Assessing the contribution of tumor mutational phenotypes to cancer progression risk.
Assessing the contribution of tumor mutational phenotypes to cancer progression risk.
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评估肿瘤突变表型对癌症进展风险的贡献。
DOI：
10.1371/journal.pcbi.1008777
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Schwartz R
中科院分区：
文献类型：
--
作者：
Tao Y;Rajaraman A;Cui X;Cui Z;Chen H;Zhao Y;Eaton J;Kim H;Ma J;Schwartz R
癌症通过克隆进化过程中体细胞基因组改变的积累而发生。已经对驱动癌症发展和进展的潜在因果突变进行了深入研究。然而，最近的许多证据表明，肿瘤演变通常是由多种体细胞超变机制驱动的，这些机制在不同的癌症中以不同的组合或程度起作用。突变表型的这些变化预测了进展结果，与它们迄今为止产生的特定突变无关。在这里，我们探讨了突变表型的这些差异如何以及在多大程度上作用于癌症以预测其未来进展的问题。我们开发了一种计算范式，使用进化树推理（肿瘤发生）算法来获得量化单个肿瘤突变表型的特征，然后使用机器学习框架来识别预测进展的关键特征。对乳腺浸润癌和肺癌的分析表明，癌症进展的未来临床结局（总生存率和无病生存率）的风险的很大一部分可以仅仅从系统发育分析得出的突变表型特征中解释。我们进一步表明，突变表型具有额外的预测能力，即使在考虑传统的临床和驱动基因为中心的基因组预测进展。这些结果证实了突变表型在促进癌症进展风险方面的重要性，并提出了增强传统临床数据或以驱动程序为中心的生物标志物的预测能力的策略。癌症是由以前健康的细胞群中的突变引起的，这些细胞群通常在肿瘤生长明显之前的数年内积累。这一过程受进化规律的支配，细胞中出现的随机突变偶尔会导致选择具有生长优势的特定细胞群。然而，这一过程在任何特定肿瘤中如何展开是高度随机和特异的，使得肿瘤生长难以预测。肿瘤进化如此随机的一个原因是，肿瘤经常包含突变，这些突变使癌细胞以不同的方式产生新的突变，而这些突变机制（我们称之为“突变表型”）本身的变化为癌症的未来进展提供了预测能力。然而，人们对癌症进展的风险在多大程度上以突变机制的这些变化为特征知之甚少，而不是其他来源。在这项工作中，我们研究的问题有多少癌症进展的风险是由这些差异患者之间的突变偏好解释。我们发现，未来癌症进展的风险中约有三分之一是由突变表型的变化引起的。此外，这些突变表型与其他预测信息来源是互补的，并且只是部分冗余，这一发现通过显示使用这些信息的机器学习模型可以增强我们预测哪些癌症进展和复发的能力而得到证实，这超出了仅从更传统的基因组和临床数据来源所能实现的能力。
Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression—overall survival and disease-free survival—can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers. Cancer results from mutations in previously healthy cell populations that typically accumulate over a period of years before tumor growth is apparent. This process is governed by the laws of evolution, by which random mutations arising in cells will occasionally lead to selection for particular cell populations with growth advantages. How this process unfolds in any given tumor is highly random and idiosyncratic, however, making tumor growth difficult to predict. One reason tumor evolution is so random is that tumors frequently contain mutations that bias the cancer cells to generate new mutations in different ways, yet variations among these mechanisms of mutability (which we call “mutational phenotypes”) themselves provide predictive power for a cancer’s future progression. However, little is known about the degree to which the risk of cancer progressing is characterized by these variations in mutation mechanism as opposed to other sources. In this work, we examine the question of how much of the risk of cancer progression is explained by these differences patient-to-patient in mutability preferences. We find that approximately a third of the risk of future cancer progression is accounted for by variations in mutational phenotypes. Furthermore, these mutational phenotypes are complementary to and only partially redundant with other sources of predictive information, a finding confirmed by showing that machine learning models using such information can enhance our ability to predict which cancers progress and recur beyond what can be accomplished from more traditional genomic and clinical data sources alone.
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