Systems Pharmacology-Based Precision Therapy and Drug Combination Discovery for Breast Cancer.

Ze-Jia Cui,Bo-Min Lv,Hong-Yu Zhang,Teng-Fei Dai,Yuan Quan,Xin-Yu Tong,Xiong-Hui Zhou,Min Gao

doi:10.3390/cancers13143586

Ze-Jia Cui, Bo-Min Lv + Show 6 more

Open Access

https://doi.org/10.3390/cancers13143586

Copy DOI

Abstract

Simple SummaryBreast cancer (BC) is a typical global cancer and the second leading cause of cancer-related deaths among women worldwide. BC is a heterogeneous disease with several subtypes, and it is a challenge to use multi-omic data effectively to find suitable drugs for patients. In this paper, we used the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drug combinations that could be used to treat different BC subtypes. Our results showed that this precision drug discovery strategy identified important disease-related genes in individuals and specific groups, supporting its efficiency, high reliability, and practical application value in drug discovery.Breast cancer (BC) is a common disease and one of the main causes of death in females worldwide. In the omics era, researchers have used various high-throughput sequencing technologies to accumulate massive amounts of biomedical data and reveal an increasing number of disease-related mutations/genes. It is a major challenge to use these data effectively to find drugs that may protect human health. In this study, we combined the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drugs that could be used to treat different BC subtypes. We used this strategy to screen four BC subtype-specific drug combinations and verified the potential activity of combining gefitinib and irinotecan in triple-negative breast cancer (TNBC) through in vivo and in vitro experiments. The results of cell and animal experiments demonstrated that the combination of gefitinib and irinotecan can significantly inhibit the growth of TNBC tumour cells. The results also demonstrated that this systems pharmacology-based precision drug discovery strategy effectively identified important disease-related genes in individuals and special groups, which supports its efficiency, high reliability, and practical application value in drug discovery.

Highlights

Introduction distributed under the terms andBreast cancer (BC) is a solid tumour that seriously endangers women’s health and is the most common type of cancer
We aimed to identify valuable drug combinations for BC treatment and to verify the effectiveness of the precision drug discovery strategy proposed in this study
‘Glioma’ was significantly enriched, with an false discovery rates (FDRs) of 2.68 × 10−3. These results demonstrate that the gene dependency network may be used to reveal the gene dependencies of genes related to cancer prognosis

Summary

Introduction

Breast cancer (BC) is a solid tumour that seriously endangers women’s health and is the most common type of cancer. BC is a heterogeneous disease with subtypes that differ in morphology, molecular biology, clinical manifestations, and response to conditions of the Creative Commons. Due to the high heterogeneity of BC patients [5], the genes that are related to drug response may not be the same among patients, even among those with the same subtype. Genomic and environmental changes, as well as other factors, should be considered for each patient in precision medicine [6].

Objectives

Methods

Results

Discussion

Conclusion