Abstract
Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients' clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC50) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein-protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.