BackgroundDue to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.ResultsIn this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.ConclusionsThis work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.
Heparin, a widely used anticoagulant, carries the risk of an antibody mediated adverse drug reaction, heparin-induced thrombocytopenia (HIT). A subset of heparin-treated patients produces detectable levels of antibodies against complexes of heparin bound to circulating platelet factor 4 (PF4). Using a genome-wide association study (GWAS) approach, we aimed to identify genetic variants associated with anti-PF4/heparin antibodies that account for the variable antibody response seen in HIT. We performed a GWAS on anti-PF4/heparin antibody levels determined via polyclonal enzyme-linked immunosorbent assays (ELISA). Our discovery cohort (n=4237) and replication cohort (n=807) constituted patients with European ancestry and clinical suspicion of HIT with cases confirmed via functional assay. Genome-wide significance was considered at α=5x10-8. No variants were significantly associated with anti-PF4/heparin antibody levels in the discovery cohort at a genome-wide significant level. Secondary GWAS analyses included identification of variants with suggestive associations in the discovery cohort (α=1x10-4). The top variant in both cohorts was rs1555175145 (discovery β=-0.112[0.018], p=2.50x10-5; replication β=-0.104[0.051], p=0.041). In gene set enrichment analysis (GSEA), three gene sets reached false discovery rate-adjusted significance (q<0.05) in both discovery and replication cohorts: "Leukocyte Transendothelial Migration," "Innate Immune Response," and "Lyase Activity." Our results indicate that genomic variation is not significantly associated with anti-PF4/heparin antibody levels. Given our power to identify variants with moderate frequencies and effect sizes, this evidence suggests genetic variation is not a primary driver of variable antibody response in heparin-treated patients with European ancestry.
Heparin-induced thrombocytopenia (HIT) is an unpredictable, complex, immune-mediated adverse drug reaction associated with a high mortality. Despite decades of research into HIT, fundamental knowledge gaps persist regarding HIT likely due to the complex and unusual nature of the HIT immune response. Such knowledge gaps include the identity of a HIT immunogen, the intrinsic roles of various cell types and their interactions, and the molecular basis that distinguishes pathogenic and non-pathogenic PF4/heparin antibodies. While a key feature of HIT, thrombocytopenia, implicates platelets as a seminal cell fragment in HIT pathogenesis, strong evidence exists for critical roles of multiple cell types. The rise in omic technologies over the last decade has resulted in a number of agnostic, whole system approaches for biological research that may be especially informative for complex phenotypes. Applying multi-omics techniques to HIT has the potential to bring new insights into HIT pathophysiology and identify biomarkers with clinical utility. In this review, we review the clinical, immunological, and molecular features of HIT with emphasis on key cell types and their roles. We then address the applicability of several omic techniques underutilized in HIT, which have the potential to fill knowledge gaps related to HIT biology.
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