Zhao Junsong, Salomon Matthew P, Shibata Darryl, Curtis Christina, Siegmund Kimberly, Marjoram Paul
Department of Molecular and Computational Biology, University of Southern California, Los Angeles, California, United States of America.
Department of Molecular Oncology, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, California, United States of America.
PLoS One. 2017 Mar 3;12(3):e0172516. doi: 10.1371/journal.pone.0172516. eCollection 2017.
Tumor growth is an evolutionary process involving accumulation of mutations, copy number alterations, and cancer stem cell (CSC) division and differentiation. As direct observation of this process is impossible, inference regarding when mutations occur and how stem cells divide is difficult. However, this ancestral information is encoded within the tumor itself, in the form of intratumoral heterogeneity of the tumor cell genomes. Here we present a framework that allows simulation of these processes and estimation of mutation rates at the various stages of tumor development and CSC division patterns for single-gland sequencing data from colorectal tumors. We parameterize the mutation rate and the CSC division pattern, and successfully retrieve their posterior distributions based on DNA sequence level data. Our approach exploits Approximate Bayesian Computation (ABC), a method that is becoming widely-used for problems of ancestral inference.
肿瘤生长是一个进化过程，涉及突变积累、拷贝数改变以及癌症干细胞（CSC）的分裂和分化。由于无法直接观察这一过程，推断突变发生的时间以及干细胞如何分裂颇具难度。然而，这些祖先信息以肿瘤细胞基因组的肿瘤内异质性形式编码在肿瘤本身之中。在此，我们提出了一个框架，该框架能够模拟这些过程，并针对来自结直肠癌肿瘤的单腺体测序数据，估算肿瘤发展各个阶段的突变率以及癌症干细胞的分裂模式。我们对突变率和癌症干细胞分裂模式进行参数化，并基于DNA序列水平数据成功检索出它们的后验分布。我们的方法采用了近似贝叶斯计算（ABC），这是一种在祖先推断问题中越来越广泛使用的方法。
