<html><body><div id="zimbraEditorContainer" style="font-family: garamond,new york,times,serif; font-size: 12pt; color: #000000" class="2"><div></div><div data-marker="__QUOTED_TEXT__"><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;"></p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">Pawel Przytycki will present his FPO "Algorithms for deciphering cancer genomes: from differential mutation to differential allele specific expression" on Friday, 8/31/2018 at 11:30 in CS 402.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">The members of his committee are as follows: Mona Singh (adviser); Joshua Akey (LSI) and Olga Troyanskaya (readers); Mona Singh, Ben Raphael, and Yibin Kang (MOL) (examiners).<br data-mce-bogus="1"></p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">All are welcome to attend. A copy of his thesis is available in CS 310. </p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">The thesis abstract follows below.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;"><br data-mce-bogus="1"></p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">Large-scale cancer genome sequencing consortia have provided a huge influx of somatic</p><p></p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">mutation data across large cohorts of patients. Understanding how these observed</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">genetic alterations give rise to specific cancer phenotypes represents a major aim of</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">cancer genomics. In this dissertation, I present two methods for utilizing natural</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">variation as a background for interpreting cancer genomes.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">In Chapter 2, I introduce di↵erential mutation analysis, a framework for uncovering</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">cancer genes that compares the mutational profiles of genes across cancer genomes</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">with natural germline variation across healthy individuals. I hypothesize that if a</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">gene is less constrained with respect to variation across the healthy population, it</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">may also be able to tolerate a greater amount of somatic mutation without experiencing</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">a drastic detrimental functional change. I develop a fast and simple approach</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">that uncovers genes that are di↵erentially mutated between cancer genomes and the</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">genomes of healthy individuals. I demonstrate that my di↵erential mutation approach</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">outperforms considerably more sophisticated approaches for discovering cancer genes.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">In Chapter 3, I propose the concept of di↵erential allele-specific expression to</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">identify genes within an individual’s cancer whose allele-specific expression (ASE) differs</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">from what is found in matched normal tissue, with the overall goal of uncovering</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">genes whose regulation is altered via functional noncoding somatic mutations. I reason</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">that since specific noncoding mutations usually occur on only one chromosome,</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">they are expected to a↵ect only the expression of the allele derived from that chromosome.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">Thus, ASE is a potential avenue towards detecting cis mutations that lead</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">to regulatory changes. I present three methods to identify di↵erential ASE in paired</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">tumor-normal samples, and apply them to breast cancer tumor samples. I demonstrate</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">that di↵erential ASE can detect dysregulation caused by nonsense mediated</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">decay and copy number variation, that known cancer-related genes are enriched for</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">di↵erential ASE, and that genes with cis noncoding mutations are enriched for difiii</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">ferential ASE. Finally, I show that noncoding mutations in cis with genes exhibiting</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">di↵erential ASE often disrupt known regulatory mechanisms. I thus conclude that</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">di↵erential ASE is a powerful means for characterizing gene dysregulation due to cis</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;">noncoding mutations.</p><p style="margin-left: 50px;" data-mce-style="margin-left: 50px;"><br></p></div></div></body></html>