Identifying novel interactions of the colon-cancer related APC protein with Wnt-pathway nuclear transcription factors.

BACKGROUND: Colon cancer is often driven by mutations of the adenomatous polyposis coli (APC) gene, an essential tumor suppressor gene of the Wnt ß-catenin signaling pathway. APC and its cytoplasmic interactions have been well studied. However, various groups have also observed its presence in the nucleus. Identifying novel interactions of APC in the Wnt pathway will provide an opportunity to understand APC's nuclear role better and ultimately identify potential cancer treatment targets. METHODS: We used the all-vs-all sequencing (AVA-Seq) method to interrogate the interactome of protein fragments spanning most of the 60 Wnt ß-catenin pathway proteins. Using protein fragments identified the interacting regions between the proteins with more resolution than a full-length protein approach. Pull-down assays were used to validate a subset of these interactions. RESULTS: 74 known and 703 novel Wnt ß-catenin pathway protein-protein interactions were recovered in this study. There were 8 known and 31 novel APC protein-protein interactions. Novel interactions of APC and nuclear transcription factors TCF7, JUN, FOSL1, and SOX17 were particularly interesting and confirmed in validation assays. CONCLUSION: Based on our findings of novel interactions between APC and transcription factors and previous evidence of APC localizing to the nucleus, we suggest APC may compete and repress CTNNB1. This would occur through APC binding to the transcription factors (JUN, FOSL1, TCF7) to regulate the Wnt signaling pathway including through enhanced marking of CTNNB1 for degradation in the nucleus by APC binding with SOX17. Additional novel Wnt ß-catenin pathway protein-protein interactions from this study could lead researchers to novel drug designs for 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.

Novel protein contact points among TP53 and minichromosome maintenance complex proteins 2, 3, and 5.

OBJECTIVE: Identify protein contact points between TP53 and minichromosome maintenance (MCM) complex proteins 2, 3, and 5 with high resolution allowing for potential novel Cancer drug design. METHODS: A next-generation sequencing-based protein-protein interaction method developed in our laboratory called AVA-Seq was applied to a gold-standard human protein interaction set. Proteins including TP53, MCM2, MCM3, MCM5, HSP90AA1, PCNA, NOD1, and others were sheared and ligated into the AVA-Seq system. Protein-protein interactions were then identified in both mild and stringent selective conditions. RESULTS: Known interactions among MCM2, MCM3, and MCM5 were identified with the AVA-Seq system. The interacting regions detected between these three proteins overlap with the structural data of the MCM complex, and novel domains were identified with high resolution determined by multiple overlapping fragments. Fragments of wild type TP53 were shown to interact with MCM2, MCM3, and MCM5, and details on the location of the interactions were provided. Finally, a mini-network of known and novel cancer protein interactions was provided, which could have implications for fundamental changes in multiple cancers. CONCLUSION: We provide a high-resolution mini-interactome that could direct novel drug targets and implicate possible effects of specific cancer mutations.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein-protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all-vs-all sequencing (AVA-Seq) approach using a gold-standard human protein interaction set (hsPRS-v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA-Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome-wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto-activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA-Seq recovered >500 known and novel PPIs, including interactions between wild-type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

Regulation of Circular RNA CircNFATC3 in Cancer Cells Alters Proliferation, Migration, and Oxidative Phosphorylation.

Circular RNAs were once considered artifacts of transcriptome sequencing but have recently been identified as functionally relevant in different types of cancer. Although there is still no clear main function of circRNAs, several studies have revealed that circRNAs are expressed in various eukaryotic organisms in a regulated manner often independent of their parental linear isoforms demonstrating conservation across species. circNFATC3, an abundant and uncharacterized circular RNA of exon 2 and 3 from NFATC3, was identified in transcriptomic data of solid tumors. Here we show that circNFATC3 gain- and loss-of-function experiments using RNAi-mediated circRNA silencing and circular mini vector-mediated overexpression of circularized constructs in breast and ovarian cancer cell lines affect molecular phenotypes. The knockdown of circNFATC3 induces a reduction in cell proliferation, invasion, migration, and oxidative phosphorylation. Gain-of-function of circNFATC3 in MDA-MB-231 and SK-OV-3 cells show a significant increase in cell proliferation, migration, and respiration. The above results suggest that circNFATC3 is a functionally relevant circular RNA in breast and ovarian cancer.

Long non-coding RNA expression in non-obese women with polycystic ovary syndrome and weight-matched controls.

RESEARCH QUESTION: Long non-coding RNA (lncRNA) do not show protein translation but do have gene regulatory functions in several disease states. Studies have shown that lncRNA differ in overweight women with polycystic ovary syndrome (PCOS), increased insulin resistance and hyperandrogenaemia. The objective of this study was to determine the lncRNA in serum in age- and weight-matched non-obese women with and without PCOS. METHODS: In this prospective pilot cohort study, lncRNA were measured in serum in 13 non-obese women with PCOS and 10 control women undergoing IVF. RESULTS: There was no difference between groups in terms of age, body mass index or insulin resistance. Women with PCOS showed a higher free androgen index (FAI; P = 0.03) and anti-Müllerian hormone (AMH) concentration (P = 0.001). A total of 29 lncRNA (P ≤ 0.05) differed between PCOS groups. lncRNA AC095350.1 correlated with age (r = 0.79, P = 0.04), but no correlation was seen between the significantly different lncRNA and FAI or AMH values. Functional pathway assessment using the Ingenuity Pathway Assessment tool showed no relationships for the lncRNA. CONCLUSION: lncRNA in serum differed between non-obese women with PCOS and the control group, and the pattern of expression differed from that reported in obese women with PCOS from the same ethnic population; however, it but did not correlate with androgen or insulin resistance.

Alterations in long noncoding RNAs in women with and without polycystic ovarian syndrome.

Long noncoding RNAs (lncRNAs) are RNA transcripts over 200 nucleotides long that are not translated into protein; however, there is increasing evidence of their regulatory functions. To date, there are few studies measuring lncRNA in control women or women with polycystic ovary syndrome (PCOS). OBJECTIVE: To determine lncRNA differences between PCOS and control women. DESIGN: Cross sectional study. PATIENTS: Twenty four anovulatory women with all three diagnostic features of PCOS compared to 24 control women in the follicular phase of their menstrual cycle from a PCOS biobank. RESULTS: Women with PCOS were age and weight matched compared to the control women but were significantly insulin resistant and hyperandrogenemic (P < .01). Eight lncRNA (P < .05) were detected that differed between PCOS and control women, but only MIRLET7BHG correlated with body mass index (r = .66, P < .05). No lncRNA correlated with antimullerian hormone (AMH) levels, insulin resistance (HOMA-IR) or the free androgen index (FAI). Ingenuity pathway assessment (IPA) did not identify any functional pathways for the lncRNAs. CONCLUSION: LncRNAs differ between anovulatory PCOS and control women in the follicular phase of the menstrual cycle. It is unclear if this is due to inherent differences between PCOS and control women or due to changes in lncRNA that are menstrual cycle dependent. However, their IPA did not identify linked pathways, likely because few functions are as yet assigned to these lncRNAs.

Cell Type-Specific TGF-ß Mediated EMT in 3D and 2D Models and Its Reversal by TGF-ß Receptor Kinase Inhibitor in Ovarian Cancer Cell Lines.

Transcriptome profiling of 3D models compared to 2D models in various cancer cell lines shows differential expression of TGF-ß-mediated and cell adhesion pathways. Presence of TGF-ß in these cell lines shows an increased invasion potential which is specific to cell type. In the present study, we identified exogenous addition of TGF-ß can induce Epithelial to Mesenchymal Transition (EMT) in a few cancer cell lines. RNA sequencing and real time PCR were carried out in different ovarian cancer cell lines to identify molecular profiling and metabolic profiling. Since EMT induction by TGF-ß is cell-type specific, we decided to select two promising ovarian cancer cell lines as model systems to study EMT. TGF-ß modulation in EMT and cancer invasion were successfully depicted in both 2D and 3D models of SKOV3 and CAOV3 cell lines. Functional evaluation in 3D and 2D models demonstrates that the addition of the exogenous TGF-ß can induce EMT and invasion in cancer cells by turning them into aggressive phenotypes. TGF-ß receptor kinase I inhibitor (LY364947) can revert the TGF-ß effect in these cells. In a nutshell, TGF-ß can induce EMT and migration, increase aggressiveness, increase cell survival, alter cell characteristics, remodel the Extracellular Matrix (ECM) and increase cell metabolism favorable for tumor invasion and metastasis. We concluded that transcriptomic and phenotypic effect of TGF-ß and its inhibitor is cell-type specific and not cancer specific.

Silencing of ANKRD12 circRNA induces molecular and functional changes associated with invasive phenotypes.

BACKGROUND: Circular RNAs (circRNAs) that form through non-canonical backsplicing events of pre-mRNA transcripts are evolutionarily conserved and abundantly expressed across species. However, the functional relevance of circRNAs remains a topic of debate. METHODS: We identified one of the highly expressed circRNA (circANKRD12) in cancer cell lines and characterized it validated it by Sanger sequencing, Real-Time PCR. siRNA mediated silencing of the circular junction of circANKRD12 was followed by RNA Seq analysis of circANKRD12 silenced cells and control cells to identify the differentially regulated genes. A series of cell biology and molecular biology techniques (MTS assay, Migration analysis, 3D organotypic models, Real-Time PCR, Cell cycle analysis, Western blot analysis, and Seahorse Oxygen Consumption Rate analysis) were performed to elucidate the function, and underlying mechanisms involved in circANKRD12 silenced breast and ovarian cancer cells. RESULTS: In this study, we identified and characterized a circular RNA derived from Exon 2 and Exon 8 of the ANKRD12 gene, termed here as circANKRD12. We show that this circRNA is abundantly expressed in breast and ovarian cancers. The circANKRD12 is RNase R resistant and predominantly localized in the cytoplasm in contrast to its source mRNA. We confirmed the expression of this circRNA across a variety of cancer cell lines and provided evidence for its functional relevance through downstream regulation of several tumor invasion genes. Silencing of circANKRD12 induces a strong phenotypic change by significantly regulating cell cycle, increasing invasion and migration and altering the metabolism in cancer cells. These results reveal the functional significance of circANKRD12 and provide evidence of a regulatory role for this circRNA in cancer progression. CONCLUSIONS: Our study demonstrates the functional relevance of circANKRD12 in various cancer cell types and, based on its expression pattern, has the potential to become a new clinical biomarker.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation.

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Altered expression pattern of circular RNAs in primary and metastatic sites of epithelial ovarian carcinoma.

Recently, a class of endogenous species of RNA called circular RNA (circRNA) has been shown to regulate gene expression in mammals and their role in cellular function is just beginning to be understood. To investigate the role of circRNAs in ovarian cancer, we performed paired-end RNA sequencing of primary sites, peritoneal and lymph node metastases from three patients with stage IIIC ovarian cancer. We developed an in-house computational pipeline to identify and characterize the circRNA expression from paired-end RNA-Seq libraries. This pipeline revealed thousands of circular isoforms in Epithelial Ovarian Carcinoma (EOC). These circRNAs are enriched for potentially effective miRNA seed matches. A significantly larger number of circRNAs are differentially expressed between tumor sites than mRNAs. Circular and linear expression exhibits an inverse trend for many cancer related pathways and signaling pathways like NFkB, PI3k/AKT and TGF-ß typically activated for mRNA in metastases are inhibited for circRNA expression. Further, circRNAs show a more robust expression pattern across patients than mRNA forms indicating their suitability as biomarkers in highly heterogeneous cancer transcriptomes. The consistency of circular RNA expression may offer new candidates for cancer treatment and prognosis.

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.

K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores.

BACKGROUND: Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals. RESULTS: The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains. CONCLUSION: Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.

Mesenchymal cell interaction with ovarian cancer cells induces a background dependent pro-metastatic transcriptomic profile.

BACKGROUND: The cross talk between the stroma and cancer cells plays a major role in phenotypic modulation. During peritoneal carcinomatosis ovarian cancer cells interact with mesenchymal stem cells (MSC) resulting in increased metastatic ability. Understanding the transcriptomic changes underlying the phenotypic modulation will allow identification of key genes to target. However in the context of personalized medicine we must consider inter and intra tumoral heterogeneity. In this study we used a pathway-based approach to illustrate the role of cell line background in transcriptomic modification during a cross talk with MSC. METHODS: We used two ovarian cancer cell lines as a surrogate for different ovarian cancer subtypes: OVCAR3 for an epithelial and SKOV3 for a mesenchymal subtype. We co-cultured them with MSCs. Genome wide gene expression was determined after cell sorting. Ingenuity pathway analysis was used to decipher the cell specific transcriptomic changes related to different pro-metastatic traits (Adherence, migration, invasion, proliferation and chemoresistance). RESULTS: We demonstrate that co-culture of ovarian cancer cells in direct cellular contact with MSCs induces broad transcriptomic changes related to enhance metastatic ability. Genes related to cellular adhesion, invasion, migration, proliferation and chemoresistance were enriched under these experimental conditions. Network analysis of differentially expressed genes clearly shows a cell type specific pattern. CONCLUSION: The contact with the mesenchymal niche increase metastatic initiation and expansion through cancer cells' transcriptome modification dependent of the cellular subtype. Personalized medicine strategy might benefit from network analysis revealing the subtype specific nodes to target to disrupt acquired pro-metastatic profile.

Longitudinal study of recurrent metastatic melanoma cell lines underscores the individuality of cancer biology.

Recurrent metastatic melanoma provides a unique opportunity to analyze disease evolution in metastatic cancer. Here, we followed up eight patients with an unusually prolonged history of metastatic melanoma, who developed a total of 26 recurrences over several years. Cell lines derived from each metastasis were analyzed by comparative genomic hybridization and global transcript analysis. We observed that conserved, patient-specific characteristics remain stable in recurrent metastatic melanoma even after years and several recurrences. Differences among individual patients exceeded within-patient lesion variability, both at the DNA copy number (P<0.001) and RNA gene expression level (P<0.001). Conserved patient-specific traits included expression of several cancer/testis antigens and the c-kit proto-oncogene throughout multiple recurrences. Interestingly, subsequent recurrences of different patients did not display consistent or convergent changes toward a more aggressive disease phenotype. Finally, sequential recurrences of the same patient did not descend progressively from each other, as irreversible mutations such as homozygous deletions were frequently not inherited from previous metastases. This study suggests that the late evolution of metastatic melanoma, which markedly turns an indolent disease into a lethal phase, is prone to preserve case-specific traits over multiple recurrences and occurs through a series of random events that do not follow a consistent stepwise process.

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

BACKGROUND: Ovarian cancer is the most deadly gynecological cancer because of late diagnosis, frequently with diffuse peritoneal metastases. Recent findings have shown that serous epithelial ovarian cancer has a narrow mutational spectrum with TP53 being the most frequently targeted when single genes are considered. It is, however, important to understand which pathways as a whole may be targeted for mutation. FINDINGS: Previously published mutational data provided by the cancer genome atlas networks findings on ovarian cancer was searched for statistically significant enrichment of genes in pathways. These pathways were then searched in all patients to identify the spectrum of mutations. Statistical significance was further shown through in-silico permutations of exome sequences using empirically observed mutation rates. We detected mutations in the cell adhesion pathway genes in more than 89% of serous epithelial ovarian cancer patients. This level of near universal mutational targeting of the cell adhesion pathway, including the extracellular matrix pathway, is previously unreported in epithelial ovarian cancer. CONCLUSIONS: Taken together with previous studies on the role of cell adhesion and extracellular matrix gene expression in ovarian cancer and metastasis, our results identify pathways for which the mutational prevalence has previously been overlooked using single gene approaches. Analysis of mutations at the pathway level will be critical in studying heterogeneous diseases such as ovarian cancer.

Mesenchymal cell interaction with ovarian cancer cells triggers pro-metastatic properties.

Tumor microenvironment is an important actor of ovarian cancer progression but the relations between mesenchymal cells and ovarian cancer cells remain unclear. The objective of this study was to determine the ovarian cancer cells' biological modifications induced by mesenchymal cells. To address this issue, we used two different ovarian cancer cell lines (NIH:OVCAR3 and SKOV3) and co-cultured them with mesenchymal cells. Upon co-culture the different cell populations were sorted to study their transcriptome and biological properties. Transcriptomic analysis revealed three biological-function gene clusters were enriched upon contact with mesenchymal cells. These were related to the increase of metastatic abilities (adhesion, migration and invasion), proliferation and chemoresistance in vitro. Therefore, contact with the mesenchymal cell niche could increase metastatic initiation and expansion through modification of cancer cells. Taken together these findings suggest that pathways involved in hetero-cellular interaction may be targeted to disrupt the acquired pro-metastatic profile.

Gene expression analysis of matched ovarian primary tumors and peritoneal metastasis.

BACKGROUND: Ovarian cancer is the most deadly gynecological cancer due to late diagnosis at advanced stage with major peritoneal involvement. To date most research has focused on primary tumor. However the prognosis is directly related to residual disease at the end of the treatment. Therefore it is mandatory to focus and study the biology of metastatic disease that is most frequently localized to the peritoneal cavity in ovarian cancer. METHODS: We used high-density gene expression arrays to investigate gene expression changes between matched primary and metastatic (peritoneal) lesions. RESULTS: Here we show that gene expression profiles in peritoneal metastasis are significantly different than their matched primary tumor and these changes are affected by underlying copy number variation differences among other causes. We show that differentially expressed genes are enriched in specific pathways including JAK/STAT pathway, cytokine signaling and other immune related pathways. We show that underlying copy number variations significantly affect gene expression. Indeed patients with important differences in copy number variation displayed greater gene expression differences between their primary and matched metastatic lesions. CONCLUSIONS: Our analysis shows a very specific targeting at both the genomic and transcriptomic level to upregulate certain pathways in the peritoneal metastasis of ovarian cancer. Moreover, while primary tumors use certain pathways we identify distinct differences with metastatic lesions. The variation between primary and metastatic lesions should be considered in personalized treatment of ovarian cancer.