Mattesen, Trine B.Rasmussen, Mads H.Sandoval, JuanOngen, HalitÁrnadóttir, Sigrid S.Gladov, JosephineMartínez Cardús, AnnaCastro de Moura, ManuelMadsen, Anders H.Laurberg, SørenDermitzakis, Emmanouil T.Esteller, Manel, 1968-Andersen, Claus L.Bramsen, Jesper B.2021-07-052021-07-052020-04-242041-1723https://hdl.handle.net/2445/178783Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their methylation profiles. MethCORR is here applied to methylation profiles from 877 fresh-frozen/FFPE samples and comparative analysis identifies the same two subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicts relapse-free survival (HR = 2.66, 95%CI [1.67-4.22], P value < 0.001 (log-rank test)) than UICC tumor, node, metastasis (TNM) staging and microsatellite instability status are identified and validated using DNA methylation-specific PCR. The MethCORR approach is general, and may be similarly successful for other cancer types.15 p.application/pdfengcc-by (c) Mattesen, Trine B. et al., 2020https://creativecommons.org/licenses/by/4.0/Càncer colorectalGenètica quantitativaPronòstic mèdicColorectal cancerQuantitative geneticsPrognosisinfo:eu-repo/semantics/article7003692021-07-05info:eu-repo/semantics/openAccess32332866