The heterogenous nature of colorectal cancer (CRC) renders it a major clinical challenge. Increasing genomic understanding of CRC has improved our knowledge of this heterogeneity and the main cancer drivers, with significant improvements in clinical outcomes. Comprehensive molecular characterization has allowed clinicians a more precise range of treatment options based on biomarker selection. Furthermore, this deep molecular understanding likely extends therapeutic options to a larger number of patients. The biological associations of consensus molecular subtypes (CMS) with clinical outcomes in localized CRC have been validated in retrospective clinical trials. The prognostic role of CMS has also been confirmed in the metastatic setting, with CMS2 having the best prognosis, whereas CMS1 tumors are associated with a higher risk of progression and death after chemotherapy. Similarly, according to mesenchymal features and immunosuppressive molecules, CMS1 responds to immunotherapy, whereas CMS4 has a poorer prognosis, suggesting that a CMS1 signature could identify patients who may benefit from immune checkpoint inhibitors regardless of microsatellite instability (MSI) status. The main goal of these comprehensive analyses is to switch from “one marker-one drug” to “multi-marker drug combinations” allowing oncologists to give “the right drug to the right patient.” Despite the revealing data from transcriptomic analyses, the high rate of intra-tumoral heterogeneity across the different CMS subgroups limits its incorporation as a predictive biomarker. In clinical practice, when feasible, comprehensive genomic tests should be performed to identify potentially targetable alterations, particularly in RAS/BRAF wild-type, MSI, and right-sided tumors. Furthermore, CMS has not only been associated with clinical outcomes and specific tumor and patient phenotypes but also with specific microbiome patterns. Future steps will include the integration of clinical features, genomics, transcriptomics, and microbiota to select the most accurate biomarkers to identify optimal treatments, improving individual clinical outcomes. In summary, CMS is context specific, identifies a level of heterogeneity beyond standard genomic biomarkers, and offers a means of maximizing personalized therapy.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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