Computationally-Efficient Framework for Inferring the Evolutionary History of a Tumor Given Single-Cell Sequencing Data - Dr. Julia Chifman
DCEG Events
June 5, 2019 | 10:30 AM – 11:30 AM
NCI Shady Grove Rockville, MD
BIOSTATISTICS BRANCH SEMINAR SERIES PRESENTS
Speaker
Julia Chifman, Ph.D.
Assistant Professor
Department of Mathematics and Statistics
American University
Abstract
The advent of single-cell sequencing provides the ability to model clonal evolution of tumors within individual patients. Inference of such within-patient tumor phylogenies has the potential to advance the authors' understanding of the variation in the process of tumor progression, with strong clinical implications. Here the authors consider the problem of rapid and robust inference of the tumor phylogeny from a sample of single-cell genotype data under a Markov model that incorporates error in the observed sequencing process. Using techniques from algebraic statistics, the authors develop a computationally-efficient method for inference and demonstrate its properties using simulation. The authors apply the method to empirical data, including a dataset on bladder cancer. The authors conclude with a discussion of how the phylogeny can be used to infer a probability distribution on the space of possible mutation orders.
**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods. The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**
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