Abstract 2947: Discovering novel secondary drug combinations in sex hormone-related cancers using large-scale pharmacogenomics databases through computational modeling and molecular genetics
Authors: Mazumder Suman , Chen Li , Mukherjee Ujjal K. , Mitra Amit K. 
Published: August 2019

Abstract
Response to drugs is heterogeneous - not all patients respond equally well to treatment and those who do often acquire resistance over the course of treatment. Therefore, predicting the efficacy of anti-cancer drugs and identifying secondary regimens to circumvent drug resistance is essential to prevent delay in the selection of more effective alternative strategies.
Epidemiologic studies have demonstrated a tendency for common cancers to aggregate in families. For example, breast, prostate and ovarian cancers are sometimes grouped together owing to shared etiology, common genetic susceptibility regions and similar effects of sex hormones (estrogen or testosterone) exposure on their progression. Our objective is to use the vast array of human cancer cell lines in Genomics of Drug Sensitivity in Cancer (GDSC1000), the largest public database of drug sensitivity in human cancer cell lines - 265 anti-cancer drugs in 1074 cell lines (224,510 IC50 values), to apply a computational prediction algorithm that will identify successful secondary drug combinations in sex-hormone related cancers resistant to standard-of-care drugs.
We used a greedy algorithm-based set-covering computational optimization method followed by a regularization technique to seek all secondary drugs that could kill maximum number of cancer cell lines resistant to the standard-of-care drug in a sequential manner ordered by the number of cell lines killed. A greedy algorithm constructs a solution to an optimization problem piece by piece through a sequence of choices to find the overall, or globally, optimal solution. For the purpose of this study, we used 136 potentially sex-hormone related cancer subtypes: breast (n=52), cervix (n=14), endometrium (n=11), ovary (n=45), prostate (n=8), testis (n=3), vulva (n=3). To validate our in silico prediction results, we treated the drug-resistant cancer cell lines with the predicted best secondary drugs, alone or as combination, and performed in vitro chemo-sensitivity assays to evaluate the effect of combination treatments using Chou-Talalay’s combination index (CI) method and the isobologram algorithm. Furthermore, we performed gene expression profiling (GEP) analysis using llumina’s next-generation sequencing system to identify differentially expressed (DE) genes and pathways underlining novel secondary therapies in chemotherapy-resistant cancers.
Our preliminary data suggests promising early breakthrough and supports our hypothesis that the predicted drug combinations can circumvent drug resistance in human cancers - which we will be presenting at the annual meeting. This study is part of our unique research endeavor that aims to create a universally applicable software application for predicting novel secondary therapies in drug-resistant cancers for any cancer type and any test drug.
Citation Format: Suman Mazumder, Li Chen, Ujjal K. Mukherjee, Amit K. Mitra. Discovering novel secondary drug combinations in sex hormone-related cancers using large-scale pharmacogenomics databases through computational modeling and molecular genetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2947.