Lu, Karen H.

Abstract A missense single-nucleotide polymorphism (SNP) in the immune modulatory gene IL1A has been associated with ovarian cancer risk (rs17561). Although the exact mechanism through which this SNP alters risk of ovarian cancer is not clearly understood, rs17561 has also been associated with risk of endometriosis, an epidemiologic risk factor for ovarian cancer. Interleukin-1α (IL1A) is both regulated by and able to activate NF-κB, a transcription factor family that induces transcription of many proinflammatory genes and may be an important mediator in carcinogenesis. We therefore tagged SNPs in more than 200 genes in the NF-κB pathway for a total of 2,282 SNPs (including rs17561) for genotype analysis of 15,604 cases of ovarian cancer in patients of European descent, including 6,179 of high-grade serous (HGS), 2,100 endometrioid, 1,591 mucinous, 1,034 clear cell, and 1,016 low-grade serous, including 23,235 control cases spanning 40 studies in the Ovarian Cancer Association Consortium. In this large population, we confirmed the association between rs17561 and clear cell ovarian cancer [OR, 0.84; 95% confidence interval (CI), 0.76–0.93; P = 0.00075], which remained intact even after excluding participants in the prior study (OR, 0.85; 95% CI, 0.75–0.95; P = 0.006). Considering a multiple-testing–corrected significance threshold of P < 2.5 × 10−5, only one other variant, the TNFSF10 SNP rs6785617, was associated significantly with a risk of ovarian cancer (low malignant potential tumors OR, 0.85; 95% CI, 0.79–0.91; P = 0.00002). Our results extend the evidence that borderline tumors may have a distinct genetic etiology. Further investigation of how these SNPs might modify ovarian cancer associations with other inflammation-related risk factors is warranted. Cancer Res; 74(3); 852–61. ©2013 AACR.

AbstractThis article reviews progress in chemopreventive drug development, especially data and concepts that are new since the 2002 AACR report on treatment and prevention of intraepithelial neoplasia. Molecular biomarker expressions involved in mechanisms of carcinogenesis and genetic progression models of intraepithelial neoplasia are discussed and analyzed for how they can inform mechanism-based, molecularly targeted drug development as well as risk stratification, cohort selection, and end-point selection for clinical trials. We outline the concept of augmenting the risk, mechanistic, and disease data from histopathologic intraepithelial neoplasia assessments with molecular biomarker data. Updates of work in 10 clinical target organ sites include new data on molecular progression, significant completed trials, new agents of interest, and promising directions for future clinical studies. This overview concludes with strategies for accelerating chemopreventive drug development, such as integrating the best science into chemopreventive strategies and regulatory policy, providing incentives for industry to accelerate preventive drugs, fostering multisector cooperation in sharing clinical samples and data, and creating public-private partnerships to foster new regulatory policies and public education.

Abstract Purpose: Advanced-stage epithelial ovarian cancer has a poor prognosis with long-term survival in less than 30% of patients. When the disease is detected in stage I, more than 90% of patients can be cured by conventional therapy. Screening for early-stage disease with individual serum tumor markers, such as CA125, is limited by the fact that no single marker is up-regulated and shed in adequate amounts by all ovarian cancers. Consequently, use of multiple markers in combination might detect a larger fraction of early-stage ovarian cancers. Experimental Design: To identify potential candidates for novel markers, we have used Affymetrix human genome arrays (U95 series) to analyze differences in gene expression of 41,441 known genes and expressed sequence tags between five pools of normal ovarian surface epithelial cells (OSE) and 42 epithelial ovarian cancers of different stages, grades, and histotypes. Recursive descent partition analysis (RDPA) was performed with 102 probe sets representing 86 genes that were up-regulated at least 3-fold in epithelial ovarian cancers when compared with normal OSE. In addition, a panel of 11 genes known to encode potential tumor markers [mucin 1, transmembrane (MUC1), mucin 16 (CA125), mesothelin, WAP four-disulfide core domain 2 (HE4), kallikrein 6, kallikrein 10, matrix metalloproteinase 2, prostasin, osteopontin, tetranectin, and inhibin] were similarly analyzed. Results: The 3-fold up-regulated genes were examined and four genes [Notch homologue 3 (NOTCH3), E2F transcription factor 3 (E2F3), GTPase activating protein (RACGAP1), and hematological and neurological expressed 1 (HN1)] distinguished all tumor samples from normal OSE. The 3-fold up-regulated genes were analyzed using RDPA, and the combination of elevated claudin 3 (CLDN3) and elevated vascular endothelial growth factor (VEGF) distinguished the cancers from normal OSE. The 11 known markers were analyzed using RDPA, and a combination of HE4, CA125, and MUC1 expression could distinguish tumor from normal specimens. Expression at the mRNA level in the candidate markers was examined via semiquantitative reverse transcription-PCR and was found to correlate well with the array data. Immunohistochemistry was performed to identify expression of the genes at the protein level in 158 ovarian cancers of different histotypes. A combination of CLDN3, CA125, and MUC1 stained 157 (99.4%) of 158 cancers, and all of the tumors were detected with a combination of CLDN3, CA125, MUC1, and VEGF. Conclusions: Our data are consistent with the possibility that a limited number of markers in combination might identify >99% of epithelial ovarian cancers despite the heterogeneity of the disease.

Abstract Purpose: Epithelial ovarian cancers are thought to arise from flattened epithelial cells that cover the ovarian surface or that line inclusion cysts. During malignant transformation, different histotypes arise that resemble epithelial cells from normal fallopian tube, endometrium, and intestine. This study compares gene expression in serous, endometrioid, clear cell, and mucinous ovarian cancers with that in the normal tissues that they resemble. Experimental Design: Expression of 63,000 probe sets was measured in 50 ovarian cancers, in 5 pools of normal ovarian epithelial brushings, and in mucosal scrapings from 4 normal fallopian tube, 5 endometrium, and 4 colon specimens. Using rank-sum analysis, genes whose expressions best differentiated the ovarian cancer histotypes and normal ovarian epithelium were used to determine whether a correlation based on gene expression existed between ovarian cancer histotypes and the normal tissues they resemble. Results: When compared with normal ovarian epithelial brushings, alterations in serous tumors correlated with those in normal fallopian tube (P = 0.0042) but not in other normal tissues. Similarly, mucinous cancers correlated with those in normal colonic mucosa (P = 0.0003), and both endometrioid and clear cell histotypes correlated with changes in normal endometrium (P = 0.0172 and 0.0002, respectively). Mucinous cancers displayed the greatest number of alterations in gene expression when compared with normal ovarian epithelial cells. Conclusion: Studies at a molecular level show distinct expression profiles of different histologies of ovarian cancer and support the long-held belief that histotypes of ovarian cancers come to resemble normal fallopian tube, endometrial, and colonic epithelium. Several potential molecular markers for mucinous ovarian cancers have been identified.

Abstract Purpose: Women with advanced epithelial ovarian cancer are routinely treated with platinum-paclitaxel chemotherapy following cytoreductive surgery, yet only ∼20% achieve long-term disease-free survival. We hypothesized that differences in gene expression before treatment could distinguish patients with short versus long time to recurrence after administration of platinum-paclitaxel combination chemotherapy. Experimental Design: To test this hypothesis, gene expression profiling of 79 primary surgically resected tumors from women with advanced-stage, high-grade epithelial ovarian cancer was done using cDNA microarrays containing 30,721 genes. Supervised learning algorithms were applied in an effort to develop a binary classifier that could discriminate women at risk for early (≤21 months) versus late (>21 months) relapse after initial chemotherapy. Results: A 14-gene predictive model was developed using a set of training samples (n = 51) and subsequently tested using an independent set of test samples (n = 28). This model correctly predicted the outcome of 24 of the 28 test samples (86% accuracy) with 95% positive predictive value for early relapse. Conclusions: Predictive markers for early recurrence can be identified for platinum-paclitaxel combination chemotherapy in primary ovarian carcinoma. The proposed 14-gene model requires further validation.