ctDNA in the Management of Resectable & Advanced Colorectal Cancer: Current Status and Future Directions

Circulating tumor DNA (ctDNA) is an increasingly important biomarker for molecular residual disease (MRD) after curative-intent treatment of colorectal cancer (CRC), but its prognostic value must be distinguished from proven clinical utility. This structured narrative review synthesizes evidence from PubMed/MEDLINE, Embase, ClinicalTrials.gov, and major oncology conference proceedings, covering stage I-III CRC and selected patients with resected or ablated oligometastatic stage IV disease. We review tumor-informed and tumor-agnostic assay strategies, postoperative sampling, serial versus landmark testing, low-shedding disease, clonal hematopoiesis, and major prospective and randomized studies. Postoperative ctDNA positivity consistently identifies patients at substantially increased risk of recurrence, while serial ctDNA dynamics provide additional prognostic information. In stage II colon cancer, the randomized DYNAMIC trial showed that a ctDNA-guided strategy reduced adjuvant chemotherapy use without compromising long-term recurrence outcomes. By contrast, DYNAMIC-III did not establish non-inferiority for ctDNA-guided de-escalation and showed no recurrence-free survival benefit from conventional chemotherapy escalation in ctDNA-positive stage III disease. The Phase III ALTAIR trial likewise did not meet its pre specified primary disease-free survival endpoint for treatment of molecular relapse with trifluridine/tipiracil. Current professional guidance therefore supports a cautious distinction between clinical validity and clinical utility: ctDNA can inform prognosis, counseling, and clinical-trial eligibility, but should not routinely replace standard surveillance or independently determine treatment escalation or de-escalation outside evidence-based indications or clinical trials. Future progress requires harmonized assays, randomized demonstration of outcome benefit, and prospective validation of integrated ctDNA, pathology, transcriptomic, and AI-derived risk models.