Longitudinal Assessment of Circulating Tumor DNA: A Proposed Statistical Framework

Abstract

Abstract As circulating tumor DNA (ctDNA) levels can reflect disease progression, achieving a comprehensive understanding of the temporal evolution of ctDNA is key to informing clinical decision making. However, temporal changes can exhibit complex non-linear patterns and differ substantially across patients. Additionally, patient characteristics and outcomes may impact temporal change. Thus, traditional statistical approaches may be inadequate in characterizing ctDNA evolution over time. In this proof-of-concept study, we propose utilizing a new approach using a hierarchical random effects cubic spline model, which is sufficiently flexible to capture complex temporal ctDNA patterns while supporting the integration of patient characteristics. To demonstrate the benefits of the approach, a retrospective cohort of non-small cell lung cancer patients who received anti-EGFR therapies was analyzed. Model results are presented graphically in the form of patient-level response patterns, where each combination of patient characteristics produces a unique pattern. Patients with various ages, levels of health status, as well as mortality status were contrasted, where results provide examples of how the model can further our conceptualization of ctDNA dynamics and demonstrates how results can be used in targeted, patient-centered, clinical decision-making.