High-prevalence and broad spectrum of Cell Adhesion and Extracellular Matrix gene pathway mutations in epithelial ovarian cancer

Discovery of new therapeutic targets in ovarian cancer through identifying significantly non-mutated genes

BACKGROUND

Mutated and non-mutated genes interact to drive cancer growth and metastasis. While research has focused on understanding the impact of mutated genes on cancer biology, understanding non-mutated genes that are essential to tumor development could lead to new therapeutic strategies. The recent advent of high-throughput whole genome sequencing being applied to many different samples has made it possible to calculate if genes are significantly non-mutated in a specific cancer patient cohort.

METHODS

We carried out random mutagenesis simulations of the human genome approximating the regions sequenced in the publicly available Cancer Growth Atlas Project for ovarian cancer (TCGA-OV). Simulated mutations were compared to the observed mutations in the TCGA-OV cohort and genes with the largest deviations from simulation were identified. Pathway analysis was performed on the non-mutated genes to better understand their biological function. We then compared gene expression, methylation and copy number distributions of non-mutated and mutated genes in cell lines and patient data from the TCGA-OV project. To directly test if non-mutated genes can affect cell proliferation, we carried out proof-of-concept RNAi silencing experiments of a panel of nine selected non-mutated genes in three ovarian cancer cell lines and one primary ovarian epithelial cell line.

RESULTS

We identified a set of genes that were mutated less than expected (non-mutated genes) and mutated more than expected (mutated genes). Pathway analysis revealed that non-mutated genes interact in cancer associated pathways. We found that non-mutated genes are expressed significantly more than mutated genes while also having lower methylation and higher copy number states indicating that they could be functionally important. RNAi silencing of the panel of non-mutated genes resulted in a greater significant reduction of cell viability in the cancer cell lines than in the non-cancer cell line. Finally, as a test case, silencing ANKLE2, a significantly non-mutated gene, affected the morphology, reduced migration, and increased the chemotherapeutic response of SKOV3 cells.

CONCLUSION

We show that we can identify significantly non-mutated genes in a large ovarian cancer cohort that are well-expressed in patient and cell line data and whose RNAi-induced silencing reduces viability in three ovarian cancer cell lines. Targeting non-mutated genes that are important for tumor growth and metastasis is a promising approach to expand cancer therapeutic options.

Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer

Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients, and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those genes could lead to new therapeutic strategies.

Identifying genes that contribute to cancer biology is complicated partly because cancers can have dozens of somatic mutations and thousands of germline variants. Somatic mutations are gene variants that arise after conception in an organism while germline variants are gene variants present at conception in an organism. Most methods to identify cancer drivers have focused on determining somatic mutations. In this study we attempt to identify, from a tumor sample, important germline and somatic variants by determining if a variant is expressed (made into RNA) more than expected from the amount of the variant in the genome. The preferred expression of a variant could benefit cancer cells. When applying our analysis to ovarian cancer samples we found that despite the apparent heterogeneity, different patients frequently share the same genes with preferentially expressed variants. These genes in many cases are known to affect cancer processes such as DNA repair, cell adhesion and cell signaling and are targetable with known drugs. We therefore conclude that our analysis can identify germline and somatic gene variants that contribute to cancer biology and can potentially guide individualized therapies.

Catalpol suppresses proliferation and facilitates apoptosis of OVCAR-3 ovarian cancer cells through upregulating microRNA-200 and downregulating MMP-2 expression

Catalpol is expected to possess diverse pharmacological actions including anti-cancer, anti-inflammatory and hypoglycemic properties. Matrix metalloproteinase-2 (MMP-2) is closely related to the pathogenesis of ovarian cancer. In addition, microRNA-200 (miR-200) can modulate phenotype, proliferation, infiltration and transfer of various tumors. Here, OVCAR-3 cells were employed to investigate whether the effect of catalpol (25, 50 and 100 μg/mL) promoted apoptosis of ovarian cancer cells and to explore the potential mechanisms. Our results demonstrate that catalpol could remarkably reduce the proliferation and accelerate the apoptosis of OVCAR-3 cells. Interestingly, our findings show that catalpol treatment significantly decreased the MMP-2 protein level and increased the miR-200 expression level in OVCAR-3 cells. Further, microRNA-200 was shown to regulate the protein expression of MMP-2 in OVCAR-3 cells. It is concluded that catalpol suppressed cellular proliferation and accelerated apoptosis in OVCAR-3 ovarian cancer cells via promoting microRNA-200 expression levels and restraining MMP-2 signaling.