Identifying Drug Sensitivity Subnetworks with NETPHIX

Dorit S. Hochbaum,Rebecca Sarto Basso,Amanda S. Liu,Yoo-Ah Kim,Teresa M. Przytycka,Fabio Vandin,Damian Wojtowicz

doi:10.1016/j.isci.2020.101619

Abstract

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Highlights

Genetic alterations in cancer are associated with diverse phenotypic properties such as drug response or patient survival
Applying NETPHIX to a large-scale drug response data (Genomics of Drug Sensitivity in Cancer [GDSC]), we identified sensitivity-associated subnetworks for many of the drugs, which provided important insights into drug action
NETPHIX Method Overview NETPHIX takes gene alteration information, drug response profiles, and interaction network as inputs and identifies genetic alterations underlying the phenotype of interest (Figure 1A)

Summary

Introduction

Genetic alterations in cancer are associated with diverse phenotypic properties such as drug response or patient survival. The identification of mutations causing specific phenotypes and the interpretation of the phenotype-genotype relationships remain challenging owing to a large number of passenger mutations and cancer heterogeneity. Several projects have characterized drug sensitivity in hundreds of cancer cell lines for a large number of drugs (Yang et al, 2013; Barretina et al, 2012). These data, together with information about the genetic alterations in these cell lines, provided unprecedented opportunities to understand how genetic alterations affect drug sensitivity

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