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Title: Algorithms for deciphering cancer genomes: from differential mutation to differential allele specific expression
Authors: Przytycki, Pawel
Advisors: Singh, Mona
Contributors: Computer Science Department
Keywords: Algorithms
Allele Specific Expression
Somatic Mutation
Subjects: Computer science
Molecular biology
Issue Date: 2018
Publisher: Princeton, NJ : Princeton University
Abstract: Large-scale cancer genome sequencing consortia have provided a huge influx of somatic mutation data across large cohorts of patients. Understanding how these observed genetic alterations give rise to specific cancer phenotypes represents a major aim of cancer genomics. In this dissertation, I present two methods for utilizing natural variation as a background for interpreting cancer genomes. In Chapter 2, I introduce differential mutation analysis, a framework for uncovering cancer genes that compares the mutational profiles of genes across cancer genomes with natural germline variation across healthy individuals. I hypothesize that if a gene is less constrained with respect to variation across the healthy population, it may also be able to tolerate a greater amount of somatic mutation without experiencing a drastic detrimental functional change. I develop a fast and simple approach that uncovers genes that are differentially mutated between cancer genomes and the genomes of healthy individuals. I demonstrate that my differential mutation approach outperforms considerably more sophisticated approaches for discovering cancer genes. In Chapter 3, I propose the concept of differential allele-specific expression to identify genes within an individual’s cancer whose allele-specific expression (ASE) differs from what is found in matched normal tissue, with the overall goal of uncovering genes whose regulation is altered via functional noncoding somatic mutations. I reason that since specific noncoding mutations usually occur on only one chromosome, they are expected to affect only the expression of the allele derived from that chromosome. Thus, ASE is a potential avenue towards detecting cis mutations that lead to regulatory changes. I present three methods to identify differential ASE in paired tumor-normal samples, and apply them to breast cancer tumor samples. I demon- strate that differential ASE can detect dysregulation caused by nonsense mediated decay and copy number variation, that known cancer-related genes are enriched for differential ASE, and that genes with cis noncoding mutations are enriched for diffferential ASE. Finally, I show that noncoding mutations in cis with genes exhibiting differential ASE often disrupt known regulatory mechanisms. I thus conclude that differential ASE is a powerful means for characterizing gene dysregulation due to cis noncoding mutations.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Computer Science

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