Saurabh Sinha, Ph.D., and Casey Hanson, Ph.D. Candidate

Ph.D. candidate Casey Hanson

Computational Discovery of Transcription Factors Associated With Drug Response

Saurabh Sinha, Ph.D.
Associate Professor and Willett Faculty Scholar
University of Illinois at Urbana-Champaign Computer Science
Urbana, Illinois

Casey Hanson, Ph.D. Candidate
Research Assistant, Ph.D Candidate
University of Illinois at Urbana-Champaign
Champaign, Illinois

Topic: Biomarkers, Cancer

Dr. Sinha is an associate professor of Computer Science at the University of Illinois, Urbana-Champaign. His research interests include computational regulatory genomics, cis-regulatory evolution, pharmacogenomics, and big-data for genomics. Dr. Sinha is the education lead for the Mayo-Illinois Alliance, and serves as research principal investigator on the NIH BD2K Center at Illinois.

Hanson is a fifth year Ph.D. candidate in Computer Science at the University of Illinois Urbana - Champaign under the advisement of Dr. Sinha. His research involves developing statistics learning methods to identify the transcriptional regulators of drug response.

Their work can identify TFs that regulate the genes associated with drug response in a specific cohort. They can then conduct precision medicine by designing biologics or drugs that augment the activities of the few associated regulators. Changes at the regulatory level can propagate through their many downstream targets to genes informative of the drug response, which concludes with a phenotypic shift in the cell's response to the drug.

Their poster for the Individualizing Medicine Conference 2015 highlights identifying biomarkers in cancer. Instead of identifying single proteins associated with drug response, they want to include gene regulatory information to identify the regulators of these proteins associated with drug response. This cuts down on the number of biologics needed to be developed, as augmentation of a single protein can have effects that cascade through its regulatory targets to the drug response. An alternative would be to develop therapies that target each of the regulatory targets, but this increases the size and complexity of the therapy.

Here is the complete abstract from Dr. Sinha and Hanson’s Featured Poster of the Day for the Individualizing Medicine Conference 2015.

Abstract

Computational Discovery of Transcription Factors Associated With Drug Response

Primary Presenter: Saurabh Sinha, Ph.D.
Track: Pharmacogenomics
Title: Computational Discovery of Transcription Factors Associated With Drug Response
Authors: Casey Hanson, Junmei Cairns, Liewei Wang, Saurabh Sinha
Institutions: University of Illinois at Urbana-Champaign, Mayo Clinic

Purpose:
Genome wide association studies in pharmacogenomics generally involve associating drug-induced response with biomarkers. While GWAS suffers from sensitivity issues after correction, even signals that survive face problems of functional interpretation. Our study ameliorates this issue by posing the statistical test in the context of gene regulation. Rather than identifying SNPs or genes associated with drug response, we integrate biomarkers with genome-wide transcription factor (TF) binding data to elucidate whether a TF’s regulatory influence is associated with the drug. Our approach (GENMi) integrates gene expression, genotype, and drug-response data with ENCODE TF tracks to quantify the association between TFs and drugs via cis-regulatory eQTLs.

Methods:
The GENMi method for testing a (TF, drug) combination consists of the following procedure. First, SNPs located outside of the TF’s ENCODE peak are discarded. Considering the 50kb upstream region of a gene as a putative cis-regulatory region, the gene is scored by the most significant eQTL under the TF’s peak. The top 400 eQTL genes are then tested for overlap with all genes correlated with the drug’s-induced cytotoxicity, using Gene Set Enrichment Analysis.

Results:
We analyzed 114 TFs and 24 treatments using GENMi, yielding 334 significantly associated (TF, drug) pairs. The top 20 sparse (TF, drug) pairs yielded literature support for 13 associations, often from studies where perturbation of the TF’s expression changes drug response. We demonstrate the advantage of our approach by contrasting it with a baseline without using gene expression data. Our method reports more associations than the baseline approach at identical false positive rates (FPR). We further tested 14 TFs GENMi associated with either anthracycline (doxorubicin or epirubicin) and 21 TFs associated with either taxanes (paclitaxel or docetaxel). MTS cytotoxicity assays after TF knockdowns in two triple negative breast cancer cell lines, BT549 and MDA-MB231, yielded 6 TFs that significantly de-sensitized the cell to taxane induced apoptosis and 4 TFs that significantly de-sensitized the cell to anthracycline induced apoptosis

Tags: biomarker, cancer, Casey Hanson, Dr. Saurabh Sinha, individualized medicine, Individualized Medicine Conference 2015, pharmacogenomics, Uncategorized