Preservation of Epstein–Barr virus status and mismatch repair protein status along the metastatic course of gastric cancer

Dislich, Bastian; Blaser, Nicola; Berger, Martin D.; Gloor, Beat; Langer, Rupert

doi:10.1111/his.14059

Cited by 15 publications

(18 citation statements)

References 21 publications

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“…EBV-associated tumors have been assigned a specific subtype only in the TCGA classification with a rate of 9%, but the majority of EBV-associated tumors group in the MSS/p53+ subtype of the ACRG (12/18 EBV positive tumors), and the authors admit that these tumors may represent an own subtype [15]. In our cohort, only two (1.7%) tumors are EBV-associated which is within the worldwide reported range of prevalence and in line with previous data of Switzerland [38,39]. The CIN subtype, with 50% being the most common subtype of TCGA classification, is characterized by chromosomal instability, namely the degree of aneuploidy and a high somatic copy number variation.…”

Section: Discussionsupporting

confidence: 91%

Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach

Daun

Nienhold

Paasinen-Sohns

et al. 2021

Cancers

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Gastric adenocarcinoma (GAC) is a heterogeneous disease and at least two major studies have recently provided a molecular classification for this tumor: The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ARCG). Both classifications quote four molecular subtypes, but these subtypes only partially overlap. In addition, the classifications are based on complex and cost-intensive technologies, which are hardly feasible for everyday practice. Therefore, simplified approaches using immunohistochemistry (IHC), in situ hybridization (ISH) as well as commercially available next generation sequencing (NGS) have been considered for routine use. In the present study, we screened 115 GAC by IHC for p53, MutL Homolog 1 (MLH1) and E-cadherin and performed ISH for Epstein–Barr virus (EBV). In addition, sequencing by NGS for TP53 and tumor associated genes was performed. With this approach, we were able to define five subtypes of GAC: (1) Microsatellite Instable (MSI), (2) EBV-associated, (3) Epithelial Mesenchymal Transition (EMT)-like, (4) p53 aberrant tumors surrogating for chromosomal instability and (5) p53 proficient tumors surrogating for genomics stable cancers. Furthermore, by considering lymph node metastasis in the p53 aberrant GAC, a better prognostic stratification was achieved which finally allowed us to separate the GAC highly significant in a group with poor and good-to-intermediate prognosis, respectively. Our data show that molecular classification of GAC can be achieved by using commercially available assays including IHC, ISH and NGS. Furthermore, we present an integrative workflow, which has the potential to overcome the uncertainty resulting from discrepancies from existing classification schemes.

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Section: Discussionsupporting

confidence: 91%

Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach

Daun

Nienhold

Paasinen-Sohns

et al. 2021

Cancers

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“…In this retrospective, multicentre cohort study, we collected digitised histological slides from formalin-fixed paraffin-embedded gastric cancer resection samples with matched microsatellite instability and EBV status from ten cohorts of patients with gastric cancer. Samples from the ten cohorts were as follows: samples from the pathology archives of Inselspital, University of Bern (Bern, Switzerland—ie, the BERN cohort); 23 samples from the CLASSIC trial from participating study centres in South Korea (ie, the CLASSIC cohort); 24 samples from the Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial from participating study centres in the UK (ie, the MAGIC cohort); 25 samples from the Leeds Teaching Hospitals National Health Service Trust (Leeds, UK—ie, the LEEDS cohort); samples from the Kanagawa Cancer Center Hospital (Yokohama, Japan—ie, the KCCH cohort); 26 samples from the pathology archive at the University Hospital Augsburg (Augsburg, Germany—ie, the AUGSB cohort); samples from the University of Siena (Siena, Italy—ie, the ITALIAN cohort); 27 samples from the pathology archive at University of Cologne (Cologne, Germany—ie, the KOELN cohort); 28 samples from the Institute of Pathology at the Technical University Munich (Munich, Germany—ie, the TUM cohort); 4 and samples (diagnostic slides) originate from the The Cancer Genome Atlas (TCGA) project and are derived from the National Institute of Health Genomic Data Commons portal ( the TCGA cohort ). 8 , 29 …”

Section: Methodsmentioning

confidence: 99%

“…All other data were provided by the respective study principal investigators and different data sharing policies apply as described previously in those original publications. We cannot make any individual patient-level data available to others, but these data can be requested from the respective pathology institutions as defined in the references for BERN, 23 CLASSIC, 24 MAGIC, 25 LEEDS, 26 KCCH, 26 AUGSB, 4 ITALIAN, 27 KOELN, 28 and TUM. 4 …”

Section: Data Sharingmentioning

confidence: 99%

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Muti

Heij

Keller

et al. 2021

The Lancet Digital Health

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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 learningbased classifiers to detect microsatellite instability and EBV status from routine histology slides. MethodsIn 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 0•5. FindingsAcross 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 0•597 (95% CI 0•522-0•737) to 0•836 (0•795-0•880) and EBV status in five of eight cohorts, with AUROCs ranging from 0•819 (0•752-0•841) to 0•897 (0•513-0•966). 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 0•723 (95% CI 0•676-0•794) to 0•863 (0•747-0•969) for detection of microsatellite instability and from 0•672 (0•403-0•989) to 0•859 (0•823-0•919) 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.

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“…In addition, we used four proprietary datasets: the DACHS study ("Darmkrebs: Chancen der Verhütung durch Screening"), a large population-based case-control and patient cohort study on CRC, including samples of patients with stages I-IV from different laboratories in southwestern Germany coordinated by the German Cancer Research Center (Heidelberg, Germany) [23,24]. The DACHS study was approved by the ethics committees of the University of Heidelberg and of the Medical Chambers of Baden-BERN dataset is a single-center dataset collected from clinical routine samples at the pathology archive at Inselspital, University of Bern (Bern, Switzerland) [25]. Use of this data set was approved by the local ethics commission, specifically granting the use of archival tissue for molecular and immunohistochemical analysis as well as tissue microarray construction (University of Bern, Switzerland, no.…”

Section: Ethics Statement and Patient Cohortsmentioning

confidence: 99%

“…The DACHS study was approved by the ethics committees of the University of Heidelberg and of the Medical Chambers of Baden-Württemberg and Rhineland-Palatinate, and all participants signed an informed consent. The BERN dataset is a single-center dataset collected from clinical routine samples at the pathology archive at Inselspital, University of Bern (Bern, Switzerland) [25]. Use of this data set was approved by the local ethics commission, specifically granting the use of archival tissue for molecular and immunohistochemical analysis as well as tissue microarray construction (University of Bern, Switzerland, no.…”

Section: Ethics Statement and Patient Cohortsmentioning

confidence: 99%

Benchmarking artificial intelligence methods for end-to-end computational pathology

Laleh

Muti

Loeffler

et al. 2021

Preprint

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Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet- and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.

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Preservation of Epstein–Barr virus status and mismatch repair protein status along the metastatic course of gastric cancer

Cited by 15 publications

References 21 publications

Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach

Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Benchmarking artificial intelligence methods for end-to-end computational pathology

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