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A novel mesenchymal‐associated transcriptomic signature for risk‐stratification and therapeutic response prediction in colorectal cancer
Author(s) -
Matsuyama Takatoshi,
Kandimalla Raju,
Ishikawa Toshiaki,
Takahashi Naoki,
Yamada Yasuhide,
Yasuno Masamichi,
Kinugasa Yusuke,
Hansen Torben Frøstrup,
Fakih Marwan,
Uetake Hiroyuki,
Győrffy Balázs,
Goel Ajay
Publication year - 2020
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.33129
Subject(s) - oncology , medicine , colorectal cancer , hazard ratio , biomarker , cohort , biomarker discovery , proportional hazards model , risk stratification , gene signature , adjuvant therapy , cancer , bioinformatics , confidence interval , biology , gene , proteomics , gene expression , biochemistry
Risk stratification in Stage II and III colorectal cancer (CRC) patients is critical, as it allows patient selection for adjuvant chemotherapy. In view of the inadequacy of current clinicopathological features for risk‐stratification, we undertook a systematic and comprehensive biomarker discovery effort to develop a risk‐assessment signature in CRC patients. The biomarker discovery phase examined 853 CRC patients, and identified a gene signature for predicting recurrence‐free survival (RFS). This signature was validated in a meta‐analysis of 1212 patients from nine independent datasets, and its performance was compared against established prognostic signatures and consensus molecular subtypes (CMS). In addition, a risk‐prediction model was trained (n = 142), and subsequently validated in an independent clinical cohort (n = 286). As a result, this mesenchymal‐associated transcriptomic signature (MATS) identified high‐risk CRC patients with poor RFS in the discovery (hazard ratio [HR]: 1.79), and nine validation cohorts (HR: 1.86). In multivariate analysis, MATS was the most significant predictor of RFS compared to established prognostic signatures and CMS subtypes. Intriguingly, MATS robustly identified CMS4‐subtype in multiple CRC cohorts (AUC = 0.92‐0.99). In the two clinical cohorts, MATS stratified low and high‐risk groups with a 5‐year RFS in the training (HR: 4.11) and validation cohorts (HR: 2.55), as well as predicted response to adjuvant therapy in Stage II and III CRC patients. We report a novel prognostic and predictive biomarker signature in CRC, which is superior to currently used approaches and have the potential for clinical translation in near future.