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Pharmacogenomics J. 2019 Apr;19(2):178-190. doi: 10.1038/s41397-018-0024-6. Epub 2018 May 25.

Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity.

Author information

1
Bioinformatics and Genomics program, Pennsylvania State University, University Park, Pennsylvania, USA.
2
Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
3
Biomedical and Translational Informatics, Geisinger, Danville, Pennsylvania, USA.
4
Departments of Biology and Computer Science, Loyola University Chicago, Chicago, Illinois, USA.
5
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
6
Department of Medicine, University of Chicago, Chicago, Illinois, USA.
7
Bioinformatics and Genomics program, Pennsylvania State University, University Park, Pennsylvania, USA. marylyn@pennmedicine.upenn.edu.
8
Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. marylyn@pennmedicine.upenn.edu.
9
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. marylyn@pennmedicine.upenn.edu.

Abstract

Identifying genetic variants associated with chemotherapeutic induced toxicity is an important step towards personalized treatment of cancer patients. However, annotating and interpreting the associated genetic variants remains challenging because each associated variant is a surrogate for many other variants in the same region. The issue is further complicated when investigating patterns of associated variants with multiple drugs. In this study, we used biological knowledge to annotate and compare genetic variants associated with cellular sensitivity to mechanistically distinct chemotherapeutic drugs, including platinating agents (cisplatin, carboplatin), capecitabine, cytarabine, and paclitaxel. The most significantly associated SNPs from genome wide association studies of cellular sensitivity to each drug in lymphoblastoid cell lines derived from populations of European (CEU) and African (YRI) descent were analyzed for their enrichment in biological pathways and processes. We annotated genetic variants using higher-level biological annotations in efforts to group variants into more interpretable biological modules. Using the higher-level annotations, we observed distinct biological modules associated with cell line populations as well as classes of chemotherapeutic drugs. We also integrated genetic variants and gene expression variables to build predictive models for chemotherapeutic drug cytotoxicity and prioritized the network models based on the enrichment of DNA regulatory data. Several biological annotations, often encompassing different SNPs, were replicated in independent datasets. By using biological knowledge and DNA regulatory information, we propose a novel approach for jointly analyzing genetic variants associated with multiple chemotherapeutic drugs.

PMID:
29795408
DOI:
10.1038/s41397-018-0024-6
[Indexed for MEDLINE]

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