Year of Publication

2020

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department

Statistics

First Advisor

Dr. Arnold J. Stromberg

Second Advisor

Dr. Chi Wang

Abstract

Studying tumor evolution is a major task to understand the biological mechanism of carcinogenesis, develop new cancer therapies, and prevent drug resistance. We focus on two important questions in tumor evolution. The first question is to quantify intra-tumor heterogeneity, where multiple subclones of tumor cells with distinct transcriptomic profiles. Another question is to estimate the temporal order of alteration of key cancer pathways during tumor evolution. We present a new statistical method to 1) reconstruct the evolutionary history and population frequency of the subclonal lineages of tumor cells and 2) infer temporal order of pathway alterations in tumor evolution for each individual patient based on RNA-seq data. Our method uses a Bayesian nonparametric prior and nested stick-breaking process to allow for evolutional trees of infinite nodes, and to identify cell population frequencies which have the highest likelihood of generating the observed RNA-seq data. Markov Chain Monte Carlo method based on slice sampling is incorporated to perform Bayesian inference. Based on the constructed evolutional trees, a patient-specific pathway analysis is performed to identify enriched pathways that are altered in the earlier and later phases of tumor evolution of that patient. Simulations and real data analysis demonstrate that the proposed method reliably recover the phylogenetic chain and population frequency of the subclonal lineages of tumor cells and accurately infer the temporal order of pathway alterations for individual patient.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.046

Available for download on Monday, February 07, 2022

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