Genomic approaches to pre-clinical drug target prioritisation in immune-related traits

Le résumé fourni par la source

Drug discovery is time-consuming and expensive; bringing a novel compound to regulatory approval can take several years and billions of dollars. A significant portion of costs stem from compounds that have failed in clinical development, for reasons including a lack of clinical efficacy. Simultaneously, the emergence of genomic approaches like genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) has enabled better functional understanding of human genetics and robust mapping of effects of gene perturbations on phenotype. This thesis focuses on the aggregation of genomic approaches for the purposes of drug target prioritisation in pre-clinical stages of development, in immune-related traits. The Priority Index (Pi) approach combines GWAS knowledge with cell-specific eQTLs, promoter-capture Hi-C, ontologies, and network information to prioritise genes in a disease-specific manner, using solely genetics (’discovery mode’) or combining genetic data, clinical trial information, and machine learning (’supervised mode’). The outputs of Pi are computationally validated and benchmarked using multiple sclerosis as a case example, and the performance of Pi as a drug discovery prioritisation pipeline is quantified. Pi is further implemented in the context of myasthenia gravis, and analysis is conducted at the gene, pathway, and network-level to develop an understanding of drivers of pathogenesis, functionally-relevant cell types, and genetic differences between early and late onset forms of disease. Moreover, the outputs are refined through statistical fine-mapping, eQTL, and colocalisation analysis, in addition to comparison with externally-generated datasets. Lastly, experimental validation is conducted to test hypotheses generated by Pi analysis. Specifically, prioritised targets for Dupuytren’s disease and systemic lupus erythematosus are examined via epigenetic probe inhibition and siRNA knockdown, within the context of patient-derived tissue. Effects are interrogated from the perspective of both gene expression and cellular phenotype/function. This thesis demonstrates the utility of integrative genomics for target prioritisation, highlighting the potential for informing and enabling pre-clinical drug discovery.

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