Clinical translation of circulating tumor DNA: technological innovation and future pathways from molecular monitoring to precision decision-making

As the core technology of liquid biopsy, circulating tumor DNA (ctDNA) is emerging as a promising approach in precision oncology—from static tissue genotyping to dynamic molecular monitoring. Leveraging its unique advantages of non-invasiveness, real-time assessment and repeatable sampling, ctDNA analysis offers incremental value beyond traditional tissue biopsy across key clinical scenarios, including early cancer screening, minimal residual disease (MRD) assessment, treatment response monitoring and resistance mechanism dissection. This review systematically discusses the biological foundations and core technological platforms of ctDNA, with a particular focus on the trade-offs between sensitivity and applicability in tumor-informed versus tumor-naïve strategies. We further summarize the latest clinical evidence for ctDNA across multiple solid tumors, including colorectal cancer, non-small-cell lung cancer and melanoma. Despite its tremendous potential, the clinical translation of ctDNA still faces four major challenges: the sensitivity bottleneck in detecting ultra-low-abundance variants; the “low-shedder” phenotype arising from heterogeneous tumor release; false-positive interference from clonal hematopoiesis; and the lack of end-to-end standardization coupled with cost-effectiveness concerns. Notably, recent studies have revealed a possible “decoupling” between absolute ctDNA levels and relative tumor fraction during dynamic monitoring, highlighting the importance of multi-parameter integrated assessment to avoid clinical misinterpretation. Looking forward, five major trends may shape the evolution of ctDNA technologies. First, a shift towards multi-omics integration—encompassing methylation, fragmentomics and copy number alterations—to construct a panoramic molecular portrait beyond mutation detection. Second, the incorporation of artificial intelligence and machine learning for ultra-low-frequency signal denoising, multimodal data fusion and clonal evolution modeling. Third, the validation of ctDNA-guided clinical decision-making through prospective adaptive trials (e.g., treatment escalation and de-escalation strategies). Fourth, expansion into alternative biofluids such as cerebrospinal fluid and urine to overcome the limitations of blood-based assays. Fifth, reducing testing costs and expanding reimbursement to improve accessibility and equity. In summary, ctDNA analysis is transitioning from research laboratories into routine clinical practice, with the ultimate goal of achieving truly personalized, whole-course cancer management through real-time, non-invasive and panoramic molecular monitoring.