Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

Abstract

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

“…The majority of studies relied on the application of metabolomics-based approaches in BC tissue [141,143] and serum [140,142,144] samples, although groups have evaluated transcriptomic [33,45] and proteomic [146] profiles in tissue samples. Various studies have described how the combination of omics approaches could characterize specific targets and foster the development of novel therapeutic strategies for specific subgroups of BC patients [147][148][149]. In particular, multi-omics studies have focused on identifying and validating metabolic enzymes as promising therapeutic strategies for the treatment of different BC tumors (Table 4).…”

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

“…Recently, more and more studies showed that genomic aberrations are key events in the progression from normal to tumoral tissue [6,7]. In addition, to investigate the crucial role of genomic alterations and gene expression profiles in disease progression, some studies reported novel candidate genes by integrating gene expression and CNA profiles [8,9].…”

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

“…A network propagation analysis was used to reposition drugs based on a drug-pathway network, where pathways for each drug were identified by an enrichment analysis of the genes regulated by each drug using the Connectivity Map (CMAP) for the drug’s phenotypic profile [ 24 ]. Moreover, computational methods with diverse molecular data types were developed to identify therapeutic targets for BC [ 25 ]. These methods considered breast cancer in general without considering its subtypes and did not address the heterogeneity of BC.…”