Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities

Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Similar to many other malignancies, CRC is a heterogeneous disease, making it a clinical challenge for optimization of treatment modalities in reducing the morbidity and mortality associated with this disease. A more precise understanding of the biological properties that distinguish patients with colorectal tumors, especially in terms of their clinical features, is a key requirement towards a more robust, targeted-drug design, and implementation of individualized therapies. In the recent decades, extensive studies have reported distinct CRC subtypes, with a mutation-centered view of tumor heterogeneity. However, more recently, the paradigm has shifted towards transcriptome-based classifications, represented by six independent CRC taxonomies. In 2015, the colorectal cancer subtyping consortium reported the identification of four consensus molecular subtypes (CMSs), providing thus far the most robust classification system for CRC. In this review, we summarize the historical timeline of CRC classification approaches; discuss their salient features and potential limitations that may require further refinement in near future. In other words, in spite of the recent encouraging progress, several major challenges prevent translation of molecular knowledge gleaned from CMSs into the clinic. Herein, we summarize some of these potential challenges and discuss exciting new opportunities currently emerging in related fields. We believe, close collaborations between basic researchers, bioinformaticians and clinicians are imperative for addressing these challenges, and eventually paving the path for CRC subtyping into routine clinical practice as we usher into the era of personalized medicine.

Keywords: Colorectal cancer; Heterogeneity; Molecular subtyping; Personalized medicine.

Figure 1. Six CRC subtyping systems derived from a single-omic classification workflow and two major strategies for integrative analysis. (a)

The workflow for six independent CRC subtyping systems based on single-omic classification strategy. The six CRC subtyping studies employed different training cohorts, gene expression profiling platforms, clustering and classification methods, yielding discrepant subtyping results. (b) We proposed a network-based approach for multi-platform (horizontal) integration, involving several major steps: (1) classifying 18 data sets totalling over 4000 samples using each of the six subtyping systems; (2) calculating a matrix of Jaccard indices quantifying the association between each pair of subtypes; (3) evaluating the statistical significance of the association between each pair of subtypes using hypergeometric tests; (4) filtering Jaccard indices by the p-values derived from hypergeometric tests to retain only significant associations (P < 0.001); (5) constructing a network of subtype associations; (6) partitioning the network into consensus molecular subgroups using Markov cluster algorithm (MCL) [82]. More technical details can be found in [9]. (c) Multi-omic (vertical) integration for cancer classification using similarity network fusion (SNF) [80]. Two or more types of omic data such as mRNA expression, miRNA expression, DNA methylation and copy number profiles can be integrated for more comprehensive dissection of cancer heterogeneity occurring at multiple omic levels of gene regulations.

Figure 2. The CRC consensus molecular subtyping system

Network-based meta-analysis on six representative classification systems identified four consensus molecular subtypes of CRC. Each subtype shows distinct molecular characteristics and clinical associations. CMS1 (MSI Immune) tumors are characterized by CIMP high status, BRAF V600E mutations, diffuse immune infiltration, and are associated with worse survival after relapse. CMS2 (canonical) tumors are characterized by high somatic copy number alterations (SCNAs), overrepresented APC mutations and activated WNT and MYC signaling pathways. CMS3 (metabolic) tumors are largely CIMP low, enriched for KRAS mutations, and characterized by deregulation of metabolic pathways. CMS4 (mesenchymal) tumors display a high level of SCNAs, and are characterized by upregulation of EMT, TGF-β activation, stromal infiltration and worse relapse-free and overall survival. CMS2 and CMS3 tumors are more likely to be developed from tubular adenomas, while CMS1 and CMS4 tumors are potentially derived from serrated adenomas [142].

Figure 3. A putative roadmap to more personalized CRC management based on molecular subtyping

Implementing CMS subtyping for more personalized clinical management of CRC patients involves four major phases: (1) Collection of pre- or post-surgical biopsies, surgical tissues or whole blood/serum/plasma from CRC patients to isolate DNA/RNA/miRNA and proteins to perform molecular profiling. (2) Performing transcriptomic as well as multi-omic profiling using various high and low throughput platforms that are currently available as well as the methods that are under development. (3) Stratifying patients by various biomarker assays tailored for specific clinical applications. More specifically, establishing a robust biomarker associated with CRC subtyping involves multiple stages: biomarker discovery, model development, inter-lab validation as well as validation by prospective studies. At each stage, critical assessments of the assay are needed before entering the next stage. (4) Implementation of subtyping in decision making of CRC to address various clinical questions throughout the CRC progression. Multiple choices of sample sources, molecules and profiling platforms are available, yet largely unexplored (colored in gray), for developing an optimized biomarker assay for a specific clinical application.

Figure 4

New challenges to clinical translation of CRC subtyping and emerging opportunities. Clockwise, from top to bottom to top are the six major challenges hampering the clinical translation of CRC subtyping as well as their corresponding new opportunities: (1) Transforming single-omic to multi-omic molecular subtyping by integrating other types of data such as DNA methylation and miRNA expression profiles; (2) Establishing more robust classifiers for cross-platform classification based on deep learning; (3) Elucidating subtype-specific regulatory mechanisms using a multi-dimensional network approach; (4) Linking inter-tumor and intra-tumor heterogeneity studies by single cell sequencing or computational deconvolution; (5) Developing clinically accessible assays using qPCR, IHC and NanoString to implement CMS taxonomy as routine clinical practice; (6) Evaluating the role of CMS as a predictive marker by integrative analysis of genomic, pharmacological and clinical data. Importantly, multidisciplinary collaborations between basic cancer research, bioinformatics and clinical research are key to addressing these urgent challenges preventing the clinical translation of CMS taxonomy.