Gene set analysis of survival following ovarian cancer implicates macrolide binding and intracellular signaling genes

Abstract

Background

Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single nucleotide polymorphism (SNP) individually may not be well-suited to detect small effects of multiple SNPs, such as those operating within the same biological pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes.

Methods

To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWASes (N cases = 2,813, N deaths = 1,116), with a novel Principal Component – Gamma GSA method. Analysis was completed for all cases and then separately for high grade serous (HGS) histological subtype.

Results

Analysis of the HGS subjects resulted in 43 gene sets with p<0.005 (1.7%); of these, 21 gene sets had p < 0.10 in both GWASes, including intracellular signaling pathway (p = 7.3 × 10⁻⁵) and macrolide binding (p = 6.2 ×10⁻⁴) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (p=6.3 ×10⁻⁴) and macrolide binding (p=1.0×10⁻³). Of 18 gene sets with p<0.005 (0.7%), eight had p < 0.10 in both GWASes.

Conclusion

This research detected novel gene sets associated with EOC survival.

Impact

Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies.

Introduction

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer mortality among women in the United States, accounting for five percent of cancer deaths (1). Most patients are diagnosed with advanced disease, and for, the three-quarters of women diagnosed with stage III or IV disease, the likelihood of long-term disease-free survival is less than 20 percent (2–4). Stage, grade, and other clinical features of disease, such as degree of debulking and presence of ascites, are key to predicting prognosis; however, much variation in outcome is unexplained. As women may inherently vary in their ability to eradicate disease or tolerate treatment, genetic association studies have sought to identify inherited variants related to outcome. Candidate gene studies of angiogenesis, inflammation, or chemoresistance pathways show promising results, although not always consistently across populations (5–7). Similarly, genome-wide association studies (GWAS) of ovarian cancer have not yet found outcome-associated loci despite large sample sizes and comprehensive coverage of common genomic variation (8).

One explanation for the lack of findings from GWAS is that the analysis strategy commonly used, testing for association of the phenotype with each SNP individually, is not well-suited for detecting multiple variants with small effects (9–12). The application of novel methods which incorporate biological knowledge into analyses of GWAS data has proven useful to many studies (13, 14). One approach is gene set analysis (GSA) which assesses the overall evidence of association of a phenotype with all measured variation in a pre-defined set of genes, such as a biological pathway (15–17). A gene set is simply any user-defined group of genes; for example, with GWAS data, GSA allows for the use of standardized biological classifications, such as those from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Because numerous genes can be combined into a limited number of gene sets for analysis, the multiple-testing burden is greatly reduced. We have recently shown that the PC-GM approach to modeling SNPs within genes using principal component (PC) analysis (18) and then combining gene-level p-values using the Gamma method (GM) (19) is a powerful GSA method (20). Therefore, in order to identify novel avenues for investigation of this lethal condition, we conducted a GSA of EOC overall survival using the PC-GM approach and data from two large ovarian cancer GWASes.

Methods and Materials

Study Participants

GSAs were conducted using data from two independent multi-site ovarian cancer GWASes. The North American GWAS data was derived from three case-control studies of EOC, as described previously (21), including: the Mayo Clinic Ovarian Cancer Study (MAY) (Rochester, MN), the North Carolina Ovarian Cancer Study (NCO) (Durham, NC), and the Tampa Bay Ovarian Cancer Study (TBO) (Tampa, FL). NCO and TBO used population-based ascertainment with linkage to state cancer registries and the National Death Index; MAY was clinic-based and linked to medical records and the National Death Index.

The UK GWAS has also been described in detail (8, 22) and included invasive epithelial ovarian cancer cases from four studies: SEARCH Ovarian Cancer Study (SEA) (Cambridge, UK), United Kingdom Ovarian Cancer Population Study (UKO) (London, UK), Cancer Research UK Familial Ovarian Cancer Register (UKR) (London, UK), and Royal Marsden Hospital (RMH) (London, UK). These studies recruited cases via regional and nationwide registries with follow-up via linkages to national vital statistics. Study protocols were approved by an institutional review board or ethics committee at each center, and all study participants provided written informed consent.

Genotyping, Quality Control, Definition of Gene Sets

Although both GWASes used the Illumina Infinium 610K array, genotyping and quality control was performed separately. Samples were removed with call rate <95%, ambiguous gender, unresolved identical genotypes, self reported as non-Caucasian, or predicted by STRUCTURE (23) analysis to have less than 80% European ancestry. SNPs were excluded from each GWAS with call rate < 95%, Hardy-Weinberg Equilibrium (HWE) p-value < 10⁻⁴, or no variation. Genotypes were coded as 0, 1 or 2 in terms of the number of minor alleles present.

SNPs were mapped to genes based on physical location; in particular, if they resided within 20 kb of the 5′ or 3′ end of a gene based on RefSeq Build 29 (NCBI genome build 36.3). This allowed for a SNP to be mapped to multiple genes and did not consider LD blocks or gene lengths. Genes were then mapped to gene sets using the following standarized sources: the Gene Ontology (GO) project (24) which categorizes genes by function using biological, cellular, and molecular schemes (“GO:BIO, “GO:CELL”, “GO:MOLE”), the Kyoto Encyclopedia of Genes and Genomes (KEGG) (25), and PharmGKB (26) which group genes into biological pathways. GO uses a hierarchical ontology that classifies genes and therefore a level of the hierarchy (or specificity) must be specified for defining gene sets. We relied on Level 4 to determine GO gene sets, as a compromise between specificity and sensitivity. Across these sources, we identified a total of 2,566 gene sets containing approximately 16,500 unique genes. Supplementary Table 1 summarizes the numbers of SNPs, genes, and gene sets by source for the GSA.

Gene Set Analysis using PC-GM method

To assess association between predefined gene sets with overall survival from EOC, we performed GSA of each GWAS and combined results using Fisher’s method, a meta-analytic technique. We used a self-contained method which tests the null hypothesis H_o: SNPs/genes in the gene set of interest are NOT associated with the phenotype versus the alternative hypothesis H_a: SNPs/genes in the gene set are associated with the phenotype. GSA was completed in two steps, first using a principal component analysis (PCA) (18) in combination with a model of the phenotype, then a summarization step using the Gamma method (19), a generalization of Fisher’s method (27), which we refer to as the PC-GM method. Due to computational issues, redundant markers within genes (r² = 1.0) were removed prior to PCA (28). For the first step in a two-step GSA (assessing gene-level associations), the PCs that explained 80% of the genetic variation within each gene were used to assess the significance of the gene with overall survival. For the gene-level analysis, the average number of PCs included in the analysis was 3.91 for North American analysis (4.75 for North American HGS cases) and 3.74 for UK analysis (3.82 for UK HGS cases). Gene-level association testing was completed using Cox proportional hazards regression (29) considering time from date of diagnosis to death with censoring at last follow-up. We accounted for the existence of left truncated data due to delayed enrollment of some cases (average 94.4 days between diagnosis and enrollment) using the Cox regression start-stop follow-up approach (30). Covariates included age at diagnosis, study site, and the first eigenvector adjusting for possible population stratification analysis from a PCA of the genome-wide SNPs using EigenSTRAT (31). Exploratory analysis of gene set adjusting for stage and grade were also completed. More extensive clinical data, such as treatment detail and degree of debulking, was missing on a large proportion of cases and thus not included in any model.

Following determination of the gene-level association p-values for genes within each gene set, p-values were summarized to the gene set using a meta-analytical method which can be applied to p-values. We chose to use the Gamma method with soft truncation threshold value (STT) of 0.15, a generalization of Fisher’s method. The Gamma method is based on summing transformed p-values, using an inverse Gamma(ω, 1) transformation. For a particular shape parameter ω, the test statistic is defined as ${\displaystyle \sum _{i=1}^N}{G}_{\omega ,1}^{-1}(1-p_i)$, where G⁻¹ is the inverse of a Gamma(ω, 1) cumulative distribution function (19). For the Gamma method the shape parameter, ω, controls the STT. When ω is 1, the transformed p-values follow a Chi-Square distribution, and therefore the Gamma method becomes equivalent to Fisher’s Method with a STT value of 1/e. Simulation studies have shown this approach with STT = 0.15 to be powerful for testing a self-contained gene set hypothesis under a variety of genetic models (20).

To account for the correlation between genes within a gene set and the size of the gene sets, empirical gene set association p-values were determine from ten-thousand permutations. First, the response variable was randomly permuted 10,000 times keeping the genotypic data fixed (and thus keeping the correlation structure between SNPs and genes fixed). The association test for each gene within the gene set was then computed based on the data set with the permuted phenotypes and the non-permuted phenotype, followed by computation of the gene set analysis statistics. The gene set statistics based on the permuted phenotypes then represent the empirical distribution of the gene-set test statistic. The proportion of permutations in which the empirical GSA test-statistic was greater than the observed GS statistic provided the empirical estimate of the p-value for the GS test for association. It should be noted that this GSA approach does not allow for estimation of the size or direction of gene set effect.

Following GSA of each GWAS, a meta-analysis was completed using Fisher’s method to combine the gene set p-values between the North American and UK GSA. Finally, to assist in the interpretation of results, as multiple SNPs may map to multiple gene sets, we completed hierarchical clustering of the gene sets with p < 0.005. The clustering was based on a distance measure of 1 − μ (μ = average proportion of SNPs shared between gene sets).

Results

Characteristics of invasive EOC cases and those with HGS subtype are described in Table 1. To elucidate gene sets related to overall survival, we completed a combined analysis in which the gene set p-values from each survival GWAS were combined using Fisher’s method for meta-analysis. The combined HGS analysis resulted in 43 (1.7%) gene sets with p < 0.005 (Table 2), with many of the top GSs from GO. However, this apparent “enrichment” of significant GO GSs is largely due to the fact that the set of GO GSs is much larger than the set of GSs in KEGG or PharmGKB. Assuming independence in GSs, we would have expected only 12.83 GSs to have p < 0.005 out of the 2,566 GSs tested by chance alone. The top gene sets were the intracellular signaling pathway (p = 7.3 × 10⁻⁵), regulation of cell-substrate junction assembly (p = 4.0 × 10⁻⁴), anatomical structure formation involved in morphogenesis (p = 5.3 × 10⁻⁴), and organelle outer membrane (p = 5.8 × 10⁻⁴). Of the 43 gene sets with p < 0.005, 21 had p < 0.10 in both the North American and the UK analyses, including the top gene sets of intracellular signaling pathway (North American p = 1.7 × 10⁻³, UK p = 3.3 × 10⁻³, combined p = 7.3 × 10⁻⁵) and macrolide binding (North American p = 9.0 × 10⁻⁴, UK p = 6.4 × 10⁻², combined p = 6.2 × 10⁻⁴). Using a conservative Bonferroni adjustment for multiple testing (α = 2.0 × 10⁻⁵), the intracellular signaling pathway was very close to being statistically significant. The top results were similar when adjusting for stage and grade (Supplemental Table 2).

Table 1.

Clinical characteristics of epithelial ovarian cancer cases.

Variables	All Cases	HGS Cases
N Subjects	2,813	899
N Deaths (%)	1,116 (40%)	473 (53%)
Stage (FIGO)
I	800 (34%)	73 (9%)
II	234 (10%)	60 (7%)
III	1,147 (49%)	611 (73%)
IV	170 (7%)	96 (11%)
missing	462	59
Grade (3/4 combined)
1	330 (14%)	0
2	637 (27%)	0
3	1,351 (58%)	899 (100%)
Missing/unknown	18	0
Histology
Serous	1,491 (53%)	899 (100%)
Mucinous	241 (9%)	0
Endometriod	497 (18%)	0
Clear cell	265 (9%)	0
Other	319 (11%)	0
Age at diagnosis
Mean (SE)	57.8 (10.8)	60.6 (9.8)
Days from diagnosis to enrollment
Mean (SE)	663.1 (807.3)	376.1 (519.4)
Median (range)	443 (0–7,598)	92 (0–3,885)
Years from diagnosis to last follow-up
Mean (SE)	5.7 (4.1)	4.0 (2.7)
Median (range)	4.6 (<0.1–30.6)	3.3 (<0.1–16.8)
North American GWAS study sites
MAY	352 (13%)	204 (23%)
NCO	492 (18%)	162 (18%)
TBO	213 (8%)	118 (13%)
UK GWAS study sites
RMH	143 (5%)	20 (2%)
SEA	1,087 (39%)	251 (28%)
UKR	32 (1%)	0 (0%)
UKO	494 (18%)	144 (16%)

Values reported as number (percent) unless otherwise indicated. HGS = high grade serous.

Table 2.

Association between gene sets and ovarian cancer survival in cases with high-grade serous (HGS) histological subtype (p < 0.005).

Source	Gene Set	No. SNPs	No. Genes	GS P-value
GO:BIO	intracellular signaling pathway	22,715	857	7.3 ×10−5
GO:BIO	regulation of cell-substrate junction assembly	195	8	4.0 ×10−4
GO:BIO	anatomical structure formation involved in morphogenesis	9,701	375	5.3 ×10−4
GO:CELL	organelle outer membrane	2,023	109	5.8 ×10−4
GO:BIO	negative regulation of focal adhesion assembly	114	5	5.8 ×10−4
GO:MOLE	macrolide binding	126	8	6.2 ×10−4
GO:MOLE	cis-trans isomerase activity	318	34	7.5 ×10−4
GO:BIO	negative regulation of odontogenesis	41	2	8.2 ×10−4
GO:BIO	osteoblast differentiation	1,976	70	8.4 ×10−4
GO:BIO	axis specification	420	29	1.0 ×10−3
GO:CELL	photoreceptor inner segment	211	9	1.1 ×10−3
GO:CELL	outer membrane	2,131	113	1.1 ×10−3
GO:BIO	negative regulation of molecular function	5,927	331	1.6 ×10−3
GO:BIO	regulation of response to cytokine stimulus	24	3	1.7 ×10−3
GO:MOLE	SH3/SH2 adaptor activity	1,283	51	1.7 ×10−3
GO:BIO	homeostatic process	17,434	769	1.8 ×10−3
GO:BIO	embryo implantation	507	26	1.9 ×10−3
GO:BIO	tissue homeostasis	1,818	70	1.9 ×10−3
GO:BIO	chromatin disassembly	30	5	1.9 ×10−3
GO:BIO	ossification	3,808	161	2.0 ×10−3
GO:MOLE	protein dimerization activity	13,527	540	2.1 ×10−3
GO:BIO	bone development	3,913	169	2.2 ×10−3
GO:BIO	regulation of odontogenesis	194	8	2.3 ×10−3
GO:BIO	regulation of cell adhesion	3,760	123	2.5 ×10−3
GO:BIO	anatomical structure morphogenesis	33,110	1244	2.5 ×10−3
GO:BIO	signal transmission	78,767	3523	2.5 ×10−3
GO:BIO	somatic stem cell maintenance	244	12	2.7 ×10−3
GO:BIO	multicellular organismal homeostasis	2,259	92	2.8 ×10−3
GO:MOLE	protein binding, bridging	2,037	96	2.9 ×10−3
GO:BIO	organ morphogenesis	12,461	554	2.9 ×10−3
GO:BIO	peptide transport	2,067	87	3.0 ×10−3
PharmGKB	Glucocorticoid & Inflammatory genes (PD)	146	9	3.1 ×10−3
GO:MOLE	protein transmembrane transporter activity	97	14	3.2 ×10−3
GO:BIO	cell migration	11,535	386	3.4 ×10−3
GO:BIO	negative regulation of biological process	38,075	1805	3.4 ×10−3
GO:BIO	intracellular transport	12,735	700	3.5 ×10−3
GO:BIO	multicellular organismal macromolecule metabolic process	954	41	3.5 ×10−3
GO:BIO	signal transduction	66,542	3130	3.7 ×10−3
GO:BIO	muscle homeostasis	989	13	3.8 ×10−3
GO:CELL	beta-catenin destruction complex	87	6	4.0 ×10−3
GO:BIO	negative regulation of response to cytokine stimulus	8	1	4.7 ×10−3
KEGG	TGF-beta signaling pathway	1,359	86	4.7 ×10−3
GO:BIO	stem cell maintenance	414	25	4.7 ×10−3

Adjusted for age, study site, and population structure.

The top gene sets in combined analysis of all cases, regardless of histological subtype, were (Table 3) meiotic mismatch repair (p = 6.3 × 10⁻⁴), macrolide binding (p = 1.0 × 10⁻³), antigen processing and presentation of peptide antigen (p = 1.1 × 10⁻³); mismatch repair complex (p = 1.3 × 10⁻³); and regulation of cell migration (p = 1.7 × 10⁻³). Of the 18 gene sets with combined p < 0.005 (0.7%), eight had p < 0.10 in both the North American and the UK analyses. Similar results were observed in the analysis adjusting for stage and grade (Supplemental Table 2). After adjusting for multiple testing, none of the gene sets were statistically significant in the combined analysis. To aid in the interpretation of the gene set results, Figure 1 presents the results from hierarchical clustering of gene sets with p < 0.005 (based on proportion of SNPs in common) for both the analysis of all cases and HGS cases.

Table 3.

Association between gene sets and ovarian cancer survival (p < 0.005).

Source	Gene Set	No. SNPs	No. Genes	GS P-value
GO:BIO	meiotic mismatch repair	13	1	6.3 ×10−4
GO:MOLE	macrolide binding	126	8	1.0 ×10−3
GO:BIO	antigen processing and presentation of peptide antigen	610	25	1.1 ×10−3
GO:CELL	mismatch repair complex	135	7	1.3 ×10−3
GO:BIO	regulation of cell migration	5,266	171	1.7 ×10−3
GO:BIO	negative regulation of cardiac muscle cell proliferation	78	4	1.7 ×10−3
GO:BIO	positive regulation of cell death	10,363	432	2.2 ×10−3
GO:BIO	release of sequestered calcium ion into cytosol	680	24	2.4 ×10−3
GO:BIO	regulation of sequestering of calcium ion	680	24	2.4 ×10−3
GO:BIO	negative regulation of sequestering of calcium ion	680	24	2.4 ×10−3
GO:BIO	sequestering of metal ion	781	31	2.5 ×10−3
GO:BIO	regulation of locomotion	5,869	192	4.0 ×10−3
GO:BIO	regulation of cellular component movement	5,986	193	4.1 ×10−3
GO:BIO	response to radiation	3,676	190	4.2 ×10−3
GO:BIO	somatic diversification of immune receptors via germline recombination within a single locus	480	35	4.2 ×10−3
GO:BIO	negative regulation of cell migration	1,365	57	4.2 ×10−3
GO:BIO	actin cytoskeleton organization	7,554	265	4.8 ×10−3
GO:BIO	positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid transport	56	2	5.0 ×10−3

Adjusted for age, study site, and population structure.

Figure 1.

Hierarchical clustering dendrogram of gene sets with p < 0.005 (distance measure based on proportion of SNPs in common between gene sets) for the analysis of (A) all cases and (B) HGS cases.

Next, for the top gene sets (p < 0.01), we examined which particular gene(s) in these gene set may be most associated with overall survival. Table 4 presents the genes with p < 0.0005 in top gene sets. For the combined analysis the top most significant genes were: HLA-C (p = 1.3 × 10⁻⁴), MYH3 (p = 1.7 × 10⁻⁴), WNT5A (p = 3.7 × 10⁻⁴) and ZSCAN23 (p = 3.8 × 10⁻⁴).

Table 4.

Genes with p < 0.0005 in gene sets with p < 0.01 from the combined GSA.

Analysis	Gene	Combined P	US P	UK P
All	HLA-C	1.3 ×10−4	1.9 ×10−4	5.3 ×10−2
	MYH3	1.7 ×10−4	1.5 ×10−5	9.6 ×10−1
	WNT5A	3.7 ×10−4	5.5 ×10−2	6.0 ×10−4
	ZSCAN23	3.8 ×10−4	4.6 ×10−4	7.2 ×10−2
HGS	COL28A1	3.2 ×10−5	6.2 ×10−2	3.7 ×10−5
	ZNF331	1.1 ×10−4	2.0 ×10−2	4.6 ×10−4
	GNAT3	1.3 ×10−4	1.6 ×10−4	6.2 ×10−2
	NMNAT3	2.5 ×10−4	1.8 ×10−3	1.2 ×10−2
	ARMS2	3.6 ×10−4	1.5 ×10−1	2.1 ×10−4
	PPIH	3.8 ×10−4	8.1 ×10−3	4.2 ×10−3
	WWOX	4.4 ×10−4	6.2 ×10−4	6.4 ×10−2

Adjusted for age, study site, and population structure. HGS = high grade serous.

To better interpret the combined GSA results, we also examined the GSA results for the individual GWASes. In the North American GWAS, five gene sets with p-values of association with survival < 0.005 were identified among cases with HGS histological subtype, with the top gene set for analysis of HGS being inflammation related (p = 0.0007), cell migration (p =0.0009) and macrolide binding (p = 0.0009). Similarly, four gene sets were found to be associated with survival (gene set p-value < 0.001; Supplemental Table 3) in the overall group. Genetic variation in meiotic mismatch repair, as defined in the biological class of GO, and in mismatch repair complex, as defined in the cellular class of GO, were the most significantly associated gene sets (p-values = 2.0 × 10⁻⁴). Of note, the meiotic mismatch repair gene set included only MSH6 and was contained within the mismatch repair complex gene set. Other gene sets with p < 0.001 were positive regulation of cell death (p = 0.0004) and multicellular organismal aging (p = 0.0009). There was no overlap of the top gene sets (p < 0.001) from the analyses of all versus HGS cases. It should be noted that none of these results are statistically significant at the Bonferroni significance level of 2×10⁻⁵.

GSA of the UK GWAS revealed 11 gene sets with p < 0.001 from the analysis of the HGS cases and three gene sets with p < 0.001 from the analysis of all cases (Supplemental Table 3). Similar to the North American GSA, there was limited overlap between the top gene sets between the analyses of all individuals and the HGS subgroup. For the analysis of the HGS, the top gene sets were photoreceptor inner segment (p = 0.0002), organelle outer membrane (p = 0.0002), and somatic stem cell maintenance (p = 0.0004). The top gene set in the overall analysis involved regulation of the calcium ion (p = 0.001). However, none of these gene sets were significant at a Bonferroni level of 2 ×10⁻⁵. As Supplemental Table 3 illustrates, there was little agreement in top gene sets between the North American and UK analyses.

Discussion

In this manuscript, we present results from the first application of GSA to ovarian cancer. As ovarian cancer has high mortality, we assessed gene set associations with overall survival, hypothesizing that use of standardized groupings of genes based on known biology may identify novel inherited determinants of outcome and identify avenues for mechanistic study. GSA is an increasingly applied approach for secondary analysis of GWAS data, as this analysis approach reduces the number of tests and thus the impact of multiple testing on inferences; in addition, it incorporates prior biological knowledge into the analysis (15). To complete the GSA, we used a novel approach that combines the use of principal components analysis and the Gamma Method, referred to as the PC-GM approach. Simulation studies have found this approach to out perform other self-contained gene set approaches, such as Fisher’s method (32, 33), for a variety of genetic models and scenarios. Although these methods are not designed to identify specific genes or genetic variants that are associated with the trait of interest, results from a GSA can be used to plan further, in-depth, investigation focused on specific gene sets of interest and may uncover additional genetic causes of complex traits.

When attention was confined to cases with the HGS subtype, the top gene set associated with overall survival was the intracellular signaling pathway (p = 7.3 ×10⁻⁵) achieving borderline Bonferroni-corrected statistical significance. This is a large gene set containing 22,715 SNPs mapped to 857 genes. The definition of this gene set from GO’s biological classification states that it contains genes involved in “the process in which a signal is passed to downstream components within the cell, which become activated themselves to further propagate the signal and finally trigger a change in the function or state of the cell”(34). A “child” in the hierarchical structure of GO is the “signal transduction by p53 class mediator” gene set containing genes involved in the signaling process induced by the cell cycle regulator phosphoprotein p53. In addition to genes WWOX and APC which have been implicated in response to therapy (35), additional genes with modest gene-level p-values within the intracellular signaling pathway gene set included SMAD4 (p = 0.009), IL6 (p = 0.0145), ERBB4 (p = 0.018), and JAK2 (p = 0.023). Thus, as the most statistically significant gene set in our report, even based on a smaller sample size of HGS cases alone, this particular collection of genes merits additional follow-up.

One of the most significant gene sets in analysis of all cases was macrolide binding (all cases p =1.0 × 10⁻³; HGS cases p =6.2 ×10⁻⁴) which contained 126 SNPs mapped to eight genes (including FKBP1B [p=0.018], FKBP3 [p = 0.086], NFATC1 [p = 0.021] and FKBP6 [p = 0.088]). This gene set originated from the GO molecular classification which indicates that the gene set is a “child” of the drug binding gene set and a “parent” to the FK506 binding gene set which contains genes that interact “selectively and non-covalently with the immunosuppressant FK506”(36). Henriksen et al (37) found that the FK506 binding protein 65 (FKBP65) was highly expressed in ovarian epithelium and in benign ovarian tumor cells, while the expression levels were lower in invasive tumor cells. FKBP65 was also found to be inversely associated with expression of p53 (37).

While this GSA of GWAS data provides novel insight and findings into the association of genome-wide germline variation with overall survival, this type of analysis has limitations. One limitation is that our definitions of gene sets and pathways are limited to our knowledge about the genome, and pathways are continually evolving. Another limitation is the fact that GSA assumes that SNPs can be assigned to relevant genes, particularly in light of the fact that many phenotype-associated SNPs identified to date do not lie in genes. Lastly, this GSA does not allow one to determine the direction of the gene set effect on the outcome. However, as this study illustrates, novel and biologically plausible association can be detected using GSA and thus contributes to our understanding of the relationship between genetic variation and mortality from EOC. Moreover, these results have led to possible gene sets and novel genes that can be followed up in future studies, in particular: replication studies, pharmacogenomic studies, and studies investigating the development of novel EOC therapies.