Abstract 466: A novel DNA methylation-based approach for molecular subtyping and improved prognostication of colorectal cancer using formalin-fixed and paraffin-embedded tissue

Abstract

Abstract Background: Colorectal cancer (CRC) is characterized by marked inter-tumor heterogeneity, both molecularly and clinically. Patient stratification using transcriptional subtyping show promise to resolve the heterogeneity and guide precision medicine. This requires high quality RNA, which can be purified from fresh frozen (FF) tissue, but not from clinically collected formalin-fixed paraffin-embedded (FFPE) tissue. Consequently, transcriptional subtyping is not applicable to most CRC patients. Aim: To establish a DNA methylation-based approach for molecular characterization and subtyping, which is compatible with both FF and FFPE tissue, and use this to improve patient prognostication as compared to histopathological TNM staging. Methods: Using paired RNA expression and DNA methylation profiles (450K array) from 394 CRCs, we identified 200 CpG sites genome-wide, whose methylation levels correlated best with expression for each gene. With this information, we 1) devised an approach, methCORR, that can infer RNA expression in both FF and FFPE samples using only DNA methylation profiles, 2) established a methCORR network that clusters genes according to overlap in the CpG sites associated with their expression level and 3) used the network to determine and characterize biological traits associated with tumor aggressiveness. Results: The methCORR-inferred RNA expression profiles in FF tissue consistently exhibited high correlation to matched RNA-seq. profiles (median Pearson r=0.97; range 0.91-0.98). We found a higher correlation between FF RNA-seq. and FFPE inferred RNA profiles (median r= 0.97; range 0.96-0.98) than between FF and FFPE RNA-seq. profiles (median r= 0.78; range 0.51-0.92). Gene expression profiles were inferred in two FF (n=231, n=203), and two FFPE cohorts (n=113, n=56). Clustering of these profiles identified the same two CRC subtypes independently in all cohorts. Characterization using the methCORR network showed that the two subtypes resembled conventional and serrated CRC. Moreover, methCORR network analysis identified subtype-specific traits that associated strongly with tumor aggressiveness, such as T-cell, fibroblast, and epithelial-mesenchymal transition activity, and thus allowed identification of subtype-specific prognostic biomarkers. These better predicted relapse-free survival (HR=3.22, 95%CI 2.00-5.17) than TNM staging (HR=1.99, 95%CI 1.28-3.08). Finally, we derived four simple and clinically applicable DNA methylation-specific PCR assays for subtyping and prognostication of CRC FFPE samples. Conclusion: We have developed a novel method for characterization, subtyping, and prognostication of CRC, which is compatible with FF and FFPE samples. We envision that application of the methCORR approach to other cancer types will generate similar fruitful results. Citation Format: Trine B. Mattesen, Mads H. Rasmussen, Juan Sandoval, Halit Ongen, Sigrid S. Árnadóttir, Anders H. Madsen, Søren Laurberg, Emmanouil T. Dermitzakis, Manel Esteller, Claus L. Andersen, Jesper B. Bramsen. A novel DNA methylation-based approach for molecular subtyping and improved prognostication of colorectal cancer using formalin-fixed and paraffin-embedded tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 466.