RT Journal Article
SR Electronic
T1 Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 125138
DO 10.1101/125138
A1 Jie Liu
A1 John T. Halloran
A1 Jeffrey A. Bilmes
A1 Riza M. Daza
A1 Choli Lee
A1 Elisabeth M. Mahen
A1 Donna Prunkard
A1 Chaozhong Song
A1 Sibel Blau
A1 Michael O. Dorschner
A1 Vijayakrishna K. Gadi
A1 Jay Shendure
A1 C. Anthony Blau
A1 William S. Noble
YR 2017
UL http://biorxiv.org/content/early/2017/04/06/125138.abstract
AB 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.