Abstract
Colorectal cancer (CRC) is one of the most molecularly heterogeneous malignancies, with complexity that extends far beyond traditional histopathological classifications. The consensus molecular subtypes (CMS) established in 2015 brought a marked advancement in the taxonomy of CRC, consolidating six classification systems into four novel subtypes, which focus on vital gene expression patterns and clinical and prognostic outcomes. However, nearly a decade of clinical experience with CMS classification has revealed fundamental limitations that underscore the inadequacy of any single classification system for capturing the full spectrum of CRC biology. The inherent challenges of the current paradigm are multifaceted. In the CMS classification, mixed phenotypes that remain unclassifiable constitute 13% of CRC cases. This reflects the remarkable heterogeneity that CRC shows. The tumor budding regions reflect the molecular shift due to CMS 2 to CMS 4 switching, causing further heterogeneity. Moreover, the reliance on bulk RNA sequencing fails to capture the spatial organization of molecular signatures within tumors and the critical contributions of the tumor microenvironment. Recent technological advances in spatial transcriptomics, single-cell RNA sequencing, and multi-omic integration have revealed the limitations of transcriptome-only classifications. The emergence of CRC intrinsic subtypes that attempt to remove microenvironmental contributions, pathway-derived subtypes, and stem cell-based classifications demonstrates the field's recognition that multiple complementary classification systems are necessary. These newer molecular subtypes are not discrete categories but biological continua, thus highlighting that the vast molecular landscape is a tapestry of interlinked features, not rigid subtypes. Multiple technical hurdles cause difficulty in implementing the clinical translation of these newer molecular subtypes, including gene signature complexity, platform-dependent variations, and the difficulty of getting and preserving fresh frozen tissue. CMS 4 shows a poor prognostic outcome among the CMS subtypes, while CMS 1 is associated with poor survival in metastatic cases. However, the predictive value for definitive therapy remains subdued. Looking forward, the integration of artificial intelligence, liquid biopsy approaches, and real-time molecular monitoring promises to enable dynamic, multi-dimensional tumor characterization. The temporal and spatial complexity can only be captured by complementary molecular taxonomies rather than a single, unified system of CRC classification. Such an approach recognizes that different clinical questions - prognosis, treatment selection, resistance prediction - may require different molecular lenses, each optimized for specific clinical applications. This editorial advocates for a revolutionary change from pursuing a single "best" classification system toward a diverse approach that welcomes the molecular mosaic of CRC. Only through such comprehensive molecular characterization can we hope to achieve the promise of precision oncology for the diverse spectrum of patients with CRC.