The somatic molecular evolution of cancer: Mutation, selection, and epistasis

被引:11
|
作者
Dasari, Krishna [1 ]
Somarelli, Jason A. [2 ]
Kumar, Sudhir [3 ,4 ]
Townsend, Jeffrey P. [1 ,5 ,6 ,7 ,8 ]
机构
[1] Yale Coll, New Haven, CT USA
[2] Duke Univ, Med Ctr, Dept Med, Durham, NC 27706 USA
[3] Temple Univ, Inst Genom & Evolutionary Med, Philadelphia, PA 19122 USA
[4] Temple Univ, Dept Biol, Philadelphia, PA 19122 USA
[5] Yale Sch Publ Hlth, Dept Biostat, New Haven, CT USA
[6] Yale Univ, Yale Canc Ctr, New Haven, CT USA
[7] Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA
[8] Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT USA
基金
美国国家科学基金会;
关键词
Molecular evolution; Cancer; Fitness landscapes; Epistasis; Metastasis; Tumor microenvironment; CELL-INTRINSIC MECHANISMS; ACQUIRED-RESISTANCE; LINEAGE PLASTICITY; TUMOR; LANDSCAPE; HETEROGENEITY; IPILIMUMAB; DISCOVERY; BLOCKADE; FITNESS;
D O I
10.1016/j.pbiomolbio.2021.08.003
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixa-tion occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal decon-volution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evo-lution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:56 / 65
页数:10
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