Fazio, Anna de

Abstract We performed a multi-stage genome-wide association study (GWAS) for drug response in ovarian cancer. We selected 313 chemotherapy treated patients from the Australian Ovarian Cancer Study (AOCS). Patients were treated with paclitaxel/carboplatin and the trait of interest was Progression Free Survival (PFS). We maximized power for a fixed genotyping budget by genotyping individuals from the tails of the distribution: 94 with good survival and 94 with poor survival, taking into account extent of residual disease following surgery. Patients were genotyped on Illumina HumanOmni1-Quad arrays (1140419 loci).Two further cohorts with measures of PFS and whole genome data available; 134 ovarian patients from the Mayo clinic (genotyped on Illumina HumanHap 610 arrays, 620901 loci) and 68 patients from The Cancer Genome Atlas ovarian cancer set (genotyped on Illumina Human1M arrays, 1199187 loci) were meta-analyzed with the AOCS set (stage 1). We have imputed all the stage 1 samples with reference of the 1000 Genome data to allow more direct comparison of SNPs not common to all of the arrays used (up to 6 million SNPs with good imputation quality). Based on the chemotherapy treatment received by each patient, we assigned the patients into primary and secondary analysis, with primary analysis using a more strictly defined treatment subgroup. We followed up the stage 1 findings by genotyping 26 SNPs in more than 3,000 samples collected from 8 study sites of Ovarian Cancer Association Consortium (OCAC). These 26 SNPs were mainly genotyped SNPs, chosen from 23 distinct regions showing the strongest association with chemotherapy response from either primary or secondary analysis. 2 SNPs showed nominal significance in the replication sample (chr 12: rs1861932, chr 22: rs708444). We are currently genotyping more regions to seek replication of the initial findings. In summary, we sought to identify genes that underlie individual differences in chemoresistance. In the future, typing variations in these genes will be important for personalizing drug regimes for patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4679. doi:10.1158/1538-7445.AM2011-4679

Abstract Purpose: Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy. Experimental Design: We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas. Results: In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P < 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (Pper-allele = 2 × 10−2), with a stronger effect in the subset of women with optimally debulked tumors (Pper-allele = 4 × 10−3). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (Pper-allele = 9 × 10−3). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts. Conclusion: This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect. Clin Cancer Res; 17(16); 5490–500. ©2011 AACR.