Functional precision medicine approaches may complement current paradigms for non-metastatic colorectal cancer management.

Colorectal cancer is a major healthcare burden, and modern management of non-metastatic disease largely depends on guideline-concordant care based on histopathologic staging and empiric systemic therapy. While multidisciplinary care pathways and standardized guidelines have improved outcomes at the population level, they fall short in addressing the inter-patient and intra-tumoral heterogeneity that contributes to treatment resistance, recurrence of the disease, and unnecessary toxicity. Using a hypothetical patient journey, this commentary highlights how current practice often fails to align with patient needs despite being guideline-concordant. We discuss the limitations of current treatment paradigms and the shortcomings of modern tools such as genomic profiling, highlighting the continued need for complementary approaches. We hypothesize that functional precision medicine approaches have the potential to complement existing treatment paradigms and improve therapeutic stratification. We provide illustrative examples of their potential utility drawn from our recent clinical correlation study on colorectal cancer, in which we reported an association between assay outcomes and clinical response in a retrospective cohort. Additionally, we demonstrate the ability to identify intra-patient heterogeneity in ex vivo drug responses, suggesting phenotypically distinct subpopulations with differential drug sensitivity. Further investigation leading to the integration of these or similar technologies alongside genomic and minimal residual disease assessments could refine therapy selection and improve existing surveillance strategies. Ultimately, we suggest that while guideline-concordant care remains necessary, it is not sufficient for all patients. Continued research efforts utilizing functional precision medicine technologies may enable colorectal cancer management to move toward a more personalized framework that maximizes patient outcomes.