105: Genome-level Selection as a Universal Marker of Cancer Therapy Resistance

️ Episode 105: Genome-level Selection as a Universal Marker of Cancer Therapy Resistance

In this episode of PaperCast Base by Base, we explore groundbreaking research revealing how the evolutionary dynamics of tumor genomes can serve as a universal marker of resistance to cancer therapy. By analyzing whole-exome sequencing data from multiple cancer types—both treated and untreated—the study uncovers a consistent evolutionary signal that correlates with treatment failure and poor prognosis.

Study Highlights:
The authors examined the strength of protein-level selection (dN/dS) and mutational burden (N) across diverse patient cohorts, including colorectal, esophageal, lung, breast, bladder, glioblastoma, leukemia, and multiple myeloma. In untreated cancers, dN/dS values remained stable during primary tumor progression, with some cancer-specific changes in metastases. In stark contrast, resistant tumors from treated patients showed a near-universal shift toward neutral evolution (dN/dS ≈ 1), a state linked to higher tumor fitness and worse outcomes. This shift was validated in both published datasets and a large original multiple myeloma cohort, and was consistent across solid tumors and hematologic malignancies. Analyses of sequential biopsies and phylogenetic trees revealed a bi-phasic evolutionary pattern: early positive selection of driver mutations followed by late purifying selection against deleterious passengers, with the neutral regime marking maximal tumor adaptability.

Conclusion:
Monitoring the selection regime of tumor genomes during treatment could provide a valuable biomarker to guide clinical decisions, enabling timely adjustments in therapy when resistance emerges.

Reference:
Persi E, Sudalagunta PR, Wolf YI, Canevarolo RR, Damaghi M, Shain KH, Silva AS, Koonin EV. Genome-level selection in tumors as a universal marker of resistance to therapy. Nature Communications. 2025;16:6535. doi:https://doi.org/10.1038/s41467-025-61709-x

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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