Consensus Molecular Subtypes (CMS) Classification: a progress towards Subtype-Driven treatments in colorectal cancer

共识分子亚型(CMS)分类:结直肠癌亚型驱动治疗的进展

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Abstract

BACKGROUND: Colorectal cancer (CRC) is heterogeneous with varied molecular profiles, clinical outcomes, and treatment responses. The Consensus Molecular Subtypes (CMS) classification categorizes CRC into four molecular subtypes (CMS1-4) based on gene expression profiles, aiming to improve prognosis prediction and guide personalized therapy. OBJECTIVE: This paper reviews the CMS classification, its prognostic and predictive roles, methods of identification, association with polyps, immunotherapy applications, intratumoral heterogeneity (ITH), and barriers to clinical adoption. METHODS: The review synthesizes data from global studies on CMS, searches were conducted in PubMed, Scopus, and Web of Science, till June 2025, focusing on gene expression profiling, immunohistochemistry (IHC), and image-based CMS (imCMS) classifiers based on computational models (such deep learning). It examines CMS-specific treatment responses, immune profiles, and emerging strategies like single-cell RNA sequencing to address ITH. RESULTS: CMS classifies CRC into four subtypes: CMS1 (MSI-immune, ~ 15%), CMS2 (canonical, ~ 40%), CMS3 (metabolic, ~ 13%), and CMS4 (mesenchymal, ~ 22%). CMS is prognostic in adjuvant (e.g., PETACC-3, NSABP C-07) and metastatic settings (e.g., CALGB 80405, FIRE-3), with CMS2 linked to the best survival and CMS4 the worst outcomes. CMS1 responds to immunotherapy, CMS2/3 to bevacizumab, and CMS4 to irinotecan. Classification methods include gene expression profiling, immunohistochemistry (IHC), and image-based CMS (imCMS), but intratumoral heterogeneity (ITH) and technical barriers hinder clinical adoption. Solutions like single-cell sequencing and standardized assays are emerging. CONCLUSION: CMS classification enhances CRC prognosis and treatment personalization but faces challenges due to ITH and technical limitations. Advances in IHC, imCMS, and targeted gene panels may facilitate broader clinical adoption, improving patient outcomes through tailored therapies.

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