The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Impact of deleterious passenger mutations on cancer progression
Open accessAbstract
Cancer progression is driven by the accumulation of a small number of genetic alterations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional mutations termed passengers. Passengers are widely believed to have no role in cancer, yet many passengers fall within protein-coding genes and other functional elements that can have potentially deleterious effects on cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter the course of neoplastic progression. Our approach combines evolutionary simulations of cancer progression with an analysis of cancer sequencing data. From simulations, we find that passengers accumulate and largely evade natural selection during progression. Although individually weak, the collective burden of passengers alters the course of progression, leading to several oncological phenomena that are hard to explain with a traditional driver-centric view. We then tested the predictions of our model using cancer genomics data and confirmed that many passengers are likely damaging and have largely evaded negative selection. Finally, we use our model to explore cancer treatments that exploit the load of passengers by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Though both approaches lead to cancer regression, the latter is a more effective therapy. Our results suggest a unique framework for understanding cancer progression as a balance of driver and passenger mutations.
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Cites
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Self-citationAn Integrated Genomic Analysis of Human Glioblastoma Multiforme
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A census of human cancer genes
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Distant metastasis occurs late during the genetic evolution of pancreatic cancer
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Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants
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Melanoma genome sequencing reveals frequent PREX2 mutations
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Assessing the evolutionary impact of amino acid mutations in the human genome.
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Universal Patterns of Selection in Cancer and Somatic Tissues
Martincorena, I., Raine, K. M., Gerstung, M., Dawson, K. J., Haase, K., Van Loo, P., Davies, H., Stratton, M. R., & Campbell, P. J. (2017) · Cell · doi:10.1016/j.cell.2017.09.042 · Cited by 1,523
RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues
Yizhak, K., Aguet, F., Kim, J., Hess, J. M., Kübler, K., Grimsby, J., Frazer, R., Zhang, H., Haradhvala, N. J., Rosebrock, D., et al. (2019) · Science · doi:10.1126/science.aaw0726 · Cited by 500
Acute myeloid leukaemia: a paradigm for the clonal evolution of cancer?
Grove, C. S., & Vassiliou, G. S. (2014) · Disease Models & Mechanisms · doi:10.1242/dmm.015974 · Cited by 172
Mutational signatures and mutable motifs in cancer genomes
Rogozin, I. B., Pavlov, Y. I., Goncearenco, A., De, S., Lada, A. G., Poliakov, E., Panchenko, A. R., & Cooper, D. N. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx049 · Cited by 58
Next-generation sequencing methodologies to detect low-frequency mutations: “Catch me if you can”
Menon, V., & Brash, D. E. (2023) · Mutation Research - Reviews in Mutation Research · doi:10.1016/j.mrrev.2023.108471 · Cited by 48
Metadata
- Authors
- McFarland, C. D., Korolev, K. S., Kryukov, G. V., Sunyaev, S. R., & Mirny, L. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2013-02-06
- Volume
- 110
- Issue
- 8
- Pages
- 2910-2915
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2059437408
- Cited by
- 317
- References
- 11
- FWCI
- 10.24
- Type
- journal article
- Language
- en
- Open access
- green
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- McFarland, C. D., Korolev, K. S., Kryukov, G. V., Sunyaev, S. R., & Mirny, L. A. (2013). Impact of deleterious passenger mutations on cancer progression. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1213968110
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
