Dasari Krishna, Somarelli Jason A, Kumar Sudhir, Townsend Jeffrey P
Yale College, New Haven, CT, USA.
Department of Medicine, Duke University Medical Center, USA.
Prog Biophys Mol Biol. 2021 Oct;165:56-65. doi: 10.1016/j.pbiomolbio.2021.08.003. Epub 2021 Aug 6.
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 fixation 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 deconvolution, 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 evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.
癌症进展归因于单核苷酸变异、拷贝数畸变、杂合性缺失、染色体不稳定、上位性相互作用以及肿瘤微环境中的体细胞变化。目前尚不完全清楚这些变化中哪些对癌症至关重要,哪些是辅助性的。利用经典进化生物学中的一些概念和工具,可以阐明患者体内癌症进化的动态本质。癌细胞中的中性突变率可根据同义突变等基因组数据计算得出,而选择压力可根据中性突变和漂变预期之外的固定率计算得出。然而,这些突变对癌症的效应大小因上位性相互作用而变得复杂,上位性相互作用可决定基因突变的可能顺序。反过来,对体细胞癌症进展的纵向系统发育分析提供了一个机会,以识别癌症进化中的关键时刻,将驱动突变的时间与临床时间线中的相应标志性事件联系起来。这些分析揭示了肿瘤发生过程中以及整个临床时间尺度上遗传和表型变化的时间方面。使用相关框架,克隆反卷积、克隆的物理位置及其系统发育关系可用于推断肿瘤迁移历史。此外,可采用长期应用于生物体基因型与环境相互作用的方法来分析与肿瘤微环境的遗传相互作用。与基因型、表型和环境相关的癌症进化适应度景观能够实现更准确的个性化治疗策略。了解肿瘤、原发性肿瘤和转移性肿瘤进化背后的轨迹,有望在准确和个性化预测治疗反应方面取得根本性进展。