Liu Jie, Halloran John T, Bilmes Jeffrey A, Daza Riza M, Lee Choli, Mahen Elisabeth M, Prunkard Donna, Song Chaozhong, Blau Sibel, Dorschner Michael O, Gadi Vijayakrishna K, Shendure Jay, Blau C Anthony, Noble William S
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Department of Electrical Engineering, University of Washington, Seattle, WA, USA.
Sci Rep. 2017 Dec 5;7(1):16943. doi: 10.1038/s41598-017-16813-4.
A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.
全面表征肿瘤遗传异质性对于理解癌症如何演变以及如何逃避治疗至关重要。尽管已经开发了许多算法来捕捉肿瘤异质性，但它们旨在分析单一类型的基因组畸变或单个活检样本。在此，我们展示了THEMIS（通过集成系统实现的肿瘤异质性可扩展建模），它使用动态图形模型，允许对来自同一患者的多个活检样本中的不同类型基因组畸变进行联合分析。模拟实验表明，THEMIS比其前身TITAN具有更高的准确性。THEMIS的异质性分析结果通过临床肿瘤活检的单细胞DNA测序得到验证。当使用THEMIS分析同一患者多个活检样本之间的肿瘤异质性时，它有助于揭示突变积累历史、追踪癌症进展并识别与治疗耐药性相关的突变。我们通过一个可扩展的建模平台实现了我们的模型，这使得我们的方法具有开放性、可重复性，并且便于其他人进行扩展。
