Unraveling Drug Response from Pharmacogenomic Data to Advance Systems Pharmacology Decisions in Tumor Therapeutics

Konstantinos Kardamiliotis,Andigoni Malousi,Ioanna Aslanidou,Ioannis S Vizirianakis,Evangelina Karanatsiou,Eirini Stergiou

doi:10.3390/futurepharmacol2010003

Konstantinos Kardamiliotis, Andigoni Malousi + Show 4 more

Open Access

https://doi.org/10.3390/futurepharmacol2010003

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Abstract

The availability of systematic drug response registries for hundreds cell lines, coupled with the comprehensive profiling of their genomes/transcriptomes enabled the development of computational methods that investigate the molecular basis of drug responsiveness. Herein, we propose an automated, multi-omics systems pharmacology method that identifies genomic markers of anti-cancer drug response. Given a cancer type and a therapeutic compound, the method builds two cell line groups on the antipodes of the drug response spectrum, based on the outer quartiles of the maximum micromolar screening concentration. The method intersects cell lines that share common features in their mutation status, gene expression levels or copy number variants, and a pool of drug response biomarkers (core genes) is built, using genes with mutually exclusive alterations in the two cell line groups. The relevance with the drug target pathways is then quantified, using the combined interaction score of the core genes and an accessory protein network having strong, physical/functional interactions. We demonstrate the applicability and effectiveness of our methodology in three use cases that end up in known drug-gene interactions. The method steps into explainable bioinformatics approaches for novel anticancer drug-gene interactions, offering high accuracy and increased interpretability of the analysis results. Availability: https://github.com/PGxAUTH/PGxGDSC.

Highlights

The advances in -omics technologies have led to the creation of high-throughput and high-content resources, as essential tools upon experimentation in cancer research, implementing systems pharmacology and molecular medicine approaches
In parallel with the advances been witnessed by the research community in genomic technologies, during the last decades the research in anticancer drug discovery has led to the approval of more than 100 agents by the US Food and Drug Administration (FDA) [3]
We propose an automated methodology that relies on public resources to explain the causal relationships between genomic variants, copy number variations and gene expression profiles with drug efficacy over various cancer cell lines and tumor types

Summary

Introduction

The advances in -omics technologies have led to the creation of high-throughput and high-content resources, as essential tools upon experimentation in cancer research, implementing systems pharmacology and molecular medicine approaches. Next-generation sequencing enabled the development of predictive models that incorporate genomic and gene expression data in order to assess the therapeutic potential of anti-cancer drugs [5]. These pharmacogenomic models have proven highly accurate in several cases, implying the prevalence of strong interactions between genomic profiles and drug sensitivity in the pre-clinical setting. To exploit this evidence in a clinical environment, we first need to estimate the clinical risk-benefit in standardized pharmacogenomically informed clinical procedures [6].

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