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Objective: Ovarian cancer is a hormone-related disease with a strong genetic basis. However, none of its high-penetrance susceptibility genes and GWAS-identified variants to date are known to be involved in hormonal pathways. Given the hypothesized etiologic role of gonadotropins, an assessment of how variability in genes involved in the gonadotropin signaling pathway impacts disease risk is warranted., Methods: Genetic data from 41 ovarian cancer study sites were pooled and unconditional logistic regression was used to evaluate whether any of the 2185 SNPs from 11 gonadotropin signaling pathway genes was associated with ovarian cancer risk. A burden test using the admixture likelihood (AML) method was also used to evaluate gene-level associations., Results: We did not find any genome-wide significant associations between individual SNPs and ovarian cancer risk. However, there was some suggestion of gene-level associations for four gonadotropin signaling pathway genes: INHBB (p=0.045, mucinous), LHCGR (p=0.046, high-grade serous), GNRH (p=0.041, high-grade serous), and FSHB (p=0.036, overall invasive). There was also suggestive evidence for INHA (p=0.060, overall invasive)., Conclusions: Ovarian cancer studies have limited sample numbers, thus fewer genome-wide susceptibility alleles, with only modest associations, have been identified relative to breast and prostate cancers. We have evaluated the majority of ovarian cancer studies with biological samples, to our knowledge, leaving no opportunity for replication. Using both our understanding of biology and powerful gene-level tests, we have identified four putative ovarian cancer loci near INHBB, LHCGR, GNRH, and FSHB that warrant a second look if larger sample sizes and denser genotype chips become available., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Objective: To examine whether urine can be used to measure specific ovarian cancer proteomic profiles and whether one peak alone or in combination with other peaks or CA125 has the sensitivity and specificity to discriminate between ovarian cancer pelvic mass and benign pelvic mass., Methods: A total of 209 women were admitted for surgery for pelvic mass at the Gynaecological Department at Rigshospitalet, Copenhagen. Of the women, 156 had benign gynaecological tumors, 13 had borderline tumors and 40 had malignant epithelial ovarian cancer. The prospectively and preoperatively collected urine samples were aliquotted and frozen at -80 degrees until the time of analysis. The urine was fractionated using equalizer bead technology and then analyzed with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Biomarkers were purified and identified using combinations of chromatographic techniques and tandem mass spectrometry., Results: Benign and malignant ovarian cancer cases were compared; 21 significantly different peaks (p<0.001) were visualized using Mann-Whitney analysis, ranging in m/z values from 1,500 to 185,000. The three most significant peaks were purified and identified as fibrinogen alpha fragment (m/z=2570.21), collagen alpha 1 (III) fragment (m/z=2707.32) and fibrinogen beta NT fragment (m/z=4425.09). The area under the receiver operator characteristic curve (ROC AUC) value for these three peaks in combination was 0.88, and their ROC AUC value in combination with CA125 was 0.96., Conclusion: This result supports the feasibility of using urine as a clinical diagnostic medium, and the ROC AUC value for the three most significant peaks in combination with or without CA125 demonstrates the enhanced prediction performance of combined marker analysis.