Computational Modeling for Circulating Cell-Free DNA in Clinical Oncology.

Abstract

Purpose: Liquid biopsy, specifically circulating cell-free DNA (cfDNA), has emerged as a powerful tool for cancer early diagnosis, prognosis, and treatment monitoring over a wide range of cancer types. Computational modeling (CM) of cfDNA data is essential to harness its full potential for real-time, noninvasive insights into tumor biology, enhancing clinical decision making.

Design: This work reviews CM-cfDNA methods applied to clinical oncology, emphasizing both machine learning (ML) techniques and mechanistic approaches. The latter integrate biological principles, enabling a deeper understanding of cfDNA dynamics and its relationship with tumor evolution.

Results: Key findings highlight the effectiveness of CM-cfDNA approaches in improving diagnostic accuracy, identifying prognostic markers, and predicting therapeutic outcomes. ML models integrating cfDNA concentration, fragmentation patterns, and mutation detection achieve high sensitivity and specificity for early cancer detection. Mechanistic models describe cfDNA kinetics, linking them to tumor growth and response to treatment, for example, immune checkpoint inhibitors. Longitudinal data and advanced statistical constructs further refine these models for quantification of interindividual and intraindividual variability.

Conclusion: CM-cfDNA represents a pivotal advancement in precision oncology. It bridges the gap between extensive cfDNA data and actionable clinical insights, supporting its integration into routine cancer care. Future efforts should focus on standardizing protocols, validating models across populations, and exploring hybrid approaches combining ML with mechanistic modeling to improve biological understanding.