A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

• Published in: Life science alliance Vol. 7; no. 8; p. e202402730
• Main Authors: de Back, Tim R, Wu, Tan, Schafrat, Pascale JM, ten Hoorn, Sanne, Tan, Miaomiao, He, Lingli, van Hooff, Sander R, Koster, Jan, Nijman, Lisanne E, Vink, Geraldine R, Beumer, Inès J, Elbers, Clara C, Lenos, Kristiaan J, Sommeijer, Dirkje W, Wang, Xin, Vermeulen, Louis
• Format: Journal Article
• Language: English
• Published: United States 01.08.2024
• Subjects: Aged; Biomarkers, Tumor - genetics; Colorectal Neoplasms - classification; Colorectal Neoplasms - genetics; Colorectal Neoplasms - pathology; Consensus; ErbB Receptors - genetics; ErbB Receptors - metabolism; Female; Formaldehyde; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Humans; Male; Middle Aged; Paraffin Embedding; Prognosis; Tissue Fixation - methods
• ISSN: 2575-1077; 2575-1077
• DOI: 10.26508/lsa.202402730

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24–2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10–27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.