Narrow Results

Heterogeneous high-throughput biological data become readily available for various diseases. The amount of data points generated by such experiments does not allow manual integration of the information to design the most optimal therapy for a disease. We describe a novel computational workflow for designing therapy using Ariadne Genomics Pathway Studio software. We use publically available microarray experiments for glioblastoma and automatically constructed ResNet and ChemEffect databases to exemplify how to find potentially effective chemicals for glioblastoma — the disease yet without effective treatment. Our first approach involved construction of signaling pathway affected in glioblastoma using scientific literature and data available in ResNet database. Compounds known to affect multiple proteins in this pathway were found in ChemEffect database. Another approach involved analysis of differential expression in glioblastoma patients using Sub-Network Enrichment Analysis (SNEA). SNEA identified angiogenesis-related protein Cyr61 as the major positive regulator upstream of genes differentially expressed in glioblastoma. Using our findings, we then identified breast cancer drug Fulvestrant as a major inhibitor of glioblastoma pathway as well as Cyr61. This suggested Fulvestrant as a potential treatment against glioblastoma. We further show how to increase efficacy of glioblastoma treatment by finding optimal combinations of Fulvestrant with other drugs.

This PSB 2024 session discusses the many broad biological, computational, and statistical approaches currently being used for therapeutic drug target identification and repurposing of existing treatments. Drug repurposing efforts have the potential to dramatically improve the treatment landscape by more rapidly identifying drug targets and alternative strategies for untreated or poorly managed diseases. The overarching theme for this session is the use and integration of real-world data to identify drug-disease pairs with potential therapeutic use. These drug-disease pairs may be identified through genomic, proteomic, biomarkers, protein interaction analyses, electronic health records, and chemical profiling. Taken together, this session combines novel applications of methods and innovative modeling strategies with diverse real-world data to suggest new pharmaceutical treatments for human diseases.

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network “impact matrix”, which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced “omics” profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITS_eGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITS_SNP-SNP). Significance of ITS_SNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10^-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient’s specific polymorphisms.

In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical model, called conditional random field (CRF), to systematically integrate genomic, chemical, functional and pharmacological data plus the topology of DTI networks into a unified framework to predict missing DTIs. Tests on two benchmark datasets show that our method can achieve excellent prediction performance with the area under the precision-recall curve (AUPR) up to 94.9. These results demonstrate that our CRF model can successfully exploit heterogeneous data to capture the latent correlations of DTIs, and thus will be practically useful for drug repositioning. Supplementary Material is available at http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf.