Leveraging Human Genetics to Guide Cancer Drug Development.
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Embargo End Date
Authors
Kinnersley, B
Sud, A
Coker, EA
Tym, JE
Di Micco, P
Al-Lazikani, B
Houlston, RS
Sud, A
Coker, EA
Tym, JE
Di Micco, P
Al-Lazikani, B
Houlston, RS
Document Type
Journal Article
Date
2018-11-21
Date Accepted
2018-09-11
Abstract
PURPOSE: The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology. MATERIALS AND METHODS: Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability. RESULTS: For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs. CONCLUSION: The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.
Citation
JCO clinical cancer informatics, 2018, 2 pp. 1 - 11
Source Title
Publisher
AMER SOC CLINICAL ONCOLOGY
ISSN
2473-4276
eISSN
2473-4276
Collections
Research Team
Computational Biology and Chemogenomics
Cancer Genomics
Cancer Genomics