Evolutionary genomics, tumor dynamics, and mathematical modeling of cancer progression: Implications for precision oncology.

Authors

Wei Li, Xiaotong Wang

Published in

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. Pages 106019. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

Cancer is a disease of somatic evolution driven by the sequential acquisition of genetic alterations that confer reproductive advantage. Understanding how tumors grow, diversify, and adapt to therapeutic pressure requires an integrated framework spanning evolutionary genomics, population genetics, and dynamical systems theory. This review synthesizes current knowledge on clonal dynamics, intratumor heterogeneity (ITH), and the mathematical tools used to model cancer progression, with direct implications for the design of precision oncology strategies. Neutral evolution and Darwinian selection jointly govern subclonal architecture, and quantitative metrics of ITH predict treatment failure with reproducible accuracy. Mathematical models including logistic growth equations, Lotka-Volterra competition systems, Wright-Fisher processes, and agent-based simulations have formalized these biological phenomena into tractable frameworks that yield clinically testable predictions. Fitness landscape theory explains how tumors navigate mutational space toward states of high adaptive fitness, while adaptive therapy protocols exploit competitive suppression between sensitive and resistant clones. Liquid biopsy technologies now provide real-time genomic surveillance aligned with model predictions, enabling monitoring without repeated tissue sampling. Integration of these computational and experimental approaches is accelerating the identification of actionable biomarkers and informing next-generation treatment scheduling. The convergence of tumor evolutionary biology and quantitative modeling represents a fundamental shift in oncology, from treating disease states to managing evolving ecosystems.

PMID:
42749242
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.