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Lee HS. Spatial and Temporal Tumor Heterogeneity in Gastric Cancer: Discordance of Predictive Biomarkers. J Gastric Cancer 2025; 25:192-209. [PMID: 39822175 PMCID: PMC11739643 DOI: 10.5230/jgc.2025.25.e3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Accepted: 12/09/2024] [Indexed: 01/19/2025] Open
Abstract
Gastric cancer (GC) is a highly heterogeneous disease that varies in both histological presentation and genetic characteristics. Recent advances in the treatment of metastatic and unresectable GC have made several biomarker tests essential for patient management. Predictive biomarkers such as human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1), mismatch-repair (MMR) proteins, claudin 18.2, and fibroblast growth factor receptor 2b (FGFR2b) are commonly evaluated using immunohistochemistry. However, the expression levels of these biomarkers may vary across different tumor areas, and the accuracy of biomarker diagnosis can be affected by sample quantity, sample location, and collection method. Therefore, tumor heterogeneity presents substantial challenges for accurate biomarker-based diagnosis and prediction of therapeutic responses. Tumor heterogeneity can be categorized into spatial heterogeneity, which refers to variations within the primary tumor (intra-tumoral) or between primary and metastatic sites, and temporal heterogeneity, which encompasses changes over time. This review addresses the tumor heterogeneity in predictive biomarker expression in GC, focusing on HER2, PD-L1, MMR, the Epstein-Barr virus, claudin 18.2, and FGFR2b.
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Affiliation(s)
- Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
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2
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Morgagni P, Bencivenga M, Carneiro F, Cascinu S, Derks S, Di Bartolomeo M, Donohoe C, Eveno C, Gisbertz S, Grimminger P, Gockel I, Grabsch H, Kassab P, Langer R, Lonardi S, Maltoni M, Markar S, Moehler M, Marrelli D, Mazzei MA, Melisi D, Milandri C, Moenig PS, Mostert B, Mura G, Polkowski W, Reynolds J, Saragoni L, Van Berge Henegouwen MI, Van Hillegersberg R, Vieth M, Verlato G, Torroni L, Wijnhoven B, Tiberio GAM, Yang HK, Roviello F, de Manzoni G. International consensus on the management of metastatic gastric cancer: step by step in the foggy landscape : Bertinoro Workshop, November 2022. Gastric Cancer 2024; 27:649-671. [PMID: 38634954 PMCID: PMC11193703 DOI: 10.1007/s10120-024-01479-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/05/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Many gastric cancer patients in Western countries are diagnosed as metastatic with a median overall survival of less than twelve months using standard chemotherapy. Innovative treatments, like targeted therapy or immunotherapy, have recently proved to ameliorate prognosis, but a general agreement on managing oligometastatic disease has yet to be achieved. An international multi-disciplinary workshop was held in Bertinoro, Italy, in November 2022 to verify whether achieving a consensus on at least some topics was possible. METHODS A two-round Delphi process was carried out, where participants were asked to answer 32 multiple-choice questions about CT, laparoscopic staging and biomarkers, systemic treatment for different localization, role and indication of palliative care. Consensus was established with at least a 67% agreement. RESULTS The assembly agreed to define oligometastases as a "dynamic" disease which either regresses or remains stable in response to systemic treatment. In addition, the definition of oligometastases was restricted to the following sites: para-aortic nodal stations, liver, lung, and peritoneum, excluding bones. In detail, the following conditions should be considered as oligometastases: involvement of para-aortic stations, in particular 16a2 or 16b1; up to three technically resectable liver metastases; three unilateral or two bilateral lung metastases; peritoneal carcinomatosis with PCI ≤ 6. No consensus was achieved on how to classify positive cytology, which was considered as oligometastatic by 55% of participants only if converted to negative after chemotherapy. CONCLUSION As assessed at the time of diagnosis, surgical treatment of oligometastases should aim at R0 curativity on the entire disease volume, including both the primary tumor and its metastases. Conversion surgery was defined as surgery on the residual volume of disease, which was initially not resectable for technical and/or oncological reasons but nevertheless responded to first-line treatment.
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Affiliation(s)
- Paolo Morgagni
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Maria Bencivenga
- General and Upper GI Surgery, Department of Surgery, University Hospital Verona, University of Verona, Verona, Italy.
| | - Fatima Carneiro
- Department of Pathology, Centro Hospitalar de São João, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
| | - Stefano Cascinu
- Department of Medical Oncology, Comprehensive Cancer Center, Università Vita-Salute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Sarah Derks
- Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria Di Bartolomeo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claire Donohoe
- Medicinal Chemistry, Trinity Translational Medicine Institute, Trinity Centre for Health Sciences, Trinity College Dublin, The University of Dublin, St. James's Hospital, Dublin 8, Ireland
| | - Clarisse Eveno
- Department of Digestive and Oncologic Surgery, Claude Huriez University Hospital, Centre Hospitalier Universitaire (CHU) Lille, Université de Lille, Lille, France
| | - Suzanne Gisbertz
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Grimminger
- Department of General, Visceral and Transplant Surgery, University Medical Center, University of Mainz, Mainz, Germany
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Heike 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
| | - Paulo Kassab
- Gastric Surgery Division, BP Gastric Surgery Department, Santa Casa Medical School, São Paulo, Brazil
| | - Rupert Langer
- Institute of Pathology and Microbiology, Johannes Kepler University Linz, Altenberger Strasse 69, 4040, Linz, Austria
| | - Sara Lonardi
- Istituto Oncologico Veneto IOV-IRCCS, Padua, Italy
| | - Marco Maltoni
- Unit of Palliative Care, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Forlì-Cesena, Italy
| | - Sheraz Markar
- Surgical Interventional Trials Unit, University of Oxford, Oxford, UK
| | - Markus Moehler
- Department of Medicine, Johannes-Gutenberg University Clinic, Mainz, Germany
| | - Daniele Marrelli
- Unit of General Surgery and Surgical Oncology, Department of Medicine Surgery and Neurosciences, University of Siena, 53100, Siena, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, University of Siena, 53100, Siena, Italy
| | - Davide Melisi
- Medical Oncology at the Department of Medicine, University of Verona, Verona, Italy
| | - Carlo Milandri
- Department of Oncology, San Donato Hospital, 52100, Arezzo, Italy
| | | | - Bianca Mostert
- Department of Medical Oncology, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Gianni Mura
- Department of Surgery, San Donato Hospital, Arezzo, Italy
| | - Wojciech Polkowski
- Department of Surgical Oncology, Medical University of Lublin, Radziwiłłowska 13 St, 20-080, Lublin, Poland
| | | | - Luca Saragoni
- Pathology Unit, Santa Maria delle Croci Ravenna Hospital, Ravenna, Italy
| | - Mark I Van Berge Henegouwen
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | - Giuseppe Verlato
- Department of Diagnostics and Public Health, Section of Epidemiology and Medical Statistics, University of Verona, Verona, Italy
| | - Lorena Torroni
- Department of Diagnostics and Public Health, Section of Epidemiology and Medical Statistics, University of Verona, Verona, Italy
| | - Bas Wijnhoven
- Department of Surgery, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
| | | | - Han-Kwang Yang
- Surgical Department, SNUH National Cancer Center, Seoul, Korea
| | - Franco Roviello
- Unit of General Surgery and Surgical Oncology, Department of Medicine Surgery and Neurosciences, University of Siena, 53100, Siena, Italy
| | - Giovanni de Manzoni
- General and Upper GI Surgery, Department of Surgery, University Hospital Verona, University of Verona, Verona, Italy
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Talari FF, Bozorg A, Zeinali S, Zali M, Mohsenifar Z, Asadzadeh Aghdaei H, Baghaei K. Low incidence of microsatellite instability in gastric cancers and its association with the clinicopathological characteristics: a comparative study. Sci Rep 2023; 13:21743. [PMID: 38065969 PMCID: PMC10709324 DOI: 10.1038/s41598-023-48157-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Gastric cancer is a complex heterogeneous disease with different molecular subtypes that have clinical implications. It is characterized by high mortality rates and limited effective therapies. Microsatellite instability (MSI) has been recognized as a subgroup with a good prognosis based on TCGA and ACRG categorizations. Besides its prognostic and predictive value, gastric cancers with high MSI exhibit different clinical behaviors. The prevalence of high MSI has been assessed in gastric cancer worldwide, especially in East Asia, but there is a lack of such information in the Middle East. Therefore, this study aimed to investigate the incidence and status of MSI in Iranian gastric cancer patients using 53 samples collected from 2015 to 2020 at Taleghani Hospital Medical Center. DNA from tumoral and normal tissues were extracted and assessed through multiplex-PCR based on five mononucleotide repeats panel. Clinicopathological variables, including age, sex, Lauren classification, lymph node involvement, TNM stage, differentiation, localization, and tumor size, were also analyzed. With 2 males and 2 females, high microsatellite instability represented a small subgroup of almost 7.5% of the samples with a median age of 60.5 years. High microsatellite instability phenotypes were significantly associated with patients aged 68 years and older (p‑value of 0.0015) and lower lymph node involvement (p‑value of 0.0004). Microsatellite instability was also more frequent in females, with distal gastric location, bigger tumor size, and in the intestinal type of gastric cancer rather than the diffuse type.
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Affiliation(s)
| | - Ali Bozorg
- Biotechnology Department, College of Science, University of Tehran, Tehran, Iran.
| | - Sirous Zeinali
- Dr. Zeinali's Medical Genetics Laboratory, Kawsar Human Genetics Research Center, Tehran, Iran
- Department of Molecular Medicine, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Mohammadreza Zali
- Research Institute for Gastroenterology and Liver Diseases, Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zhale Mohsenifar
- Department of Pathology, School of Medicine, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Asadzadeh Aghdaei
- Research Institute for Gastroenterology and Liver Diseases, Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kaveh Baghaei
- Research Institute for Gastroenterology and Liver Diseases, Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Liu DHW, Grabsch HI, Gloor B, Langer R, Dislich B. Programmed death-ligand 1 (PD-L1) expression in primary gastric adenocarcinoma and matched metastases. J Cancer Res Clin Oncol 2023; 149:13345-13352. [PMID: 37491637 PMCID: PMC10587283 DOI: 10.1007/s00432-023-05142-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND Combination of immunotherapy and chemotherapy is recommended for first line treatment of gastric adenocarcinoma (GC) patients with locally advanced unresectable disease or metastatic disease. However, data regarding the concordance rate between PD-L1 combined positive score (CPS) in primary GC and matched regional lymph node metastasis (LNmet) or matched distant metastasis (Dmet) is limited. METHODS Tissue microarray sections from primary resected GC, LNmet and Dmet were immunohistochemically stained with anti-PD-L1 (clone SP263). PD-L1 expression was scored separately in tumour cells and immune cells and compared between matched primary GC, LNmet and/or Dmet. CPS was calculated and results for CPS cut-offs 1 and 5 were compared between matched samples. RESULTS 275 PD-L1 stained GC were analysed. 189 primary GC had matched LNmet. CPS cut-off 1 concordance rate between primary GC and LNmet was 77%. 23 primary GC had matched Dmet but no matched LNmet, CPS cut-off 1 concordance rate was 70%. 63 primary GC had both matched LNmet and matched Dmet, CPS cut-off 1 concordance rate of 67%. CPS cut-off 5 results were similar. The proportion of PD-L1 positive tumour cells increased from primary GC (26%) to LNmet (42%) and was highest in Dmet (75%). CONCLUSION Our study showed up to 33% discordance of PD-L1 CPS between primary GC and LNmet and/or Dmet suggesting that multiple biopsies of primary GC and metastatic sites might need to be tested before considering treatment options. Moreover, this is the first study that seems to suggest that tumour cells acquire PD-L1 expression during disease progression.
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Affiliation(s)
- Drolaiz H W Liu
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital and Johannes Kepler University, Krankenhausstraße 9, 4021, Linz, Austria
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Beat Gloor
- Department of Visceral Surgery and Medicine, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Rupert Langer
- Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital and Johannes Kepler University, Krankenhausstraße 9, 4021, Linz, Austria.
| | - Bastian Dislich
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
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Hirabayashi M, Georges D, Clifford GM, de Martel C. Estimating the Global Burden of Epstein-Barr Virus-Associated Gastric Cancer: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol 2023; 21:922-930.e21. [PMID: 35963539 DOI: 10.1016/j.cgh.2022.07.042] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Evidence suggests that a fraction of new gastric cancer cases may be etiologically associated with Epstein-Barr virus (EBV), a known carcinogenic agent. We aimed to systematically explore the proportion of EBV-positive gastric cancer. METHODS We did a systematic review (PROSPERO CRD42020164473) from January 1990 to August 2021. For each country and geographical region with available data, pooled prevalence and corresponding 95% confidence intervals (CIs) of EBV in gastric tumors were calculated for 3 subtypes of gastric adenocarcinoma (conventional adenocarcinoma, lymphoepithelioma-like gastric carcinoma, and remnant/stump carcinoma). For conventional adenocarcinoma, prevalence ratios (PRs) were presented for sex, Lauren's classification, gastric cancer stage, and anatomical location of the stomach. RESULTS In 220 eligible studies including over 68,000 cases of conventional gastric adenocarcinoma, EBV prevalence in tumor cells was 7.5% (95% CI, 6.9%-8.1%) and was higher in men compared with women (PR, 2.1; 95% CI, 1.9-2.4), in diffuse type compared with intestinal type (PR, 1.3; 95% CI, 1.1-1.5), and in the proximal region compared with the distal region (PR, 2.5; 95% CI, 2.0-3.1). There was no difference in EBV prevalence by gastric cancer stage. EBV prevalence was 75.9% (95% CI, 62.8%-85.5%) among lymphoepithelioma-like gastric carcinoma and 26.3% (95% CI, 22.2%-32.0%) among remnant or stump carcinoma. CONCLUSIONS Assuming a causal association between EBV and gastric cancer, our findings, when applied to the GLOBOCAN 2020 gastric cancer incidence, suggest that primary prevention such as the development of an effective EBV vaccine might prevent 81,000 EBV-associated gastric cancer cases worldwide annually.
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Affiliation(s)
- Mayo Hirabayashi
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Damien Georges
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Gary M Clifford
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Catherine de Martel
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France.
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Zhang LH, Zhuo HQ, Hou JJ, Zhou Y, Cheng J, Cai JC. Proteomic signatures of infiltrative gastric cancer by proteomic and bioinformatic analysis. World J Gastrointest Oncol 2022; 14:2097-2107. [PMID: 36438703 PMCID: PMC9694269 DOI: 10.4251/wjgo.v14.i11.2097] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/16/2022] [Accepted: 10/18/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Proteomic signatures of Ming's infiltrative gastric cancer (IGC) remain unknown.
AIM To elucidate the molecular characteristics of IGC at the proteomics level.
METHODS Twelve pairs of IGC and adjacent normal tissues were collected and their proteomes were analyzed by high performance liquid chromatography tandem mass spectrometry. The identified peptides were sequenced de novo and matched against the SwissProt database using Maxquant software. The differentially expressed proteins (DEPs) were screened using |log2(Fold change)| > 1 and P-adj < 0.01 as the thresholds. The expression levels of selected proteins were verified by Western blotting. The interaction network of the DEPs was constructed with the STRING database and visualized using Cytoscape with cytoHubba software. The DEPs were functionally annotated using clusterProfiler, STRING and DAVID for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. P < 0.05 was considered statistically significant.
RESULTS A total of 7361 DEPs were identified, of which 94 were significantly up-regulated and 223 were significantly down-regulated in IGC relative to normal gastric tissues. The top 10 up-regulated proteins were MRTO4, BOP1, PES1, WDR12, BRIX1, NOP2, POLR1C, NOC2L, MYBBP1A and TSR1, and the top 10 down-regulated proteins were NDUFS8, NDUFS6, NDUFA8, NDUFA5, NDUFC2, NDUFB8, NDUFB5, NDUFB9, UQCRC2 and UQCRC1. The up-regulated proteins were enriched for 9 biological processes including DNA replication, ribosome biogenesis and initiation of DNA replication, and the cellular component MCM complex. Among the down-regulated proteins, 17 biological processes were enriched, including glucose metabolism, pyruvic acid metabolism and fatty acid β-oxidation. In addition, the mitochondrial inner membrane, mitochondrial matrix and mitochondrial proton transport ATP synthase complex were among the 6 enriched cellular components, and 11 molecular functions including reduced nicotinamide adenine dinucleotide dehydrogenase activity, acyl-CoA dehydrogenase activity and nicotinamide adenine dinucleotide binding were also enriched. The significant KEGG pathways for the up-regulated proteins were DNA replication, cell cycle and mismatch repair, whereas 18 pathways including oxidative phosphorylation, fatty acid degradation and phenylalanine metabolism were significantly enriched among the down-regulated proteins.
CONCLUSION The proteins involved in cell cycle regulation, DNA replication and mismatch repair, and metabolism were significantly altered in IGC, and the proteomic profile may enable the discovery of novel biomarkers.
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Affiliation(s)
- Li-Hua Zhang
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Institute of Gastrointestinal Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
| | - Hui-Qin Zhuo
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen 361004, Fujian Province, China
| | - Jing-Jing Hou
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen 361004, Fujian Province, China
| | - Yang Zhou
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Institute of Gastrointestinal Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
| | - Jia Cheng
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen 361004, Fujian Province, China
| | - Jian-Chun Cai
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Institute of Gastrointestinal Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, Fujian Province, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen 361004, Fujian Province, China
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Ghaffari Laleh N, Truhn D, Veldhuizen GP, Han T, van Treeck M, Buelow RD, Langer R, Dislich B, Boor P, Schulz V, Kather JN. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun 2022; 13:5711. [PMID: 36175413 PMCID: PMC9522657 DOI: 10.1038/s41467-022-33266-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks. Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.
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Affiliation(s)
- Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Gregory Patrick Veldhuizen
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Tianyu Han
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Marko van Treeck
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Roman D Buelow
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Rupert Langer
- Institute of Pathology, University of Bern, Bern, Switzerland.,Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
| | - Bastian Dislich
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Peter Boor
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany. .,Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany. .,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. .,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. .,Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
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Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-Claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med Image Anal 2022; 79:102474. [DOI: 10.1016/j.media.2022.102474] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 04/07/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
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9
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Dislich B, Mertz KD, Gloor B, Langer R. Interspatial Distribution of Tumor and Immune Cells in Correlation with PD-L1 in Molecular Subtypes of Gastric Cancers. Cancers (Basel) 2022; 14:cancers14071736. [PMID: 35406506 PMCID: PMC8996833 DOI: 10.3390/cancers14071736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022] Open
Abstract
(1) Background: EBV-positive and mismatch repair-deficient (MMRd) gastric cancers (GCs) show higher levels of tumor-infiltrating lymphocytes (TILs) and PD-L1 expression and thus a more profound response to immunotherapy. However, the majority of GCs are EBV-negative (EBV−) and MMR proficient (MMRp). We analyzed PD-L1 expression and TILs in EBV-MMRpGCs in comparison to EBV-positive (EBV+) and MMRdGCs to identify an immunogenic phenotype susceptible to immunotherapy. (2) Methods: A next-generation tissue microarray of 409 primary resected GCs was analyzed by Epstein-Barr encoding region (EBER) in situ hybridization for MSH1, PMS2, MSH2, MSH6, PD-L1, and CD8 immunohistochemistry. PD-L1 positivity was defined as a combined positive score (CPS) of ≥1. CD8+ TILs and their proximity to cancer cells were digitally analyzed on the HALO™ image analysis platform. (3) Results: Eleven cases were EBV+, 49 cases MMRd, and 349 cases EBV-MMRpGCs. The highest rate of PD-L1 positivity was seen in EBV+GCs, followed by MMRdGCs and EBV-MMRpGCs (81.8%, 73.5%, and 27.8%, respectively). EBV+ and MMRdGCs also demonstrated increased numbers and proximity of CD8+ TILs to tumor cells compared to EBV-MMRpGCs (p < 0.001 each). PD-L1 status positively correlated with the total numbers of CD8+ TILs and their proximity to tumor cells in all subtypes, including EBV-MMRpGCs (p < 0.001 each). A total of 28.4% of EBV-MMRpGCs showed high CD8+ TILs independent of PD-L1. (4) Conclusions: PD-L1 and CD8 immunohistochemistry, supplemented by digital image analysis, may identify EBV-MMRpGCs with high immunoreactivity indices, indicating susceptibility to immunotherapy.
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Affiliation(s)
- Bastian Dislich
- Institute of Pathology, University of Bern, 3008 Bern, Switzerland
- Correspondence:
| | - Kirsten D. Mertz
- Institute of Pathology, Cantonal Hospital Baselland, 4410 Liestal, Switzerland;
| | - Beat Gloor
- Department of Visceral Surgery and Medicine, Inselspital Bern, University of Bern, 3010 Bern, Switzerland;
| | - Rupert Langer
- Institute of Clinical Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University, 4021 Linz, Austria;
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Muti HS, Heij LR, Keller G, Kohlruss M, Langer R, Dislich B, Cheong JH, Kim YW, Kim H, Kook MC, Cunningham D, Allum WH, Langley RE, Nankivell MG, Quirke P, Hayden JD, West NP, Irvine AJ, Yoshikawa T, Oshima T, Huss R, Grosser B, Roviello F, d'Ignazio A, Quaas A, Alakus H, Tan X, Pearson AT, Luedde T, Ebert MP, Jäger D, Trautwein C, Gaisa NT, Grabsch HI, Kather JN. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. Lancet Digit Health 2021; 3:e654-e664. [PMID: 34417147 PMCID: PMC8460994 DOI: 10.1016/s2589-7500(21)00133-3] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/20/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023]
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 0·5. 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 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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Affiliation(s)
- Hannah Sophie Muti
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Lara Rosaline Heij
- Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Gisela Keller
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Meike Kohlruss
- Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Rupert Langer
- Institute of Pathology, Inselspital, University of Bern, Switzerland; Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
| | - Bastian Dislich
- Institute of Pathology, Inselspital, University of Bern, Switzerland
| | - Jae-Ho Cheong
- Department of Surgery, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
| | - Young-Woo Kim
- Center for Gastric Cancer, National Cancer Center, Goyang, South Korea
| | - Hyunki Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
| | - Myeong-Cherl Kook
- Department of Pathology, National Cancer Center, Goyang, South Korea
| | - David Cunningham
- Department of Medicine, Gastrointestinal and Lymphoma Units, The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Ruth E Langley
- Medical Research Council Clinical Trials Unit, University College London, London, UK
| | - Matthew G Nankivell
- Medical Research Council Clinical Trials Unit, University College London, London, UK
| | - Philip Quirke
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Jeremy D Hayden
- Department of Oesophago-Gastric Surgery, St James's University Hospital, Leeds, UK
| | - Nicholas P West
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Andrew J Irvine
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Takaki Yoshikawa
- Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Takashi Oshima
- Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Ralf Huss
- Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany
| | - Bianca Grosser
- Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany
| | - Franco Roviello
- Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Italy
| | - Alessia d'Ignazio
- Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Italy
| | - Alexander Quaas
- Institute of Pathology, University Hospital Cologne, Cologne, Germany
| | - Hakan Alakus
- Department of General, Visceral, Cancer and Transplantation Surgery, University Hospital Cologne, Cologne, Germany
| | - Xiuxiang Tan
- Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Tom Luedde
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Düsseldorf, Germany
| | - Matthias P Ebert
- Department of Medicine II, Mannheim Institute for Innate Immunoscience and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Christian Trautwein
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Heike I Grabsch
- 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, Netherlands
| | - 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, UK; Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
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Daun T, Nienhold R, Paasinen-Sohns A, Frank A, Sachs M, Zlobec I, Cathomas G. Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach. Cancers (Basel) 2021; 13:cancers13153722. [PMID: 34359622 PMCID: PMC8345215 DOI: 10.3390/cancers13153722] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/12/2021] [Accepted: 07/19/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary In this study, we present a simple but comprehensive molecular analysis of gastric carcinoma. The two major existing classification schemes show some discrepancies and are highly technically demanding, which makes them hardly feasible in daily diagnostic routines. Our workflow is based on simple and commercially available technology and provides a potential consensus approach by integrating the two major classification schemes. Furthermore, our approach allows the molecular subtypes to be assigned to different prognostic groups. We are convinced that our approach may help to better understand the molecular mechanisms of this worldwide health burden and that it could pave the way for new therapeutic targets. Abstract 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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Affiliation(s)
- Till Daun
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
| | - Ronny Nienhold
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
| | - Aino Paasinen-Sohns
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
| | - Angela Frank
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
| | - Melanie Sachs
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
| | - Inti Zlobec
- Institute of Pathology, University of Bern, 3008 Bern, Switzerland;
| | - Gieri Cathomas
- Institute of Pathology, Cantonal Hospital Basel-Land, 4410 Liestal, Switzerland; (T.D.); (R.N.); (A.P.-S.); (A.F.); (M.S.)
- Correspondence: ; Tel.: +41-61-925-2622
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