Wang, Yue
(2018)
Systematic Identification of Non-coding Pharmacogenomic Interactions in Cancer.
Master's Thesis, University of Pittsburgh.
(Unpublished)
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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| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| 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 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/34091 |
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