Person:

Birrer, Michael J.

Molecular subtypes of serous ovarian cancer have been recently described. Using data from independent datasets including over 900 primary tumour samples, we show that deregulation of the Let-7 pathway is specifically associated with the C5 molecular subtype of serous ovarian cancer. DNA copy number and gene expression of HMGA2, alleles of Let-7, LIN28, LIN28B, MYC, MYCN, DICER1, and RNASEN were measured using microarray and quantitative reverse transcriptase PCR. Immunohistochemistry was performed on 127 samples using tissue microarrays and anti-HMGA2 antibodies. Fluorescence in situ hybridisation of bacterial artificial chromosomes hybridized to 239 ovarian tumours was used to measure translocation at the LIN28B locus. Short interfering RNA knockdown in ovarian cell lines was used to test the functionality of associations observed. Four molecular subtypes (C1, C2, C4, C5) of high-grade serous ovarian cancers were robustly represented in each dataset and showed similar pattern of patient survival. We found highly specific activation of a pathway involving MYCN, LIN28B, Let-7 and HMGA2 in the C5 molecular subtype defined by MYCN amplification and over-expression, over-expression of MYCN targets including the Let-7 repressor LIN28B, loss of Let-7 expression and HMGA2 amplification and over-expression. DICER1, a known Let-7 target, and RNASEN were over-expressed in C5 tumours. We saw no evidence of translocation at the LIN28B locus in C5 tumours. The reported interaction between LIN28B and Let-7 was recapitulated by siRNA knockdown in ovarian cancer cell lines. Our results associate deregulation of MYCN and downstream targets, including Let-7 and oncofetal genes, with serous ovarian cancer. We define for the first time how elements of an oncogenic pathway, involving multiple genes that contribute to stem cell renewal, is specifically altered in a molecular subtype of serous ovarian cancer. By defining the drivers of a molecular subtype of serous ovarian cancers we provide a novel strategy for targeted therapeutic intervention.

Molecular events leading to epithelial ovarian cancer are poorly understood but ovulatory hormones and a high number of life-time ovulations with concomitant proliferation, apoptosis, and inflammation, increases risk. We identified genes that are regulated during the estrous cycle in murine ovarian surface epithelium and analysed these profiles to identify genes dysregulated in human ovarian cancer, using publically available datasets. We identified 338 genes that are regulated in murine ovarian surface epithelium during the estrous cycle and dysregulated in ovarian cancer. Six of seven candidates selected for immunohistochemical validation were expressed in serous ovarian cancer, inclusion cysts, ovarian surface epithelium and in fallopian tube epithelium. Most were overexpressed in ovarian cancer compared with ovarian surface epithelium and/or inclusion cysts (EpCAM, EZH2, BIRC5) although BIRC5 and EZH2 were expressed as highly in fallopian tube epithelium as in ovarian cancer. We prioritised the 338 genes for those likely to be important for ovarian cancer development by in silico analyses of copy number aberration and mutation using publically available datasets and identified genes with established roles in ovarian cancer as well as novel genes for which we have evidence for involvement in ovarian cancer. Chromosome segregation emerged as an important process in which genes from our list of 338 were over-represented including two (BUB1, NCAPD2) for which there is evidence of amplification and mutation. NUAK2, upregulated in ovarian surface epithelium in proestrus and predicted to have a driver mutation in ovarian cancer, was examined in a larger cohort of serous ovarian cancer where patients with lower NUAK2 expression had shorter overall survival. In conclusion, defining genes that are activated in normal epithelium in the course of ovulation that are also dysregulated in cancer has identified a number of pathways and novel candidate genes that may contribute to the development of ovarian cancer.

The RASSF1A tumor suppressor gene is frequently inactivated by promoter methylation in human tumors. The RASSF1A protein forms an endogenous complex with tubulin and promotes the stabilization of microtubules. Loss of RASSF1A expression sensitizes cells to microtubule destabilizing stimuli. We have observed a strong correlation between the loss of RASSF1A expression and the development of Taxol resistance in primary ovarian cancer samples. Thus, we sought to determine if RASSF1A levels could dictate the response to Taxol and whether an epigenetic therapy approach might be able to reverse the Taxol resistant phenotype of RASSF1A negative ovarian tumor cells. We found that knocking down RASSF1A expression in an ovarian cancer cell line inhibited Taxol-mediated apoptosis and promoted cell survival during Taxol treatment. Moreover, using a combination of small molecule inhibitors of DNA Methyl Transferase enzymes, we were able restore RASSF1A expression and Taxol sensitivity. This identifies a role for RASSF1A in modulating the tumor response to Taxol and provides proof of principal for the use of epigenetic therapy to overcome Taxol resistance.

This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer. Database URL: http://bcb.dfci.harvard.edu/ovariancancer

Identification of gene expression profiles of cancer stem cells may have significant implications in the understanding of tumor biology and for the design of novel treatments targeted toward these cells. Here we report a potential ovarian cancer stem cell gene expression profile from isolated side population of fresh ascites obtained from women with high-grade advanced stage papillary serous ovarian adenocarcinoma. Affymetrix U133 Plus 2.0 microarrays were used to interrogate the differentially expressed genes between side population (SP) and main population (MP), and the results were analyzed by paired T-test using BRB-ArrayTools. We identified 138 up-regulated and 302 down-regulated genes that were differentially expressed between all 10 SP/MP pairs. Microarray data was validated using qRT-PCR and 17/19 (89.5%) genes showed robust correlations between microarray and qRT-PCR expression data. The Pathway Studio analysis identified several genes involved in cell survival, differentiation, proliferation, and apoptosis which are unique to SP cells and a mechanism for the activation of Notch signaling is identified. To validate these findings, we have identified and isolated SP cells enriched for cancer stem cells from human ovarian cancer cell lines. The SP populations were having a higher colony forming efficiency in comparison to its MP counterpart and also capable of sustained expansion and differentiation in to SP and MP phenotypes. 50,000 SP cells produced tumor in nude mice whereas the same number of MP cells failed to give any tumor at 8 weeks after injection. The SP cells demonstrated a dose dependent sensitivity to specific γ-secretase inhibitors implicating the role of Notch signaling pathway in SP cell survival. Further the generated SP gene list was found to be enriched in recurrent ovarian cancer tumors.

Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Standard therapy includes treatment with platinum-based combination chemotherapies yet there is no biomarker model to predict their responses to these agents. We here have developed and independently tested our multi-gene molecular predictors for forecasting patients' responses to individual drugs on a cohort of 55 ovarian cancer patients. To independently validate these molecular predictors, we performed microarray profiling on FFPE tumor samples of 55 ovarian cancer patients (UVA-55) treated with platinum-based adjuvant chemotherapy. Genome-wide chemosensitivity biomarkers were initially discovered from the in vitro drug activities and genomic expression data for carboplatin and paclitaxel, respectively. Multivariate predictors were trained with the cell line data and then evaluated with a historical patient cohort. For the UVA-55 cohort, the carboplatin, taxol, and combination predictors significantly stratified responder patients and non-responder patients (p = 0.019, 0.04, 0.014) with sensitivity = 91%, 96%, 93 and NPV = 57%, 67%, 67% in pathologic clinical response. The combination predictor also demonstrated a significant survival difference between predicted responders and non-responders with a median survival of 55.4 months vs. 32.1 months. Thus, COXEN single- and combination-drug predictors successfully stratified platinum resistance and taxane response in an independent cohort of ovarian cancer patients based on their FFPE tumor samples.

Ovarian cancer (OC) accounts for more deaths than all other gynecological cancers combined. To identify common low-penetrance OC susceptibility genes, we conducted a genome-wide association study (GWAS) of 507,094 SNPs in 1,768 cases and 2,354 controls, with follow-up of 21,955 SNPs in 4,162 cases and 4,810 controls, leading to the identification of a confirmed susceptibility locus at 9p22 (BNC2)1. Here, we report on nine additional candidate loci (p≤10-4), identified after stratifying cases by histology, genotyped in an additional 4,353 cases and 6,021 controls. Two novel susceptibility loci with p≤5×10-8 were confirmed (8q24, p=8.0×10-15 and 2q31, p=3.8×10-14); two additional loci were also identified that approached genome-wide significance (3q25, p=7.1×10-8 and 17q21, p=1.4×10-7). The associations with serous OC were generally stronger than other subtypes. Analysis of HOXD1, MYC, TiPARP, and SKAP1 at these loci, and BNC2 at 9p22, supports a functional role for these genes in OC development.

Genome wide association studies (GWAS) have identified four susceptibility loci for epithelial ovarian cancer (EOC) with another two loci being close to genome-wide significance. We pooled data from a GWAS conducted in North America with another GWAS from the United Kingdom. We selected the top 24,551 SNPs for inclusion on the iCOGS custom genotyping array. Follow-up genotyping was carried out in 18,174 cases and 26,134 controls from 43 studies from the Ovarian Cancer Association Consortium. We validated the two loci at 3q25 and 17q21 previously near genome-wide significance and identified three novel loci associated with risk; two loci associated with all EOC subtypes, at 8q21 (rs11782652, P=5.5×10-9) and 10p12 (rs1243180; P=1.8×10-8), and another locus specific to the serous subtype at 17q12 (rs757210; P=8.1×10-10). An integrated molecular analysis of genes and regulatory regions at these loci provided evidence for functional mechanisms underlying susceptibility that implicates CHMP4C in the pathogenesis of ovarian cancer.

Background

Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.

Methodology/Principal Findings

Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation (“batch-effect”). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2nd validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p<0.01), 1st validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2nd validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2nd validation set.

Conclusions/Significance

Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.

Activating Protein 1 (AP-1) plays a vital role in cell proliferation, differentiation and apoptosis. While de-regulation of AP-1 has been linked to many cancers, little is known regarding its downstream transcriptional targets that associate with cellular transformation. Previous studies identified PAK3, a serine/threonine kinase, as a potential AP-1 target gene. PAK3 has been implicated in a variety of pathological disorders and over-expression of other PAK-family members has been linked to cancer. In this study, we investigate AP-1 regulation of PAK3 expression and the role of PAK3 in cJun/AP-1-associated cellular transformation. Our results showed elevated PAK3 expression at both the mRNA and protein level in cJun-over-expressing Rat1a fibroblasts, as well as in transformed human fibroblasts. Elevated PAK3 expression in cJun/AP-1 over-expressing cells associated with a significant increase in PAK3 promoter activation. This increased promoter activity was lost when a single putative Jun binding site, which can bind AP-1 directly both in vitro and in vivo, was mutated. Further, inhibition of PAK3 using siRNA showed a regression in the cell morphology, migratory potential and actin organisation associated with AP-1 transformed cells. Our study is a first to describe a role for AP-1 in regulating PAK3 expression and suggest that PAK3 is an AP-1 target required for actin organization and migration observed in transformed cells.