Universität zu Köln

Muti, Hannah Sophie, Heij, Lara Rosaline, Keller, Gisela, Kohlruss, Meike, Langer, Rupert ORCID: 0000-0001-9491-3609, Dislich, Bastian, Cheong, Jae-Ho, Kim, Young-Woo, Kim, Hyunki, Kook, Myeong-Cherl, Cunningham, David ORCID: 0000-0001-5158-1069, Allum, William H., Langley, Ruth E., Nankivell, Matthew G., Quirke, Philip ORCID: 0000-0002-3597-5444, Hayden, Jeremy, West, Nicholas P., Irvine, Andrew J., Yoshikawa, Takaki, Oshima, Takashi, Huss, Ralf, Grosser, Bianca, Roviello, Franco, d'Ignazio, Alessia, Quaas, Alexander, Alakus, Hakan, Tan, Xiuxiang, Pearson, Alexander, Luedde, Tom, Ebert, Matthias P., Jaeger, Dirk, Trautwein, Christian, Gaisa, Nadine Therese, Grabsch, Heike, I and Kather, Jakob Nikolas (2021). Correspondence to: Dr Jakob Nikolas Kather, @jnkath For the Genomic Data Commons data portal see https://portal.gdc.cancer.gov See Online for appendix. Lancet Digit. Health, 3 (10). S. E654 - 11. AMSTERDAM: ELSEVIER. ISSN 2589-7500

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Abstract

Background Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning based classifiers to detect microsatellite instability and EBV status from routine histology slides. Methods In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0middot5. Findings Across the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0middot597 (95% CI 0middot522-0middot737) to 0middot836 (0middot795-0middot880) and EBV status in five of eight cohorts, with AUROCs ranging from 0middot819 (0middot752-0middot841) to 0middot897 (0middot513-0middot966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0middot723 (95% CI 0middot676-0middot794) to 0middot863 (0middot747-0middot969) for detection of microsatellite instability and from 0middot672 (0middot403-0middot989) to 0middot859 (0middot823-0middot919) for detection of EBV status. Interpretation Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. Funding German Cancer Aid and German Federal Ministry of Health. Copyright (c) 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Muti, Hannah Sophie UNSPECIFIED UNSPECIFIED UNSPECIFIED Heij, Lara Rosaline UNSPECIFIED UNSPECIFIED UNSPECIFIED Keller, Gisela UNSPECIFIED UNSPECIFIED UNSPECIFIED Kohlruss, Meike UNSPECIFIED UNSPECIFIED UNSPECIFIED Langer, Rupert UNSPECIFIED orcid.org/0000-0001-9491-3609 UNSPECIFIED Dislich, Bastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Cheong, Jae-Ho UNSPECIFIED UNSPECIFIED UNSPECIFIED Kim, Young-Woo UNSPECIFIED UNSPECIFIED UNSPECIFIED Kim, Hyunki UNSPECIFIED UNSPECIFIED UNSPECIFIED Kook, Myeong-Cherl UNSPECIFIED UNSPECIFIED UNSPECIFIED Cunningham, David UNSPECIFIED orcid.org/0000-0001-5158-1069 UNSPECIFIED Allum, William H. UNSPECIFIED UNSPECIFIED UNSPECIFIED Langley, Ruth E. UNSPECIFIED UNSPECIFIED UNSPECIFIED Nankivell, Matthew G. UNSPECIFIED UNSPECIFIED UNSPECIFIED Quirke, Philip UNSPECIFIED orcid.org/0000-0002-3597-5444 UNSPECIFIED Hayden, Jeremy UNSPECIFIED UNSPECIFIED UNSPECIFIED West, Nicholas P. UNSPECIFIED UNSPECIFIED UNSPECIFIED Irvine, Andrew J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Yoshikawa, Takaki UNSPECIFIED UNSPECIFIED UNSPECIFIED Oshima, Takashi UNSPECIFIED UNSPECIFIED UNSPECIFIED Huss, Ralf UNSPECIFIED UNSPECIFIED UNSPECIFIED Grosser, Bianca UNSPECIFIED UNSPECIFIED UNSPECIFIED Roviello, Franco UNSPECIFIED UNSPECIFIED UNSPECIFIED d'Ignazio, Alessia UNSPECIFIED UNSPECIFIED UNSPECIFIED Quaas, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Alakus, Hakan UNSPECIFIED UNSPECIFIED UNSPECIFIED Tan, Xiuxiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Pearson, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Luedde, Tom UNSPECIFIED UNSPECIFIED UNSPECIFIED Ebert, Matthias P. UNSPECIFIED UNSPECIFIED UNSPECIFIED Jaeger, Dirk UNSPECIFIED UNSPECIFIED UNSPECIFIED Trautwein, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Gaisa, Nadine Therese UNSPECIFIED UNSPECIFIED UNSPECIFIED Grabsch, Heike, I UNSPECIFIED UNSPECIFIED UNSPECIFIED Kather, Jakob Nikolas UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-605957
DOI:	10.1016/S2589-7500(21)00133-3
Journal or Publication Title:	Lancet Digit. Health
Volume:	3
Number:	10
Page Range:	S. E654 - 11
Date:	2021
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	2589-7500
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MICROSATELLITE INSTABILITY; GASTRIC-CANCER Multiple languages Medical Informatics; Medicine, General & Internal Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/60595