In light of the marked differences in the intrinsic biological underpinnings and prognostic outcomes among different subtypes, Consensus Molecular Subtype (CMS) classification provides a new taxonomy of colorectal cancer (CRC) solely based on transcriptomics data and has been accepted as a standard rule for CRC stratification. Even though CMS was built on highly cancer relevant features, it suffers from limitations in capturing the promiscuous mechanisms in a clinical setting. There are at least two facts about using transcriptomic data for prognosis prediction: the engagement of genes or pathways that execute the clinical response pathway are highly dynamic and interactive with others; and a predefined patient stratification not only largely decrease the statistical analysis power, but also excludes the fact that clusters of patients that confer similar clinical outcomes may or may not overlap with a pre-defined subgrouping. To enable a flexible and prospective stratified exploration, we here present a novel computational framework based on bi-clustering aiming to identify gene regulatory mechanisms associated with various biological, clinical and drug-resistance features, with full recognition of the transiency of transcriptional regulation and complicacies of patients subgrouping with regards to different biological and clinical settings. Our analysis on multiple large scale CRC transcriptomics data sets using a bi-clustering based formulation suggests that the detected local low rank modules can not only generate new biological understanding coherent to CMS stratification, but also identify predictive markers for prognosis that are general to CRC or CMS dependent, as well as novel alternative drug resistance mechanisms. Our key results include: (1) a comprehensive annotation of the local low rank module landscape of CRC; (2) a mechanistic relationship between different clinical subtypes and outcomes, as well as their characteristic biological underpinnings, visible through a novel consensus map; and (3) a few (novel) resistance mechanisms of Oxaliplatin, 5-Fluorouracil, and the FOLFOX therapy are revealed, some of which are validated on independent datasets.

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