ctDNA precedes imaging: A predictive model for real-time treatment adaptation in HPV-associated anal squamous cell carcinoma

Background: Real-time assessment of treatment response in HPV-associated anal squamous cell carcinoma (ASCC) remains challenging. Traditional tumor volume measurements require serial imaging that is costly, time-intensive, and delays clinical decision-making. Consequently, patients may receive prolonged suboptimal therapy or unnecessary toxicity from over-treatment. Circulating tumor DNA (ctDNA) offers a more accessible, real-time alternative biomarker-yet its predictive value and clinical utility for guiding treatment adaptation remain undefined. Methods: We developed a mechanistic mathematical model of tumor volume-ctDNA dynamics which we parameterized and calibrated to longitudinal data from 32 HPV-associated ASCC patients who received immunotherapy (pembrolizumab, once every 3 weeks, up to 2 years). The model was fit across three clinical scenarios: simultaneous measurements (8 patients), volume preceding ctDNA (14 patients), and ctDNA preceding volume (2 patients). We quantified ctDNA's predictive capacity for early treatment response assessment. Results: ctDNA demonstrated strong positive correlation with tumor burden (SLD) and predicted clinical response status within 4 weeks of treatment initiation. Critically, ctDNA kinetics preceded volume changes in multiple patients, providing early signal for response assessment before imaging confirmation. The mathematical model robustly captured heterogeneous patient dynamics across all measurement scenarios, validating its predictive framework. Conclusions: ctDNA functions as a leading indicator biomarker enabling early identification of treatment response in HPV-associated ASCC. Our quantitative framework translates this biomarker into actionable clinical predictions, allowing clinicians to make real-time decisions on treatment escalation, maintenance, or de-escalation. This approach enhances precision oncology by replacing burdensome imaging with accessible blood-based monitoring-particularly impactful for underserved populations with limited imaging access. Future studies will prospectively validate this model to inform personalized, adaptive treatment strategies.