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Dissertation/Thesis Abstract

Cryptic Driver Mutations in Cancer
by Gorelick, Alexander N., Ph.D., Weill Medical College of Cornell University, 2020, 162; 28150456
Abstract (Summary)

Identifying genetic variants that promote tumor initiation and progression is fundamental to understanding the biology and therapeutic vulnerabilities of cancer. Much research has therefore been devoted to identifying novel driver mutations, usually based on their individual recurrence in large cohorts of tumors sequenced with whole-exome sequencing or targeted sequencing panels of known cancer-associated genes. As a result of this practice, both the interaction of combinations of variants within in a tumor, and the potential oncogenic effects of mutations outside of the nuclear-encoded exome remain poorly understudied, despite emerging evidence for their contributing roles in cancer. The work presented in this dissertation aims to address two components of this knowledge gap: First, I extend our current understanding of the mutational patterns of cancer-associated genes from that of single variants viewed in isolation to include the interplay of multiple variants within a single gene and tumor, which I term “composite mutations”. Composite mutations are present in nearly one in four tumors, arise with specific allelic configurations reflecting context-specific selective pressures, and can lead to hypermorphic or neomorphic phenotypes depending on gene function. Second, I explore the role in cancer of somatic mutations in the human mitochondrial genome (mtDNA). Here, I show that individual respiratory complexes and their interactions with tissue lineage and mutational consequence are critical determinants of cancer-associated mtDNA mutation patterns. Moreover, I show that both common truncating mutations and rarer missense alleles are associated with a pan-lineage transcriptional program, and with substantial increase in overall survival of colorectal adenocarcinoma patients, demonstrating a clear functional relationship between genotype and phenotype. Taken together, my results reveal cryptic mutations in human cancer that help uncover previously undescribed molecular forces driving tumor evolution.

Indexing (document details)
Advisor: Taylor, Barry S.
Commitee: Berger, Michael, Chandarlapaty, Sarat, Solit, David, Taylor, Barry S.
School: Weill Medical College of Cornell University
Department: Computational Biology and Medicine
School Location: United States -- New York
Source: DAI-B 82/10(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Medicine
Keywords: Cancer genomics, Computational biology, Mutations, Oncogenes, Tumor suppressor genes
Publication Number: 28150456
ISBN: 9798597098982
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