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The adaptive evolution of cancer driver genes
Langyu Gu1, Canwei Xia2, Shiyu Yang3
1State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China. guly6@mail.sysu.edu.cn.
BMC Genomics
|April 25, 2023
Summary
Cancer driver genes evolve through natural selection in humans, partly as a byproduct of adaptive changes. This evolutionary perspective is crucial for understanding cancer and developing targeted precision medicine strategies for diverse populations.
Area of Science:
- Evolutionary genetics
- Human genomics
- Cancer biology
Background:
- Cancer is a significant human disease, yet cancer-associated genes often show signs of positive selection.
- This presents an evolutionary paradox: cancer may arise as a secondary consequence of adaptive selection in humans.
- Systematic studies on the evolution of cancer driver genes remain limited.
Purpose of the Study:
- To investigate the evolutionary history of cancer driver genes.
- To determine if these genes have been subject to selection during human evolution.
- To explore the relationship between genetic selection and cancer incidence.
Main Methods:
- Comparative genomics analysis
- Population genetics analysis
- Computational molecular evolutionary analysis
Main Results:
- Evolution of 568 cancer driver genes across 66 cancer types was assessed over long (millions of years) and recent (~100,000 years) timescales.
- Eight cancer genes (11 cancer types) showed positive selection in the human lineage (long timescale).
- Thirty-five cancer genes (47 cancer types) exhibited positive selection in modern human populations (recent selection).
- Specific single nucleotide polymorphisms (SNPs) in thyroid cancer driver genes (CUX1, HERC2, RGPD3) were under positive selection in East Asian and European populations.
Conclusions:
- Cancer evolution can be partly explained as a byproduct of human adaptive evolution.
- Different SNPs at the same genetic locus can face distinct selection pressures across populations.
- These findings highlight the importance of considering population-specific selection pressures in precision medicine, particularly for targeted therapies.
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