Dissertation/Thesis Abstract

Investigating Mechanisms of Robustness in BRCA -Mutated Breast and Ovarian Cancers
by Bueno, Raymund, Ph.D., Yeshiva University, 2018, 167; 11014738
Abstract (Summary)

The BRCA1 and BRCA2 (BRCA) genes are two tumor suppressors that when mutated, predispose patients to breast and ovarian cancer. The BRCA genes encode proteins that mediate the repair of DNA double strand breaks. Functional loss of the BRCA genes is detrimental to the integrity of the genome because without access to functional BRCA protein, inefficient and error-prone repair pathways are used instead. These pathways, such as Non-homologous end joining, do not accurately repair the DNA, which can introduce mutations and genomic rearrangements. Ultimately the genome is not repaired faithfully and the predisposition to cancer greatly increases. In addition to their contribution to DNA repair, the BRCA genes have been shown to have transcriptional activity, and this functional role can also be a driving factor behind the tumor suppressor activity.

Robustness is the ability of a complex system to sustain viability despite perturbations to it. In the context of a complex disease such as cancer, robustness gives cancers the ability to sustain uncontrollable growth and invasiveness despite treatments such as chemotherapy that attempt to eliminate the tumor. A complex system is robust however can be fragile to perturbations that the system not optimized against. In cancers, these fragilities have the potential to be cancer specific targets that can eradicate the disease specifically.

Patients with mutations in BRCA tend to have breast and ovarian cancers that are difficult to treat; chemotherapy is the only option and no targeted therapies are available. Targeting the synthetic lethal interaction (SLI), a mechanism of robustness, between BRCA and PARP1 genes was clinically effective in treating BRCA-mutated breast and ovarian cancers. This suggests that understanding robustness in cancers can reveal potential cancer specific therapies.

In this thesis, a computational approach was developed to identify candidate mechanisms of robustness in BRCA-mutated breast and ovarian cancers using the publicly accessible patient gene expression and mutation data from the Cancer Genome Atlas (TCGA). Results showed that in ovarian cancer patients with a BRCA2 mutation, the expression of genes that function in the DNA damage response were kept at stable expression state compared to those patients without a mutation. The stable expression of genes in the DNA damage response may highlight a SLI gene network that is precisely controlled. This result is significant as disrupting this precision can potentially lead to cancer specific death. In breast cancers, genes that were differentially expressed in patients with BRCA mutations were identified. A Bayesian network was performed to infer candidate interactions between BRCA1 and BRCA2 and the differentially expressed FLT3, HOXA11, HPGD, MLF1, NGFR, PLAT, and ZBTB16 genes. These genes function in processes important to cancer progression such as apoptosis and cell migration. The connection between these genes with BRCA may highlight how the BRCA genes influence cancer progression.

Taken together, the findings of this thesis enhance our understanding of the BRCA genes and their role in DNA damage response and transcriptional regulation in human breast and ovarian cancers. These results have been attained from systems-level models to identify candidate mechanisms underlying robustness of cancers. The work presented predicts interesting candidate genes that may have potential as drug targets or biomarkers in BRCA-mutated breast and ovarian cancers.

Indexing (document details)
Advisor: Mar, Jessica C., Guha, Chandan
School: Yeshiva University
School Location: United States -- New York
Source: DAI-B 80/03(E), Dissertation Abstracts International
Subjects: Biostatistics, Systematic biology, Bioinformatics
Keywords: BRCA Gene, Breast Cancer, Gene Expression, Ovarian Cancer, The Cancer Genome Atlas
Publication Number: 11014738
ISBN: 9780438681866
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