Henry S. Walch, Raktim Borpatragohain, Justin Jee, Waleed Chatila, Christopher Fong, Steven B. Maron, Geoffrey Y. Ku, David H. Ilson, Yelena Y. Janjigian, Abraham J. Wu, Pari Shah, Daniel G. Coit, Manjit S. Bains, Valerie W. Rusch, Bernard J. Park, Matthew J. Bott, Katherine Gray, David R. Jones, Michael Berger, Nikolaus Schultz, Vivian E. Strong, Daniela Molena, Smita Sihag
{"title":"肿瘤基因组图谱分子分类系统在食管胃癌中的临床意义","authors":"Henry S. Walch, Raktim Borpatragohain, Justin Jee, Waleed Chatila, Christopher Fong, Steven B. Maron, Geoffrey Y. Ku, David H. Ilson, Yelena Y. Janjigian, Abraham J. Wu, Pari Shah, Daniel G. Coit, Manjit S. Bains, Valerie W. Rusch, Bernard J. Park, Matthew J. Bott, Katherine Gray, David R. Jones, Michael Berger, Nikolaus Schultz, Vivian E. Strong, Daniela Molena, Smita Sihag","doi":"10.1158/1078-0432.ccr-24-3473","DOIUrl":null,"url":null,"abstract":"Purpose: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein–Barr virus (EBV)–associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system. Experimental Design: We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering–IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data. Results: Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P < 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location. Conclusions: Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.","PeriodicalId":10279,"journal":{"name":"Clinical Cancer Research","volume":"72 1","pages":""},"PeriodicalIF":10.0000,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer\",\"authors\":\"Henry S. Walch, Raktim Borpatragohain, Justin Jee, Waleed Chatila, Christopher Fong, Steven B. Maron, Geoffrey Y. Ku, David H. Ilson, Yelena Y. Janjigian, Abraham J. Wu, Pari Shah, Daniel G. Coit, Manjit S. Bains, Valerie W. Rusch, Bernard J. Park, Matthew J. Bott, Katherine Gray, David R. Jones, Michael Berger, Nikolaus Schultz, Vivian E. Strong, Daniela Molena, Smita Sihag\",\"doi\":\"10.1158/1078-0432.ccr-24-3473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein–Barr virus (EBV)–associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system. Experimental Design: We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering–IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data. Results: Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P < 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location. Conclusions: Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.\",\"PeriodicalId\":10279,\"journal\":{\"name\":\"Clinical Cancer Research\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":10.0000,\"publicationDate\":\"2025-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/1078-0432.ccr-24-3473\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1078-0432.ccr-24-3473","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer
Purpose: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein–Barr virus (EBV)–associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system. Experimental Design: We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering–IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data. Results: Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P < 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location. Conclusions: Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.
期刊介绍:
Clinical Cancer Research is a journal focusing on groundbreaking research in cancer, specifically in the areas where the laboratory and the clinic intersect. Our primary interest lies in clinical trials that investigate novel treatments, accompanied by research on pharmacology, molecular alterations, and biomarkers that can predict response or resistance to these treatments. Furthermore, we prioritize laboratory and animal studies that explore new drugs and targeted agents with the potential to advance to clinical trials. We also encourage research on targetable mechanisms of cancer development, progression, and metastasis.