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

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.

Keywords: basal subtype; breast cancer; drug repositioning; non-cancer therapeutics; personalized metabolic models; repurposing.

FIGURE 1

Basal like breast cancer specific highly connected protein-protein interaction modules. (A) EED module, (B) DHX9 module, (C) AURKA module. Darker nodes indicate the statistically significant positive correlations between mRNA and protein pairs. Thicker edges indicate lowest entropy levels between interacting pairs.

FIGURE 2

The pattern of basal like breast cancer specific modules activity across breast cancer subtypes. (A–C) EED, DHX9, and AURKA modules are highly activated in basal like tumors by using TCGA cohort-discovery set. (D–F) EED, DHX9, and AURKA modules are highly activated in basal like tumors by using METABRIC cohort-validation set.

FIGURE 3

The activity levels of basal like breast cancer specific modules in normal and basal like tumors. (A) EED module, (B) DHX9 module, (C) AURKA module.

FIGURE 4

The functional analysis of basal like breast cancer specific modules (A) The activity of oncogenic pathways correlated with module activities in TCGA cohort-discovery set. (B) The activity of oncogenic pathways correlated with module activities in METABRIC cohort-validation set. (C) High module activities characterized by high expression of cell cycle proteins.

FIGURE 5

Drug repositioning for basal like breast cancer specific modules (A) Module-drug networks (B) Drug categories of module specific drugs and common drugs among modules.

FIGURE 6

Significant essential metabolite differences between non-basal and basal like breast cancer specific personalized metabolic models and their associated pathways.