Jeff A. Bilmes's Publications

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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

Jie Liu, John Halloran, Jeffrey Bilmes, Riza Daza, Choli Lee, Elisabeth Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael Dorschner, Vijayakrishna Gadi, Jay Shendure, Anthony Blau, and William Noble. Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. bioRxiv, Cold Spring Harbor Laboratory, 2017.

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Abstract

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.

BibTeX

@article{Liu125138,
	author = {Liu, Jie and Halloran, John and Bilmes, Jeffrey and Daza, Riza and Lee, Choli and Mahen, Elisabeth and Prunkard, Donna and Song, Chaozhong and Blau, Sibel and Dorschner, Michael and Gadi, Vijayakrishna and Shendure, Jay and Blau, Anthony and Noble, William},
	title = {Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies},
	year = {2017},
	doi = {10.1101/125138},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {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.},
	URL = {https://www.biorxiv.org/content/early/2017/04/06/125138},
	eprint = {https://www.biorxiv.org/content/early/2017/04/06/125138.full.pdf},
	journal = {bioRxiv},
}

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