DOI
https://doi.org/10.25772/807X-D010
Defense Date
2018
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Computer Science
First Advisor
Tomasz Arodz
Second Advisor
J. Paul Brooks
Third Advisor
Maria C Rivera
Fourth Advisor
Bridget McInnes
Fifth Advisor
Alberto Cano
Abstract
Background
Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.
Results
We propose QuaDMutEx an QuadMutNetEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming.
QuaDMutNetEx is our proposed method that combines protein-protein interaction networks to the method elements of QuaDMutEx. In particular, QuaDMutEx incorporates three novel elements: a non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty. In the new method, we incorporated a new quadratic rewarding term that prefers gene solution set that is connected with respect to protein-protein interaction networks. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.
Conclusions
Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx and QuaDMutNetEx are tools that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
11-20-2018