Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies

Master regulators of FGFR2 signalling and breast cancer risk.

The fibroblast growth factor receptor 2 (FGFR2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying FGFR2-mediated risk are still unknown. Using model systems we show that FGFR2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify SPDEF, ERα, FOXA1, GATA3 and PTTG1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that ERα occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that ERα, FOXA1 and GATA3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.This is the final version of the article. It was originally published in Nature Communications here: http://www.nature.com/ncomms/2013/130917/ncomms3464/full/ncomms3464.html

Cancer as a defective network for NF-κB

In a recent review we addressed the role of the transcription factor NF-κB, in shaping the cancer microenvironment. NF-κB, which interacts with chromatin modulators by cell-specific dynamics, controls cell interactions during inflammation, and its abnormal feedback regulation is implicated in cancer. Inflammation normally reprograms cells through changes in key topological elements of chromosomal DNA. As a result, inflammation overrides cell phenotype: initially, reprogramming cell function halts processes that impede the response of a damaged tissue to the cause of the harm, and eventually, late reprogramming of cells will replenish tissue structure and restore function. Each cell type provides a distinct resource for restoration of tissue integrity, tissue function, and for replenishment of the responsiveness of the immune system. Modulators of NF-κB transcriptional activity alter key aspects of gene expression and tissue integrity. NF-κB network alterations confer transcriptional plasticity to cancer

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks