Early-onset colorectal cancer (EOCRC) is increasing worldwide and represents a biologically and clinically heterogeneous disease that requires precision approaches extending beyond conventional single-gene biomarkers. Recent analyses of receptor tyrosine kinase-RAS (RTK-RAS) pathway alterations demonstrate that their clinical significance is strongly dependent on age at diagnosis, ancestry, and treatment exposure. In particular, RTK-RAS alterations have been associated with unfavorable survival in untreated early-onset non-Hispanic White colorectal cancer, while an opposite association has been observed in FOLFOX-treated late-onset disease. At the same time, overall RTK-RAS pathway alteration frequencies remain relatively stable across patient groups, whereas gene-specific differences involving ERBB2, NF1, FGFR2, NTRK2, and other pathway components reveal substantial molecular heterogeneity. These observations challenge the use of RTK-RAS alteration status as a uniform prognostic biomarker and instead support a context-dependent model in which genomic alterations interact with age-related tumor biology, treatment pressures, ancestry-associated factors, and broader molecular states. Emerging genomic studies further suggest that EOCRC may harbor distinct mutational processes related to earlier-life exposures, reinforcing the need to integrate pathway alterations with mutational signatures, tumor microenvironment features, and multi-omic information. Artificial intelligence-enabled platforms such as AI-HOPE, developed at the Velazquez-Villarreal Lab at City of Hope, provide a scalable strategy for interrogating these complex multidimensional relationships through rapid cohort construction and hypothesis generation, while maintaining conventional statistical and experimental validation as essential components of inference. Future studies incorporating diverse populations, detailed treatment histories, genomic ancestry, longitudinal sampling, and functional validation will be critical for determining whether context-specific RTK-RAS patterns can ultimately improve risk stratification and therapeutic selection in EOCRC.

Colorectal cancer, Early-onset colorectal cancer, RTK–RAS pathway, FOLFOX, Artificial intelligence, AI-agents