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Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer

腺癌 微卫星不稳定性 肿瘤科 内科学 医学 癌症 临床意义 阶段(地层学) 生物信息学 生物 基因 遗传学 微卫星 古生物学 等位基因
作者
Henry Walch,Raktim Borpatragohain,Justin Jee,Walid K. 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 D. Gray,David R. Jones,Michael F. Berger,Nikolaus Schultz
出处
期刊:Clinical Cancer Research [American Association for Cancer Research]
卷期号:31 (10): 1912-1921 被引量:1
标识
DOI:10.1158/1078-0432.ccr-24-3473
摘要

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 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 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.
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