Drug repurposing for glioblastoma based on molecular subtypes

Abstract

A recent multi-platform analysis by The Cancer Genome Atlas identified four distinct molecular subtypes for glioblastoma (GBM) and demonstrated that the subtypes correlate with clinical phenotypes and treatment responses. In this study, we developed a computational drug repurposing approach to predict GBM drugs based on the molecular subtypes. Our approach leverages the genomic signature for each GBM subtype, and integrates the human cancer genomics with mouse phenotype data to identify the opportunity of reusing the FDA-approved agents to treat specific GBM subtypes. Specifically, we first constructed the phenotype profile for each GBM subtype using their genomic signatures. For each approved drug, we also constructed a phenotype profile using the drug target genes. Then we developed an algorithm to match and prioritize drugs based on their phenotypic similarities to the GBM subtypes. Our approach is highly generalizable for other disorders if provided with a list of disorder-specific genes. We first evaluated the approach in predicting drugs for the whole GBM. For a combined set of approved, potential and off-label GBM drugs, we achieved a median rank of 9.3%, which is significantly higher (p < e−7) than 45.7% for a recent approach that also uses the mouse phenotype data. Then we applied the approach on GBM subtypes. Analysis result shows the variations of enriched pathways, associated phenotypes and prioritized drugs across different subtypes. We ranked the first-line chemotherapy for GBM in different positions for each subtypes, and the rank variation was consistent with the previous finding on different drug responses among subtypes. In summary, this study makes an effort towards translating the molecular stratification into better survival for GBM.

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment

“… 5 The primary advantage of drug repositioning is that it starts from compounds with well-characterized pharmacology and safety profiles that can greatly reduce the risk of attrition in drug development in clinical phases. 6 We have recently developed novel computational algorithms that identified repurposed drug candidates to treat neuropsychiatric disorders, including schizophrenia 7 and Parkinson’s disease, 8 , 9 , 10 infectious diseases including dengue fever 11 and malaria; 12 cancers including glioblastoma; 13 , 14 and immune-mediated diseases including Crohn’s disease, 15 inflammatory bowel disease 16 and rheumatoid arthritis. 17 However, to date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for HGSOC have not been undertaken.…”

A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer

“…Recently, the Mouse Genome Database (MGD) has made available large amounts of phenotypic descriptions of systematic gene knockouts in mouse models 35 . We have recently shown that these strong causal gene-phenotype annotations (278,553 gene-phenotype associations for 41,905 mutant alleles and 10,744 phenotypes) have great potential for virtual phenotypic screening for drug discovery 21 – 23 . In this study, we used gene-phenotype associations from MGD to assess the functional effects of top ranked microbial metabolites on CRC-related phenotypes.…”

Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

“…The same modeling strategy used with bevacizumab can be applied to simulate the effect of other drugs. Recently, a drug repurposing in silico experiment which combines human genomic data with mouse phenotypes has suggested the possible utility of a number of drugs with different indications (see Table 3) for potential glioblastoma treatment (60). The intensity and the degree of heterogeneity in the response are very variable across the 8 drugs tested here.…”