Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer.

Beste Turanli,Mathias Uhlen,Kubra Karagoz,Michael L Gatza,Kazim Yalcin Arga,Gholamreza Bidkhori,Raghu Sinha,Adil Mardinoglu

doi:10.3389/fgene.2019.00420

Abstract

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated “omics” approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

Highlights

Breast cancer is the most commonly diagnoses and second leading cause of cancer-related deaths in women in the United States with an estimated 268,600 new cases and 41,760 deaths in 2019 (Siegel et al, 2019)
Cancer cells are characterized by increase in network entropy comprising high uncertainty, pathway redundancy and promiscuous signaling resulting from intra-sample heterogeneity
We categorized the expression of each gene and for each patient using 179 basal and 852 non-basal-like samples from The Cancer Genome Atlas (TCGA) into three classes as -1, 0, 1, These classes were integrated with a high confident protein-protein interactions (PPIs) network (Karagoz et al, 2016) and the frequency of PPIs estimated for both basal-like and non-basal-like tumors

Summary

Introduction

Breast cancer is the most commonly diagnoses and second leading cause of cancer-related deaths in women in the United States with an estimated 268,600 new cases and 41,760 deaths in 2019 (Siegel et al, 2019). This subtype, which is highly concordant with TNBC, accounts for ∼15–20% of diagnosed breast tumors but more than 1-in-4 breast cancer related deaths each year This is, due in part, to the lack of effective therapeutic options for TNBC patients aside from multi-agent chemotherapy, which remains the standard-of-care treatment despite a limited and varied response among patients and the related toxic side-effects (Solzak et al, 2017). In this context, we and others, have proposed that systems level analyses can assist in revealing the underlying molecular mechanism of the diseases, discovery of biomarkers for specific subtypes, identification of subtype specific drug targets and reposition of drugs that can be used in effective treatment of patients (Mardinoglu and Nielsen, 2015; Mardinoglu et al, 2018; Turanli et al, 2018)

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