Goode, Ellen L.

Abstract Background Ovarian cancer is the fifth most common cause of cancer death in women in the US; approximately 15,000 women will die from this disease in 2011 (ACS, 2011). The 5 year survival rate for stage IV disease is only 18%. Only a handful of independent significant prognostic factors are known, including tumor stage at surgery, size of residual tumor at the end of primary surgery, and age at diagnosis. Understanding how the variation of cytosine methylation in the context of cytosine-guanine dinucleotides (CpGs) affects complex disease etiology and survival is the current focus of many ongoing research studies. We conducted an epigenome-wide study of methylation in ovarian cancer cases to evaluate whether variation in methylation of CpG sites in genomic DNA may be associated with ovarian cancer survival. Methods Subjects included cases from an ongoing case-control study. The Mayo Clinic Ovarian Cancer Study is a clinic-based study that recruits ovarian cancer cases within 12 months of diagnosis. Pre-treatment, germline DNA from lymphocytes from 137 cases was bisulfite-modified, amplified and genotyped on the Illumina 450K methylation chip and included in these analyses. Quality control (QC) analyses include the elimination of all methylation probes with a single nucleotide polymorphism (SNP) or insertion/deletion (in/del) at the same location; removal of non-specific probes; and estimation of bisulfite conversion rates using embedded control probes. Planned analyses include assessment of clinical covariates (age, stage, extent of surgical debulking, grade, presence of ascites) to determine which are associated with overall survival in our data. Additional analyses will assess the age- and multi-variate-adjusted association of each methylation probe with overall survival using Cox proportional hazards regression models with each methylation probe modeled as a continuous variable. Data will be summarized using Manhattan plots. These analyses will be repeated using ‘time to recurrence’ as the outcome variable, with recurrence defined as time to ovarian cancer recurrence/progression or death. Results Bioinformatics analyses suggest that 36,659 probes were located at the site of a SNP or in/del and should be removed from future analyses. Another 448 probes had a detection rate of p< 0.01 and 6630 probes had signals for the negative control samples; all were removed from analyses. A total of 43,802 probes were removed. In addition, ten cases were excluded due to either poor bisulfite conversion (N=9) or extremely low raw methylation values across all probes (N=1). This leaves a total of 127 cases for analyses. Results from planned analyses will be presented, including estimation of the association of methylation probes with both overall survival and time to recurrence. Conclusions Data to be presented will describe whether there is any evidence that methylation of genomic DNA in ovarian cancer cases is associated with outcome of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5008. doi:1538-7445.AM2012-5008

Abstract A substantial proportion of familial colorectal cancer (CRC) is not a consequence of known susceptibility loci, such as mismatch repair (MMR) genes, supporting the existence of additional loci. To identify novel CRC loci, we conducted a genome-wide linkage scan in 356 white families with no evidence of defective MMR (i.e., no tumors with loss of expression of MMR proteins; no microsatellite instability (MSI)-high tumors, and no evidence of linkage to MMR genes). This represents the largest linkage analysis of proficient MMR (pMMR) familial CRC to date. Families were ascertained via the NCI-supported Colon Cancer Family Registry multi-site (Colon CFR) consortium, the City of Hope, and Memorial University of Newfoundland. A total of 1,612 individuals (average 5.0 per family, range 2-10 with an average of 2.2 affected and 2.8 unaffected individuals) were genotyped using genome-wide single nucleotide polymorphism linkage arrays; parametric and non-parametric linkage analysis used MERLIN in a priori-defined family groups. Five lod scores greater than 3.0 were observed, accounting for heterogeneity, in four chromosomal regions. The greatest lod scores were at 4q21.1 among families with mean age of diagnosis less than 50 years (dominant HLOD=4.51, α=0.84, 145.40 cM, rs10518142) and at 12q24.32 among all families (dominant HLOD=3.60, α=0.48, 285.15 cM, rs952093). Among families with four or more affected individuals and among clinic-based families, a common peak was observed at 15q22.31 (101.40 cM, rs1477798; dominant HLOD=3.07, α=0.29; dominant HLOD=3.03, α=0.32, respectively). Analysis of families with only two affected individuals yielded a peak at 8q13.2 (recessive HLOD=3.02, α=0.51, 132.52 cM, rs1319036). These linkage regions comprise previously unreported loci, supporting the hypothesis that novel loci contribute to CRC in familial pMMR cases. These loci are also largely distinct from those identified in case-control GWAS. To follow up on these findings, a custom Agilent Sure Select Target Enrichment panel has been designed for genes in three of these regions, in order to sequence probands using an Illumina HiSeq 2000. This study demonstrates the utility of family-based data and provides evidence for additional alleles for familial CRC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2930. doi:1538-7445.AM2012-2930

Abstract Mi(cro)RNAs are short non-coding RNA molecules that play a key role in carcinogenesis by regulating tumor suppressors and oncogenes. Published data has implicated miRNAs in ovarian cancer (OC) development and progression, and we hypothesize that single nucleotide polymorphisms (SNPs) in sites of miRNA: messenger RNA (mRNA) binding may influence OC risk by altering expression levels of targeted mRNAs. To evaluate associations between SNPs in miRNA binding sites and OC risk, we used bioinformatics tools and public databases to identify miRNA binding SNPs relevant to ovarian cancer. We then evaluated the frequency of approximately 1,100 SNPs in ∼20,000 epithelial OC cases and 20,000 controls represented in the international Collaborative Oncological Gene-environment Study. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) between genotypes and case status, with adjustment for the first five principal components representing European ancestry. Log-additive genetic models were applied to each SNP, modeling the number of copies of the minor allele. Subgroup analysis was conducted for serous adenocarcinomas, the most predominant histologic subtype of epithelial OC. Preliminary analysis of 11,215 invasive epithelial OCs and 15,910 controls included four SNPs with P<10−6 and 1 SNP with P<10−5. Analysis of 6,215 invasive serous adenocarcinomas revealed two SNPs with P<10−7 and three SNPs with P<10−6. Noteworthy is rs6104808, a C>T SNP predicted to reside in a highly conserved miRNA binding site of BTBD3 (BTB POZ domain containing 3), a transcription factor suggested to be involved in cell proliferation, cancer progression, and response to platinum-based chemotherapy. rs6104808 was associated with a decreased risk of OC overall (OR (95% CI): 0.42 (0.28-0.62), P=1.26 x 10−5)), and the association was stronger among cases with serous adenocarcinoma (OR (95% CI): 0.30 (0.17-0.50), P=9.89 x 10−6)). This represents the largest, most comprehensive epidemiologic study to date to evaluate associations between miRNA binding site SNPs and OC susceptibility. Although based on a preliminary analysis, these data suggest a significant role and putative biological mechanism for common germline variants in miRNA binding sites leading to cancer development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2927. doi:1538-7445.AM2012-2927

Abstract Ovarian cancer is the leading cause of death in gynecologic malignancies in the United States with most patients presenting with late stage disease; patients receive the same treatment regardless of histology or tumor characteristics. The mismatch repair (MMR) molecular pathway is well-defined and known to be involved in up to 20% of ovarian cancers; however, association of deficient v proficient MMR status (dMMR v pMMR status) with risk factors and outcome is not well understood. We are conducting extensive molecular testing of the MMR genes MLH1, MSH2, MSH3, MSH6, and PMS2 on Mayo Clinic invasive epithelial ovarian cancer cases. First, the prevalence of dMMR tumors will be estimated in ovarian cancer cases (n=496) based on loss of expression of MLH1, MSH2, MSH3, MSH6, and PMS2 determined by immunohistochemistry staining of primary antibodies using negative and positive controls. Staining of tissue microarrays (TMAs) is complete; triplicate cores will be scored independently for presence or loss of protein expression by a board-certified pathologist. Second, for those dMMR cases, the possible cause of MMR deficiency will be evaluated considering germline and somatic mutations. The loss of expression of each protein will be characterized as germline if a deleterious mutation is present in peripheral blood or somatic if the pathogenic mutation is only present in tumor tissue. In cases with MLH1 loss somatic hypermethylation will also be considered. Analysis of germline mutation data will include all 496 cases and somatic mutations data will include a subset of cases tested with frozen tumor DNA (n=292) using Fluidigm access array targeted resequencing. Third, the clinical features of dMMR disease will be compared to those of pMMR; in particular, age at diagnosis, histopathology, and disease outcome. Among cases in this analysis, the majority have high-grade serous disease with a mean age at diagnosis of 61 years, about half had recurrence or progression (n=243) of which two-thirds (n=167) are deceased. Finally, known and suspected ovarian cancer risk factors (family history, smoking, body mass index, parity, use of hormone therapy, and genetic variation in susceptibility loci at 2q31, 3q25, 8q24, 9p22, 17q21, and 19p13) will be analyzed by MMR phenotype. Risk factor questionnaire data is available on 462 cases (93%), as well as 737 population controls with germline genome-wide genotype data. About half of the cases (n=231) report a 1st or 2nd degree family member with breast or ovarian cancer. Given the limited knowledge about ovarian cancer etiology and the poor prognosis of advanced disease, it is very important to study risk factors with this molecular subtype. The ovarian cancer dMMR phenotype may have distinct clinicopathological features that could guide risk stratification and be useful in diagnostics and therapeutics. This is the largest study characterizing MMR in an ovarian cancer population to date. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2619. doi:1538-7445.AM2012-2619

Abstract As pathways of the immune system play an important role in multiple malignancies, we assessed the association between single-nucleotide polymorphisms (SNPs) in inflammation-related genes and risk of ovarian cancer in a multi-site case-control study. Using a custom array, we genotyped 930 epithelial ovarian cancer cases and 1,037 controls for SNPs in ALOX12, ALOX15, ALOX5, CCL11, CCL2, CCL3, CCR3, CRP, CXCL16, IL10, IL15RA, IL18, IL1A, IL1B, IL1RN, IL4R, IL6, IL6R, IL7R, IL8RA, IL8RB, IL9, NOS3, PTGS1, PTGS2, TLR2, and TNF. After analysis by logistic regression, SNPs with p<0.10 were evaluated in an independent data set of 3,143 cases and 2,102 controls from the Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI) collaboration. Combined analysis revealed association with interleukin 1, alpha (IL1A) SNPs (rs17561 and rs4848300, r2=0.990, p=0.005) which varied by histological subtype (heterogeneity p=0.03). For example, IL1A rs17561, which results in a missense change in amino acid at position 114 and correlates with multiple inflammatory phenotypes, was inversely associated with risk of clear cell, mucinous, and endometrioid subtypes, but not with the serous subtype (Table 1). Additional data on IL1A rs17561 in an even larger collection of ovarian cancer cases and controls are pending and will be analyzed soon. We also found that genotype at rs1864414 in ALOX5 (arachidonate 5-lipoxygenase) was associated with decreased risk combined across subtypes (OR 0.86, 95% CI 0.78-0.95; p=0.002). Thus, inherited variation in IL1A and ALOX5 appears to have a role in ovarian cancer risk, though it is limited to rarer subtypes for IL1A. These data add to existing literature on the importance of inflammation in tumorigenesis and growing evidence of subtype-specific traits in ovarian cancer and may provide important clues on prevention. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2603. doi:1538-7445.AM2012-2603

Abstract Rare germline mutations in the breast and ovarian cancer predisposition genes BRCA1 and BRCA2 are present in roughly 5 percent of ovarian cancer patients. Both genes play key roles in DNA damage repair but appear to have distinct, although often complementary, functions. The risks of breast and ovarian cancer have been shown to differ between BRCA1 and BRCA2 carriers, and mutation-specific effects have also been suggested for both groups. Published reports comparing the survival of ovarian cancer patients with and without BRCA1/2 mutations generally show a survival advantage among mutation carriers. This is thought to be related to an improved response of carriers to platinum-based therapies. Since it is not known whether BRCA1 and BRCA2 carriers show similar survival patterns, we have performed a large, multicenter study to investigate the impact of germline BRCA1 and BRCA2 mutations on ovarian cancer survival. 3,531 invasive epithelial ovarian cancer cases (1,178 BRCA1, 367 BRCA2 and 1,986 BRCA-negative) with survival time data from twenty-four studies in the U.S.A, Europe, Israel and Asia were included. In our main analysis, we excluded patients who were known or who were likely to have not received platinum-based therapy. Compared to non-carriers, BRCA1 and BRCA2 carriers were more likely to present with advanced stage (BRCA1; p=2×10−4, BRCA2; p=4×10−6), high grade (BRCA1; p=4×10−9, BRCA2; p=1×10−4), serous disease (BRCA1; p=3×10−6, BRCA2; p=0.003). BRCA1 carriers were younger at diagnosis (p=2×10−9) and BRCA2 slightly older at diagnosis (p=0.002) than non-carriers. BRCA1 and BRCA2 carriers did not differ in terms of tumor stage, grade or histology but did show differences in the age at diagnosis (p=2×10−14). In an unadjusted analysis, neither BRCA1 nor BRCA2 carriers differed from non-carriers in overall survival (BRCA1: HR=1.02, p=0.77, BRCA2: HR=0.89, p=0.36). After adjusting for stage, grade, histology and age at diagnosis, BRCA1 carriers showed a modest, non-significant, survival advantage (HR=0.84, p=0.12) while BRCA2 carriers showed a marked improvement (HR=0.57, p=6×10−4) compared to non-carriers. This survival advantage was also seen when comparing BRCA2 carriers to BRCA1 carriers after adjustment for age at diagnosis (HR=0.69, p=5×10−4). Similar results were obtained when we restricted the analysis to high grade, advanced stage, serous cases. The survival differences we observed between BRCA1 and BRCA2 carriers could be related to differences in tumor biology or chemo-sensitivity. Additional analyses investigating the later will be presented in addition to mutation-specific effects within BRCA1 and BRCA2. In this study, we provide strong evidence for genetic heterogeneity of survival effects in ovarian cancer patients with BRCA mutations. This could be applied to both clinical prediction and to aid our understanding of the complex biology of BRCA mutations. 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 2752. doi:10.1158/1538-7445.AM2011-2752

Abstract Several platforms for genome sequencing and methods for exon enrichment are now available and continue to evolve quickly. In this study, we compared the performance of whole exome sequencing using two instruments, Illumina's GAIIx and HiSeq 2000, comparing single versus duplicate runs. Eight runs of eight samples each were performed with genomic DNA derived from whole blood and sample libraries prepared with the Agilent SureSelect capture array. One run was conducted in duplicate on the GAIIx, and, for some analyses, the combined data were used. We used TREAT (Targeted RE-sequencing and Annotation Tool), developed in-house for data analysis, including sequence alignment (MAQ, BWA), local re-alignment (GATK), variant calling (utilizing MAQ), annotation (SIFT and Seattle Seq), and visualization. Overall, duplicate runs on the GAIIx platform (compared to a single run) increased the number of total reads by approximately two-fold: from ∼66 M to ∼130 M. For both single and duplicate runs, ∼90% of reads mapped to the reference sequence, and ∼55% of reads mapped on-target. Of importance, the percent coverage of the target region increased substantially with duplicate runs; ∼72% of the target region was covered at 40-fold or more when run in duplicate compared to only ∼52% when run once. At 30x, 20x, and 10x, there was a 1.3- (62% v. 78%), 1.2- (72% v. 84%), and 1.1-fold (84% v. 91%) increase respectively, in the percent coverage of the target region for samples run twice. A similar number of filtered, on-target SNPs per sample (∼24 K) was found for both single and duplicate run analyses. However, a 1.3-fold increase in the number of on-target indels was seen in the duplicate run (∼1,200) compared to the single run (∼900). When the combined data for samples run twice on a GAIIx were compared to the samples run using a HiSeq, the total number of reads were similar (∼130 M), although several of the samples had substantially more reads on the HiSeq platform. Samples run on a HiSeq had an increase in the percent coverage of the target region at 40x (72% v. 81%), 30x (78% v. 86%), 20x (84% v. 91%) and 10x (91% v. 95%) compared to the two-run approach on a GAIIx. A similar number of filtered on-target SNPs (∼24 K/sample) and indels (∼1200) was found with the HiSeq compared to two runs on a GAIIx. In summary, our results demonstrate increases in the total number of reads and in overall coverage with the HiSeq 2000 instrument. The total number of SNPs and indel that mapped on target for the version 1 Agilent SureSelect capture array is ∼25 K and 1,200 per sample, respectively. As capture is of variable efficiency for individual runs, higher average coverage of the target region is necessary for sufficient coverage of poorly captured regions. Additional analyses comparing Agilent SureSelect versions 1 and 2 arrays are underway. 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 2742. doi:10.1158/1538-7445.AM2011-2742

Abstract Epithelial ovarian cancer (EOC) is the 6th most commonly diagnosed cancer among women in the world, accounting for 4% of all female cancers. On a worldwide basis, an estimated 204,000 new cases are diagnosed and 125,000 women die of EOC annually, with 5-year survival rate at 45%. Metastasis and its complications are the usual cause of EOC-related deaths. Thus, factors related to tumor aggressiveness, response to therapy, and the underlying health state of the patient often predict survival. Candidate gene and genome-wide association studies (GWAS) for survival from EOC have discovered only a few the genetic variants, accounting for only a small proportion of the heritability. One explanation for the missing heritability is that the common analysis approach _ assessing the effect of each single nucleotide polymorphism (SNP) individually _ is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) overcomes this limitation by assessing the overall evidence of association of a phenotype with all measured variation in a set of genes or pathway. Therefore, we conducted a GSA using data from an EOC GWAS, using the Illumina 610-quad SNP array, to determine novel gene sets (GSs) associated with overall survival (OS) from EOC (N cases = 1057, N deaths = 495). All subjects were non-Hispanic, non-Jewish Caucasians, with sample and SNP call rates >95%. Subjects with ambiguous gender, unresolved identical genotypes, and less than 80% European ancestry were also excluded. SNPs were mapped to genes within 20 kb, with genes then mapped to GSs defined by the following: KEGG, Gene Ontology (biological, cellular, molecular) and PharmGKB. GSA was completed in which the association of each gene with OS was assessed using a principal component gene-level test (adjusting for study site, age and population sub-structure based on the first eigenvector from EigenStrat), followed by the aggregation of the gene-level p-values using the Gamma method with a soft truncation threshold of 0.15. Empirical p-values were determined using permutation methods. Of the 2,573 GSs assessed, 30 GSs had a p-value < 0.01. The top GSs were related to: mismatch repair (p = 3.0E-4), platelet formation (p=0.001), leukocyte adhesive activation (p=0.002), antigen processing and presentation of endogenous antigens (p = 0.002) or peptide antigens (p = 0.002), platinum pathway (p = 0.003), aging and lifespan (p = 0.004, p = 0.007), immune system development (p = 0.004) and somatic diversification (p=0.007). These results illustrate the usefulness of GSA for the development of novel hypotheses that can be followed-up with additional clinical or functional studies. Moreover, application of GSA to survival from EOC allowed the detection of significant GSs (and the corresponding genes/SNPs within these GSs) that impact OS that were not detected by assessing each SNP individually, where the smallest p-value from single SNP analysis of the GWAS for OS was 1.3E-05. 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 2738. doi:10.1158/1538-7445.AM2011-2738

Abstract The etiology of ovarian cancer is poorly understood but there is clearly a heritable component. Efforts to identify susceptibility alleles rarely consider interactions among alleles or their joint effects because it quickly becomes computationally intractable. In this study, we sought to identify multi-SNP effects jointly with a pathway-based analysis (GSEA-SNP) of 1,952 EOC cases and 2,042 frequency-matched controls genotyped with the Illumina 610K array. All subjects were self-reported non-Hispanic non-Jewish Caucasians, with SNP and sample call rates > 95%. Subjects with ambiguous gender, unresolved identical genotypes and < 80% European ancestry were excluded. SNPs with MAF < 1% were excluded. Missing genotypes were inferred using Mach based on the HapMap CEU population. SNPs not within introns were annotated to genes within 100 bp. We retrieved the following databases of gene sets (GSs) from a compiled database, MsigDB: human chromosome and cytogenetic band, chemical and genetic perturbations, canonical pathways, microRNA binding targets, transcription factor targets (TFT), cancer gene neighborhood (CGN), Cancer modules and Gene Ontology. The GSEA-SNP approach calculates and rank orders the trend statistic for association between each SNP and EOC risk. The Enrichment Score (ES) estimates the overrepresentation of top-ranked SNPs for each GS; the statistical significance was estimated using 10,000 permutations. The ES was normalized according to the size of GS to yield Normalized Enrichment Score (NES). The false discovery (FDR) rate was estimated using NES to adjust for multiple hypothesis testing. A total of 5181 gene-sets were included in the analysis. When controlling the FDR at 15%, 14 of the GSs were highly enriched with association signals, including two chromosomal regions, 8q13 (p = 0.0055, FDR = 0.15) and 6p24 (p = 0.0012, FDR = 0.08). BIOCARTA_LYM_PATHWAY, a pathway related to cell adhesion and diapedesis of lymphocytes, was also significant (p = 0.0002, FDR = 0.12). A TFT GS, composed of genes with promoter regions around transcription start sites containing the motif GGCNRNWCTTYS, was associated with risk (p = 0.0004, FDR = 0.08). Currently, no known transcription factors bind to this computationally predicted motif. Molecular function of double stranded RNA binding was also enriched (p = 0.005, FDR = 0.10). The remaining enriched sets were CGN sets, for which the neighborhoods around the cancer genes were originally defined using correlation of gene expression from 4 large data sets of various cancer types. The enriched CGN included: CD48 (p = 0.011), CD53 (p = 0.009), CD97 (p = 0.011), INPP5D (p = 0.010), PTPN6 (p = 0.008), VAV1 (p = 0.011), ITGAL (p = 0.012), PTPRC (p = 0.012), and STAT6 (p = 0.010). In summary, these analyses detected biologically plausible GSs related to etiology of EOC, highlighting SNPs in core enrichment groups that were not identified using individual SNP tests. 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 4729. doi:10.1158/1538-7445.AM2011-4729

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