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Systematic Identification of Non-coding Pharmacogenomic Interactions in Cancer

Wang, Yue (2018) Systematic Identification of Non-coding Pharmacogenomic Interactions in Cancer. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Long non-coding RNAs (lncRNAs) can serve as promising biomarkers and therapeutic targets in cancer. However, their roles in regulating cancer drug response have not gained much momentum.
By integrating multiple dimensional pharmacogenomic data of 11,950 lncRNAs in 5,605 tumors and 1,005 cancer cell lines, I first investigated how the cancer cell lines can recapitulate the genomic and epigenetic alterations of lncRNAs in primary tumor patients. Next, I built lncRNA-drug response models for 265 anti-cancer agents across 27 cancer types based on Elastic Net (EN) regression and bootstrap aggregation. This analysis identified a landscape of 162,327 lncRNA-drug interactions, yielding more than 1,000 lncRNA-based EN drug response prediction (LENP) models in pan-cancer and cancer-specific scales. The LENP models are further applied for 49 FDA approved drugs to TCGA patient samples from 21 cancer types. A multivariate cox regression is implemented to show that cancer cell line derived LENP models could predict the therapeutic outcome in patients with stomach, thyroid, breast, and colorectal cancer. To extend the knowledge of how lncRNAs regulate the drug resistance in cancer, I designed an lncRNA-pathway co-expression analysis and suggested that lncRNAs could regulate drug response through drug-metabolism or drug-target pathways. Finally, I conducted the RNA-seq analysis and experimentally validated that EPIC1, the top predictive lncRNA for the BET inhibitors, strongly promotes iBET762 and JQ-1 resistance in breast cancer through activating MYC transcriptional activity.
To our best knowledge, this thesis represents the first large-scale systematic study to link noncoding genotypes with drug response phenotypes in both cancer cell lines and primary tumors. The landscape of lncRNA-drug interactions should serve as a comprehensive knowledgebase for the identification of non-coding biomarkers for cancer precision therapy.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wang, Yueyuw90@pitt.eduyuw90
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairYang, Dadyang@pitt.edudyang
Committee MemberLi, Songsol4@pitt.edusol4
Committee MemberFernandez, Christianchf63@pitt.educhf63
Committee MemberJohnston, Paulpaj18@pitt.edupaj18
Date: 9 April 2018
Date Type: Publication
Defense Date: 16 March 2018
Approval Date: 9 April 2018
Submission Date: 4 April 2018
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 72
Institution: University of Pittsburgh
Schools and Programs: School of Pharmacy > Pharmaceutical Sciences
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Non-coding RNAs, Pharmacogenomics, Cancer therapy, Machine Learning
Date Deposited: 09 Apr 2018 12:37
Last Modified: 09 Apr 2018 12:37
URI: http://d-scholarship.pitt.edu/id/eprint/34091

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