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

Ruowang Li, Dokyoon Kim, Heather E. Wheeler, Scott M. Dudek, M. Eileen Dolan, Marylyn D. Ritchie

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)178-190
Number of pages13
JournalPharmacogenomics Journal
Issue number2
StatePublished - Apr 1 2019

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Pharmacology


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