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Review
. 2023 May 13;15(10):2746.
doi: 10.3390/cancers15102746.

Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management

Leili Rejali  1 , Romina Seifollahi Asl  1 , Fatemeh Sanjabi  2 , Nayeralsadat Fatemi  1 , Hamid Asadzadeh Aghdaei  1 , Mahsa Saeedi Niasar  1 , Pardis Ketabi Moghadam  1 , Ehsan Nazemalhosseini Mojarad  3 , Enrico Mini  4 , Stefania Nobili  5
Affiliations
Review

Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management

Leili Rejali et al. Cancers (Basel). .

Abstract

Colorectal cancer (CRC) is the second cause of cancer-related deaths in both sexes globally and presents different clinical outcomes that are described by a range of genomic and epigenomic alterations. Despite the advancements in CRC screening plans and treatment strategies, the prognosis of CRC is dismal. In the last two decades, molecular biomarkers predictive of prognosis have been identified in CRC, although biomarkers predictive of treatment response are only available for specific biological drugs used in stage IV CRC. Translational clinical trials mainly based on "omic" strategies allowed a better understanding of the biological heterogeneity of CRCs. These studies were able to classify CRCs into subtypes mainly related to prognosis, recurrence risk, and, to some extent, also to treatment response. Accordingly, the comprehensive molecular characterizations of CRCs, including The Cancer Genome Atlas (TCGA) and consensus molecular subtype (CMS) classifications, were presented to improve the comprehension of the genomic and epigenomic landscapes of CRCs for a better patient management. The CMS classification obtained by the CRC subtyping consortium categorizes CRC into four consensus molecular subtypes (CMS1-4) characterized by different prognoses. In this review, we discussed the CMS classification in different settings with a focus on its relationships with precursor lesions, tumor immunophenotype, and gut microbiota, as well as on its role in predicting prognosis and/or response to pharmacological treatments, as a crucial step towards precision medicine.

Keywords: biomarkers predictive of drug response; biomarkers predictive of prognosis; cancer risk; colorectal cancer; consensus molecular subtype; gut microbiota; immunoscore; precision medicine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Different types of colorectal polyps and their association with the CMS of CRC. (a) Colorectal polyps have a hereditary and sporadic basis. Only 5% of colorectal polyps are familial, consisting of familial adenomatous polyposis (FAP) and Lynch syndrome (LS). FAP-associated adenomas originate from APC germline mutation and are followed by the WNT/B catenin activation, and they are like the CRC with CIN status. Conversely, LS-related adenomas result from a germline mutation in MMR genes and are likely to cause a more advanced premalignancy. The MSI phenotype can be seen in only half of the LS polyps. About one-third of all CRC cases follow the serrated pathways consisting of sessile serrated adenomas (SSA) and hyperplastic polyps (HP). (b) Most of the FAP polyps are classified into CMS2. The LS adenomas at the advanced level are transformed from the CMS2 to the CMS1. SSA and HP adenomas are enriched with CMS1 and CMS4 phenotypes. Created with BioRender.com.
Figure 2
Figure 2
Colon cells in relation to the CMS subtypes and some molecular and genomics features of each subtype. “Colon-like” and “undifferentiated”. Colon-like cell lines include both CMS2 and CMS3, representing an increased number of key transcription factors (HNF4A and MYB). Undifferentiated cell lines include all CMS4, and most CMS1, which have an elevated expression of TGFβ-provoked genes. This TGFβ signaling in cancer-associated fibroblasts (CAFs) induces the initiating capacity for CRC cells. Furthermore, the occurrence of premetastatic features of the CAFs through TGFβ 1/2 paracrine signaling indicates the modulating role of the tumor microenvironment in cancer cell expression. Created with BioRender.com.
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