Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MF...

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Autores principales:	Natsuki Tokutomi, Kenta Nakai, Sumio Sugano
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bee21620b9b544ea83d241c7d30f9bef
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spelling	oai:doaj.org-article:bee21620b9b544ea83d241c7d30f9bef2021-12-02T20:14:56ZExtreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.1932-620310.1371/journal.pone.0243595https://doaj.org/article/bee21620b9b544ea83d241c7d30f9bef2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0243595https://doaj.org/toc/1932-6203Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.Natsuki TokutomiKenta NakaiSumio SuganoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0243595 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Natsuki Tokutomi Kenta Nakai Sumio Sugano Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
description	Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.
format	article
author	Natsuki Tokutomi Kenta Nakai Sumio Sugano
author_facet	Natsuki Tokutomi Kenta Nakai Sumio Sugano
author_sort	Natsuki Tokutomi
title	Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
title_short	Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
title_full	Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
title_fullStr	Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
title_full_unstemmed	Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
title_sort	extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/bee21620b9b544ea83d241c7d30f9bef
work_keys_str_mv	AT natsukitokutomi extremevaluetheoryasaframeworkforunderstandingmutationfrequencydistributionincancergenomes AT kentanakai extremevaluetheoryasaframeworkforunderstandingmutationfrequencydistributionincancergenomes AT sumiosugano extremevaluetheoryasaframeworkforunderstandingmutationfrequencydistributionincancergenomes
_version_	1718374668365201408

Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.

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