H&E slides.
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Affiliation(s)
- Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
| | | | - Chiara M L Löffler
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Pathology & 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
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Bavarian Cancer Research Center (BZKF), Augsburg, Germany
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Muti HS, Röcken C, Behrens HM, Löffler CML, Reitsam NG, Grosser B, Märkl B, Stange DE, Jiang X, Velduizen GP, Truhn D, Ebert MP, Grabsch HI, Kather JN. Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study. Eur J Cancer 2023; 194:113335. [PMID: 37862795 DOI: 10.1016/j.ejca.2023.113335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/15/2023] [Accepted: 09/03/2023] [Indexed: 10/22/2023]
Abstract
AIM Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). METHODS Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. RESULTS The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. CONCLUSION GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.
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Affiliation(s)
- Hannah S Muti
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Christoph Röcken
- Department of Pathology, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Chiara M L Löffler
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Nic G Reitsam
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bianca Grosser
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Daniel E Stange
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
| | - Xiaofeng Jiang
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Gregory P Velduizen
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Germany
| | - Matthias P Ebert
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; DKFZ-Hector Cancer Institute at the University Medical Center, Mannheim, Germany; Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike I Grabsch
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany; Pathology & 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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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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