Consensus molecular subtypes and orphan non-coding RNAs in colorectal cancer.

Abstract

e15651 Background: Colorectal cancer (CRC) is a highly heterogeneous disease with variable response to different treatment strategies. The consensus molecular subtype (CMS) classification system has been proposed as a gene expression-based framework aimed at capturing the genetic and molecular heterogeneity in CRCs and potentially stratifying patients into clinically relevant subgroups for treatment selection. We previously demonstrated that orphan non-coding RNAs (oncRNAs), a novel class of cancer enriched small non-coding RNAs (smRNA), exhibit cancer-specific expression. We also showed that oncRNAs can be used in a liquid biopsy assay for accurate early-stage detection of CRCs. We hypothesize that oncRNAs may be informative of CRC subtype-specific transcriptional signatures as defined by the CMS groups. Methods: To assess the effectiveness of oncRNAs for categorization of CMS group, we examined smRNA-seq data from The Cancer Genome Atlas (TCGA) for colon and rectal adenocarcinoma tumors ( n= 504), consisting of 77 CMS1, 216 CMS2, 71 CMS3, and 140 CMS4 tumors. CRC-specific oncRNA expression profiles were generated for all tumors in the TCGA cohort. We then trained a machine learning model on oncRNA expression profiles to predict CMS groups using a 5-fold cross validation setup. For our validation cohort, we applied our model to an independent CRC cohort ( n= 348) consisting of 56 CMS1, 101 CMS2, 59 CMS3, and 132 CMS4 CRC samples. Final CMS predictions for the validation cohort were made by averaging across the five training models. Results: Within TCGA testing folds, the model micro-averaged ROC-AUC for CMS classifications were 0.879 (95% CI: 0.86–0.90). In the validation cohort, micro-averaged AUC remained high at 0.915 (0.90–0.93). Sensitivities and AUCs for each subtype are provided in Table 1. The highest model-predicted likelihood for each CMS subtype had an accuracy of 73.6% (68.6%–78.1%), and the two most probable predictions had an accuracy of 91.1% (87.6%–93.9%). Conclusions: This study demonstrates the utility of oncRNA expression profiling to predict CMS for colorectal cancer. The generalizability of our machine learning model suggests that oncRNAs are informative of the biological differences across distinct CMS groups and may be used as a proxy for gene expression signatures to stratify colorectal cancers. Because smRNAs are more likely to be secreted into blood, we posit that an oncRNA-based liquid biopsy may open new opportunities to monitor patients and track changes in their tumor biology in real-time throughout their course of disease and treatment. [Table: see text]