Related Experiment Video
Updated: May 24, 2025
10:12
Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization
Published on: May 15, 2018
8.8K
Computational Modeling for Circulating Cell-Free DNA in Clinical Oncology
Linh Nguyen Phuong1, Sébastien Salas1,2, Sébastien Benzekry1
1Computational Pharmacology and Clinical Oncology Department, Centre Inria d'Université Côte d'Azur, Cancer Research Centre of Marseille, Paoli Calmettes Institute, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, Marseille, France.
JCO Clinical Cancer Informatics
|February 28, 2025
Summary
Computational modeling of cell-free DNA (cfDNA) enhances cancer diagnosis and treatment monitoring. These models provide crucial noninvasive insights into tumor biology for improved clinical decisions.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Liquid biopsy using circulating cell-free DNA (cfDNA) is a key tool for cancer management.
- Computational modeling (CM) is vital for extracting actionable clinical insights from cfDNA data.
Purpose of the Study:
- To review computational modeling (CM) methods for cell-free DNA (cfDNA) in clinical oncology.
- To highlight the role of machine learning (ML) and mechanistic approaches in analyzing cfDNA data.
- To emphasize the potential of CM-cfDNA for noninvasive cancer diagnosis, prognosis, and treatment monitoring.
Main Methods:
- Review of CM-cfDNA methods, including machine learning (ML) and mechanistic approaches.
- Integration of biological principles into mechanistic models for understanding cfDNA dynamics.
- Analysis of cfDNA concentration, fragmentation patterns, and mutation detection using ML.
Main Results:
- CM-cfDNA approaches significantly improve diagnostic accuracy and prognostic marker identification.
- ML models demonstrate high sensitivity and specificity for early cancer detection.
- Mechanistic models elucidate cfDNA kinetics in relation to tumor growth and treatment response, including immune checkpoint inhibitors.
Conclusions:
- CM-cfDNA is a significant advancement in precision oncology, translating cfDNA data into clinical insights.
- Standardization of protocols and validation across diverse populations are crucial for clinical integration.
- Hybrid ML and mechanistic modeling approaches promise enhanced biological understanding and clinical utility.
