Profiling Of Ovarian Cancer Research Articles

Screening of potential targets and small-molecule drugs related to lipid metabolism in ovarian cancer based on bioinformatics

Backgroundabout 70 % of ovarian cancer (OC) patients with postoperative chemotherapy relapse within 2–3 years due to drug resistance and metastasis, and the 5-year survival rate is only about 30 %. Lipid metabolism plays an important role in OC. We try to explore the potential targets and drugs related to lipid metabolism to provide clues for the treatment of OC. Methodsthe gene expression profiles of OC and normal ovarian tissue samples were obtained from the cancer genome atlas (TCGA) and genotype-tissue expression databases (GTEx). The differentially expressed genes (DEGs) were analyzed. Lipid metabolism related genes (LMRGs) were downloaded from MSigDB database. The DEGs related to lipid metabolism in OC was obtained by intersection. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were performed. The protein-protein interaction (PPI) network of lipid metabolism related DEGs was constructed, and seven algorithms were used to screen core potential target genes. Its expression in OC and prognostic ability were analyzed by Univariate Cox. Cmap database mining OC lipid metabolism related potential small-molecular drugs and docking. CCK8, scratch assay, transwell test and free fatty acid (FFA) assay, fluorescence detection of cellular fatty acid uptake, and the reactivity assay of CPT1A were used to detect the biological effects of drugs on OC cell.Rreverse transcription PCR(RT-qPCR) and WesternBlot were performed to measure the expression of core targets. Results437 DEGs related to lipid metabolism of OC were screened. GO and KEGG analysis showed that these DEGs were lipid metabolism, fatty acid metabolism, sphingolipid metabolism, PPAR signal pathway and so on. The PPI network based on lipid metabolism DEGs consists of 301 nodes and 1107 interaction pairs, and 6 core target genes were screened. ROC curve analysis showed that all of the 6 genes could predict the prognosis of OC. Three small molecular drugs Cephaeline, AZD8055 and GSK-1059615 were found by cmap and molecular docking showed that they all had good binding ability to target gene. Cephaeline has the strongest inhibitory effect on SKOV3 cells of OC, and could significantly inhibit cell migration and invasion regulate the mRNA and protein expression of some targets, and inhibit lipid metabolism process in ovarian cancer cells. Conclusionsix OC potential genes related to lipid metabolism were identified and verified, which can be used as potential biomarkers and therapeutic targets to evaluate the prognostic risk of OC patients. In addition, three small-molecular drugs that may be effective in the treatment of OC were unearthed, among which Cephaeline has the most potential. We speculate that Cephaeline may target six genes to inhibit progression of OC by affecting lipid metabolism.

PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism.

Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients. First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the "glmnet" R package. The nomogram was created via the "regplot." Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs' roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using "pRRophetic," the sensitivity of eight regularly used medications and immunotherapy was anticipated. 125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk (P < 0.05). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable (P = 0.015). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide (P < 0.001). PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Abstract 5789: Multi-omic artificial intelligence outcome modeling of ovarian cancer, phase I: Whole exome and whole transcriptome data

Abstract Background: Over a decade ago, the Cancer Genome Atlas (TCGA) provided the initial genomic characterization of ovarian cancer with targeted exome capture and sequencing. Unlike other TCGA analysis, they were unable to identify prognostic mutational profiles outside of BRCA status. There has been limited characterization of the ovarian cancer genomic profile since. Our objective presented here was to perform whole exome and whole transcriptome analysis of 241 ovarian cancer samples and compare to the TCGA dataset. This is the first phase of a muti-step ongoing analysis wherein we will input and correlate the complete genomic profile, complete patient outcome data, and immunohistochemical staining into a multi-omic artificial intelligence (AI) machine learning model with the aim of improved individualized outcome prediction. Methods: Tumor samples from 2000-2016 were obtained at time of surgery under approved University of Pittsburgh approved IRB consent. Whole exome and whole transcriptome sequencing were performed in collaboration with Helomics Corporation. Sequencing analysis was compared to TCGA data. Results: 241 patient samples underwent sequencing analysis. Similar to TCGA, most mutations were missense. I am having a difficulty interpreting the magee vs tcga comparison graphs, how to discuss the TP53 and BRCA comparison without using the lollipop charts and highlighting the rna-seq data into written form. Please add. Conclusions: We present our findings of one of the largest molecular characterizations of ovarian cancer. Similar to the TCGA, there is noted heterogeneity to the genomic profile of ovarian cancer. Multi-omic AI outcome modeling has the potential to overcome the gap defining prognostic sub-groups so that we can tailor therapies to the individual Citation Format: Brian Orr, Robert P. Edwards, Mackenzy Radolec. Multi-omic artificial intelligence outcome modeling of ovarian cancer, phase I: Whole exome and whole transcriptome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5789.

Abstract LB125: The role of succinate dehydrogenase in ovarian cancer metabolism

Abstract The reprogramming of cellular metabolism is an important hallmark of cancer allowing tumor cells to survive and proliferate. Recent data revealed that subsets of ovarian cancer with invasive and metastatic phenotype are highly metabolically active with increased mitochondrial respiration. To better understand the metabolic profiles of ovarian cancer, we utilized patient-derived xenografts (PDXs) derived from high-grade serous ovarian carcinoma (HGSOC), and performed comprehensive analysis of metabolic pathways by Reverse Phase Protein Array (RPPA). The RPPA analysis revealed PDX subsets with high or low mitochondrial activity and metabolism, and we identified succinate dehydrogenase (SDHA) to be a top differentially expressed metabolic protein between those PDX subsets. SDHA is a mitochondrial enzyme that participates in the TCA cycle and the electron transport chain, which are both essential for cellular metabolism. Analysis of the TCGA data demonstrated that SDHA gene alterations are highly prevalent in HGSOC patients (19% cases). Despite these findings, the impact of SDHA amplification on cancer biology is poorly understood. To assess the effect of SDHA overexpression on energy metabolism, we used SDHA-overexpressing ovarian cancer cell lines and measured cellular respiration by Seahorse. The results revealed that the overexpression of SDHA leads to a significant increase in basal and maximal respiration shifting cellular metabolism towards increased oxidative phosphorylation in mitochondria. Further, we determined if SDHA overexpression redirects cellular metabolism in response to deprivation of selected nutrients. In the conditions with low glucose or glutamine, SDHA overexpressing cells significantly increased basal respiration when compared with controls. In galactose medium that disrupts glycolysis, the SDHA lost its ability to induce cellular respiration, which indicates that SDHA relays on glycolysis to increase mitochondrial respiration. Together, our data indicate that SDHA requires glycolysis to fuel TCA cycle and and support cellular respiration. Next, we performed in vitro drug screening and found that SDHA overexpressing cells are highly sensitive to glycolysis inhibitor Shikonin. Our pilot in vivo data showed that Shikonin significantly reduces growth of SDHA-amplified tumors. In summary, our studies indicate that SDHA upregulation plays a role in reprogramming of cellular metabolism and pathogenesis of ovarian cancer. Moreover, the unique metabolic state of ovarian cancer associated with SDHA amplification could be successfully targetable offering a potential new treatment strategy for ovarian cancer patients. Citation Format: Magdalena Cybula, Maria Rostworowska, Lin Wang, Summer Wang, Ana Luiza Drumond-Bock, Magdalena Bieniasz. The role of succinate dehydrogenase in ovarian cancer metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB125.

Expression Profiling in Ovarian Cancer Reveals Coordinated Regulation of BRCA1/2 and Homologous Recombination Genes.

Predictive biomarkers are crucial in clarifying the best strategy to use poly(ADP-ribose) polymerase inhibitors (PARPi) for the greatest benefit to ovarian cancer patients. PARPi are specifically lethal to cancer cells that cannot repair DNA damage by homologous recombination (HR), and HR deficiency is frequently associated with BRCA1/2 mutations. Genetic tests for BRCA1/2 mutations are currently used in the clinic, but results can be inconclusive due to the high prevalence of rare DNA sequence variants of unknown significance. Most tests also fail to detect epigenetic modifications and mutations located deep within introns that may alter the mRNA. The aim of this study was to investigate whether quantitation of BRCA1/2 mRNAs in ovarian cancer can provide information beyond the DNA tests. Using the nCounter assay from NanoString Technologies, we analyzed RNA isolated from 38 ovarian cancer specimens and 11 normal fallopian tube samples. We found that BRCA1/2 expression was highly variable among tumors. We further observed that tumors with lower levels of BRCA1/2 mRNA showed downregulated expression of 12 additional HR genes. Analysis of 299 ovarian cancer samples from The Cancer Genome Atlas (TCGA) confirmed the coordinated expression of BRCA1/2 and HR genes. To facilitate the routine analysis of BRCA1/2 mRNA in the clinical setting, we developed a targeted droplet digital PCR approach that can be used with FFPE samples. In conclusion, this study underscores the potential clinical benefit of measuring mRNA levels in tumors when BRCA1/2 DNA tests are negative or inconclusive.

Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Signature for Ovarian Cancers.

Ovarian cancer (OC), one of the most common malignancies of the female reproductive system, is characterized by high incidence and poor prognosis. Tumor mutation burden (TMB), as an important biomarker that can represent the degree of tumor mutation, is emerging as a key indicator for predicting the efficacy of tumor immunotherapy. In our study, the gene expression profiles of OC were downloaded from TCGA and GEO databases. Subsequently, we analyzed the prognostic value of TMB in OC and found that a higher TMB score was significantly associated with a better prognosis (p = 0.004). According to the median score of TMB, 9 key TMB related immune prognostic genes were selected by LASSO regression for constructing a TMB associated immune risk score (TMB-IRS) signature, which can effectively predict the prognosis of OC patients (HR = 2.32, 95% CI = 1.68–3.32; AUC = 0.754). Interestingly, TMB-IRS is also closely related to the level of immune cell infiltration and immune checkpoint molecules (PD1, PD-L1, CTLA4, PD-L2) in OC. Furthermore, the nomogram combined with TMB-IRS and a variety of clinicopathological features can more comprehensively evaluate the prognosis of patients. In conclusion, we explored the relationship between TMB and prognosis and validated the TMB-IRS signature based on TMB score in an independent database (HR = 1.60, 95% CI = 1.13–2.27; AUC = 0.639), which may serve as a novel biomarker for predicting OC prognosis as well as possible therapeutic targets.

Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art and Future Perspectives.

Simple SummaryOvarian cancer (OC) represents the fifth leading cause of cancer-related deaths among women. In the advanced disease setting, OC recurrence after chemotherapy is over 70% in the first 2 years, with few therapeutic options. Immunotherapy with the immune checkpoint inhibitors (ICIs) showed high efficacy and changed the therapeutic scenario of many tumors in the last 10 years. With our review, we aimed to summarize the clinical trials of ICIs in OC. In OC, ICIs clinical trials have reported poor outcomes in terms of patient response and survival, with some studies failing to reach their objectives. Combining immunotherapy with drugs targeting different pathways might enhance efficacy and overcome cancer resistance. The search for biomarkers predicting ICIs response is essential for the identification of patients most likely to benefit from ICI therapy.Background: Ovarian cancer (OC) represents the eighth most common cancer and the fifth leading cause of cancer-related deaths among the female population. In an advanced setting, chemotherapy represents the first-choice treatment, despite a high recurrence rate. In the last ten years, immunotherapy based on immune checkpoint inhibitors (ICIs) has profoundly modified the therapeutic scenario of many solid tumors. We sought to summarize the main findings regarding the clinical use of ICIs in OC. Methods: We searched PubMed, Embase, and Cochrane Databases, and conference abstracts from international congresses (such as ASCO, ESMO, SGO) for clinical trials, focusing on ICIs both as monotherapy and as combinations in the advanced OC. Results: 20 studies were identified, of which 16 were phase I or II and 4 phase III trials. These trials used ICIs targeting PD1 (nivolumab, pembrolizumab), PD-L1 (avelumab, aterolizumab, durvalumab), and CTLA4 (ipilimumab, tremelimumab). There was no reported improvement in survival, and some trials were terminated early due to toxicity or lack of response. Combining ICIs with chemotherapy, anti-VEGF therapy, or PARP inhibitors improved response rates and survival in spite of a worse safety profile. Conclusions: The identification of biomarkers with a predictive role for ICIs’ efficacy is mandatory. Moreover, genomic and immune profiling of OC might lead to better treatment options and facilitate the design of tailored trials.

PSMC2/CCND1 axis promotes development of ovarian cancer through regulating cell growth, apoptosis and migration

Ovarian cancer is known as one of the most common malignancies of the gynecological system, whose treatment is still not satisfactory because of the unclear understanding of molecular mechanism. PSMC2 is an essential component of 19 S regulatory granules in 26 S proteasome and its relationship with ovarian cancer is still not clear. In this study, we found that PSMC2 was upregulated in ovarian cancer tissues, associated with tumor grade and could probably predict poor prognosis. Knocking down the endogenous PSMC2 expression in ovarian cancer cells could decrease colony formation ability, cell motility and cell proliferation rate, along with increasing cell apoptosis rate. Cells models or xenografts formed by cells with relatively lower expression of PSMC2 exhibited weaker oncogenicity and slower growth rate in vivo. Moreover, gene microarray was used to analyze the alteration of gene expression profiling of ovarian cancer induced by PSMC2 knockdown and identify CCND1 as a potential downstream of PSMC2. Further study revealed the mutual regulation between PSMC2 and CCND1, and demonstrated that knockdown of CCND1 could enhance the regulatory effects induced by PSMC2 knockdown and overexpression of CCND1 reverses it. In summary, PSMC2 may promote the development of ovarian cancer through CCND1, which may predict poor prognosis of ovarian cancer patients.

A pancancer overview of FBN1, asprosin and its cognate receptor OR4M1 with detailed expression profiling in ovarian cancer.

Ovarian cancer affects >295,000 women worldwide and is the most lethal of gynaecological malignancies. Often diagnosed at a late stage, current research efforts seek to further the molecular understanding of its aetiopathogenesis and the development of novel biomarkers. The present study investigated the expression levels of the glucogenic hormone asprosin [encoded by fibrillin-1 (FBN1)], and its cognate receptor, olfactory receptor 4M1 (OR4M1), in ovarian cancer. A blend of in silico open access The Cancer Genome Atlas data, as well as in vitro reverse transcription-quantitative PCR (RT-qPCR), immunohistochemistry and immunofluorescence data were used. RT-qPCR revealed expression levels of OR4M1 and FBN1 in clinical samples and in ovarian cancer cell lines (SKOV-3, PEO1, PEO4 and MDAH-2774), as well as the normal human ovarian surface epithelial cell line (HOSEpiC). Immunohistochemical staining of a tissue microarray was used to identify the expression levels of OR4M1 and asprosin in ovarian cancer samples of varying histological subtype and grade, including clear cell carcinoma, serous ovarian cancer and mucinous adenocarcinoma. Immunofluorescence analysis revealed asprosin expression in SKOV-3 and HOSEpiC cells. These results demonstrated the expression of both asprosin and OR4M1 in normal and malignant human ovarian tissues. This research invokes further investigation to advance the understanding of the role of asprosin and OR4M1 within the ovarian tumour microenvironment.

Mutational profiles of ovarian cancer in Chinese patients revealed potential therapeutic targets and prognostic markers.

e17525 Background: Ovarian cancer (OC) is a common and lethal gynecologic malignancy. The prognosis of OC is variable among different patients treated with standard of care therapies. Herein, we described mutational profiles of OC to identify underlying therapeutic targets and prognostic markers. Methods: The study was performed in 38 Chinese patients with high-grade serous ovarian cancer (HGSC), the most common subtype of OC. Most patients (86.8%) had advanced disease (stage III-IV). Tissue samples were subjected to capture-based targeted sequencing using a panel consisting of 520 cancer related genes. Mutational profiles including gene mutations and copy number variations (CNVs) were evaluated in each patient. Homologous recombination deficiency (HRD) status was also assessed. Analysis of mutational profile with platinum-sensitivity, progression-free survival (PFS) and platinum-free interval (PFI) were performed. Results: Genetic alterations were mainly identified in TP53 (97%), BRCA1 (24%), RB1 (21%), FGF23 (21%), CCND2 (18%), RECQL4 (18%) and NF1 (16%). CNVs were comprehensively distributed in 242 genes with 76% (29/38) of patients harboring at least one CNV. In addition to BRCA1, genetic alterations were also presented in other homologous recombination repair (HRR) genes including CDK12 (5%), BRCA2 (3%), ATM (3%), BRIP1 (3%), CHEK1 (3%) and FANCI (3%). There were 22 of 38 (58%) patients with genetic alterations of the HRR pathway. In the study, 28 patients were platinum-sensitive (74%) and 10 were platinum-resistant (26%). Platinum-sensitivity was significantly associated with BRCA1/2 mutations (p &lt; 0.01). In platinum-resistant patients, 7 of 10 patients harbored genetic alterations of actionable therapeutic targets such as PIK3CA, TSC1 and HER2 alterations. The prognosis analysis indicated that BRCA1/2 mutations were significantly associated with improved PFI (p &lt; 0.05) and marginally associated with improved PFS (p = 0.05). Although no association was observed between HRD status and patient prognosis, subgroup analysis in patients with R0 resection found positive HRD showing significant association with better PFI (p &lt; 0.05) and marginal association with better PFS (p = 0.06). Further analysis of HRD classified by NF1 revealed different PFS and PFI among patients harboring positive HRD, negative HRD with NF1 mutations and negative HRD with wild type NF1 (p &lt; 0.05). The study also observed association of LRP1B mutations and RAD52 amplification with worse PFS (p &lt; 0.05). Conclusions: In OC patients, genetic mutations were frequently occurred in both HRR and non HRR genes. CNVs were widely presented in many genes and patients. The mutational profiling also identified a number of potential therapeutic targets and prognostic markers at molecular level which could contribute to personalized treatment and management of OC.

Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer.

Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Background: Ovarian cancer (OC) is the gynecologic malignant tumor with the highest mortality rate. The interaction between autophagy and the tumor immune microenvironment(TIM) has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Methods: We obtained ARGs from the MSigDB database and downloaded the expression profile of OC from the Cancer Genome Atlas (TCGA) database. The k-means unsupervised clustering method was applied to classify patients. The single-sample gene set enrichment analysis (ssGSEA) method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. Next, we established a risk signature by least absolute shrinkage and selection operator (LASSO) Cox regression. Time-dependent receiver operating characteristic (ROC) curve analysis, Kaplan-Meier survival analysis and univariate Cox regression were used to evaluate the efficiency of the risk signature. Then, we analyzed the correlation between the risk score and 7 ARGs and immune checkpoints. Finally, we selected ULK2 and GABARAPL1 and verified their expression by qRT-PCR, WB and immunohistochemistry. Findings: We divided patients into the cluster A and cluster B subtypes. Our research confirmed that the patients in cluster A have a significantly longer survival time and a higher level of immune infiltration than the patients in cluster B. We established a signature for risk stratification, according to which we could divide the patients into high-or low-risk groups and predict their outcomes. Clinical correlation analysis found that the risk score is more reliable than other indicators for predicting disease-free survival (DFS). ULK2 and GABARAPL1 may play important roles in ovarian tumorigenesis and may be potential therapeutic targets in OC. Interpretation: The autophagy-related gene risk signature might be effective for risk stratification in OC. Moreover, our study provides a promising prognostic indicator that may predict the clinical efficacy of immunotherapy treatments. Funding Statement: This work was supported by the project of degree and postgraduate education and teaching reform in Hunan Province (JG2018A003),the National Science Foundation of China (81874137),the National Science Foundation of Hunan Province (2019JJ50308). the Outstanding Youth Foundation of Hunan Province (2018JJ1047), the Hunan Province Science and technology talent promotion project (2019TJ-Q10). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained at all participating sites, and all the participants provided signed, written, informed consent.

Abstract 4156: Molecular profiling of ovarian cancer by targeted proteomics to inform personalized therapy

Abstract Chemotherapy is the mainstay for the treatment of ovarian cancer. Taxanes, platinum salts, 5-FU, anthracyclines, gemcitabine are used extensively in ovarian cancer, however, there is no biomarker of chemotherapy that is routinely used. We examined 169 ovarian cancer samples using targeted proteomics for biomarkers of response or resistance to chemotherapy agents. Biomarkers of resistance include ERCC1 (Platinum), TUBB3 (taxanes), ALDH1A1 (cyclophosphamide) while response biomarkers include TOPO1 (irinotecan, topotecan), TOPO2A (doxorubicin, epirubicin), hENT1 (Gemcitabine).We also measure markers for several antibody-drug conjugates (FR-alpha, Her2, Trop2, gPNMB, MSLN). Methods: Tumor areas from Formalin-fixed, paraffin-embedded (FFPE) tumor tissues from clinical samples of ovarian cancer that were received at our CLIA certified laboratory were microdissected and a selected reaction monitoring mass spectrometry (SRM-MS) quantitative proteomic analysis of 72 biomarkers were conducted. Discussion: The majority of ovarian cancer samples expressed a range of resistance markers for cyclophosphamide (ALDH1A1: 87% positive ranging from 227-10766 amol/µg), platinum agents (70% positive) and taxanes (71%positive). However, they also expressed a range of response biomarkers for chemotherapies that are conventionally used to treat ovarian cancer. These include irinotecan/topotecan (TOPO1: 97% positive ranging from 459 -3299), doxorubicin (TOPO2A: 50% positive ranging from 402-3825 amol/µg), gemcitabine (hENT1/RRM1: 42% positive). Novel chemotherapy agents that could potentially be used include temozolomide (40% of patients did not express MGMT, resistance marker for temozolomide). The vast majority (78%) of ovarian cancer samples did not express any drug efflux pump proteins MRP and MDR1. Examination of potential ADC markers revealed 74% positivity for the antibody target FR-alpha with a 42 fold range of expression (585 -25000 amol/µg) and 71% positivity for the payload resistance marker TUBB3. Similarly, Trop2 (56% positivity) exhibited a wide dynamic range (222-12778 amol/µg). Another ADC target mesothelin was expressed in 66% of the cases with a 35x range of expression (302 - 10,700 amol/µg). While 56% of ovarian cancer expressed Her2 (262 -5011 amol/µg), only 4% expressed high levels of Her2 (&amp;gt;750 amol/µg), making them suitable for current anti-Her2 therapy. Others could potentially benefit from clinical trials targeting low Her2 expression. The ability to multiplex 72 protein biomarkers from 2-3 FFPE sections provides immense actionable information on clinical treatment or for patient stratification for clinical trials. Citation Format: Sheeno P. Thyparambil, Wei-Li Liao, Eunkyung An, Anuja Bhalkikar, Thomas Guiel, Robert Heaton. Molecular profiling of ovarian cancer by targeted proteomics to inform personalized therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4156.

Epidemiological Profile and Risk Factors of Ovarian Cancer in Ibn Rochd University Hospital of Casablanca Morocco: About 131 Cases

Introduction: Ovarian cancer is one of the leading causes of morbidity and mortality in women worldwide, generally characterized by a poor prognosis. Most cases are diagnosed at an advanced stage. The standardized incidence rate for the world population is 8.1/100,000 women per year, with an estimated 239,000 new cases and 152,000 deaths per year worldwide. Methods: This study aims to evaluate the epidemiological characteristics and risk factors of ovarian cancer by studying the risk factors of this pathology on a sample of 131 cases followed at the Ibn Rochd University Hospital in Casablanca from 2010 to 2012 using SPSS version 16.0 software. Results: The average age of the patients is 51.47±13 years, the significant risk factors observed are multiparity (64.3%), menopause (58%) as personal history and hypertension (77.78%) as medical history. The majority of the patients (60%) came from urban areas, (50.4%) had cystic looking tumors (34.4%) on the right side, cystadenocarcinoma (20.6%) was the most frequent histological type, (74.8%) metastases were noted, including (44.3%) peritoneal carcinosis. Conclusion: We concluded that the epidemiological profile of ovarian cancer is close to national and Maghreb data. A better biological investigation as well as an adequate management would allow its early diagnosis and the reduction of its incidence.

Germline and somatic mutations in 25 hereditary breast and ovarian cancer related genes and platinum-based chemotherapy response among Chinese patients with epithelial ovarian cancer.

e18088 Background: Insight on the germline and somatic mutation in BRCA1/2 was predictable on the sensitivity on platinum-based therapy. The therapeutic impact of mutations in other hereditary breast and ovarian cancer related genes is uncertain. Methods: We sought to investigate the germline and somatic deleterious mutations through all exons in 25 hereditary breast and ovarian cancer related genes in paired blood and frozen tumor samples from 235 Chinese women diagnosed with epithelial ovarian carcinomas. Results: Forty-seven subjects (24.6%) had a germline deleterious mutation, and 126 subjects (66%) had a somatic deleterious mutation. Thirty-four (17.8%) had both a germline and somatic mutation. Of the 203 germline and somatic deleterious mutations, 114 (56.2%) occurred in TP53, 41 (20.2%) in BRCA1, 11 (5.4%) in BRCA2 and 37 (23.6%) in 13 other genes: ATM, BARD1, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, NF1, PALB2, PTEN, RAD51C and, STK11. In 235 patients, 215 (91.5%) and 20 (8.5%) cases were sensitive and resistant to the platinum-based chemotherapy. In the whole population, homologous recombination repair (HRR) mutations, non- homologous recombination repair(non-HRR) mutations, mismatch repair (MMR) mutations had no impact on the platinum-based chemotherapy response. No significant difference in platinum response were found in HRR, non-HRR and MRR mutation(P = 0.788). In 235 patients, ones with ATM (10), CHEK2(9), NF1(5), PALB2(8), PTEN (8) mutations were available for platinum response information; Among them, there were three patients with either ATM, CHEK2 or PALB2 mutation presented platinum resistance. Conclusions: Multi-gene panel testing of germline and somatic HBOC related gene mutations was feasible to characterize a comprehensive molecular profile of ovarian cancer and showed non-BRCA gene stratification potential value in predicting patient’s response for platinum-based chemotherapy.

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

RelevanceMolecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.PurposeThis study sought to identify key pathway–network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.MethodsIngenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule–based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model.ResultsPathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs (TPM1, CALR, GSTP1, LYN, AKAP12, and CPT2) in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models (HIST1H2BK, ALB, RRAS2, HIBCH, EIF3E, RPS20, and RPL23A) to assess OC survival risks.ConclusionThese findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine.

The effects of Wnt inhibition on tumor progression and the tumor microenvironment in a syngeneic mouse model of ovarian cancer.

e17078 Background: Alterations in the Wnt/β-catenin pathway have been associated with tumor progression in multiple cancers. Gene expression profiling in ovarian cancer has correlated Wnt upregulation to patterns of immune evasion in the tumor microenvironment (TME). We investigated effects of in vitro Wnt inhibition in a murine ovarian cancer cell line with p53 knocked out as a model to mimic human ovarian carcinoma. Methods: We used a small molecule inhibitor, CGX-1321, in C57Bl6 mice, an immunocompetent mouse model, injected intraperitoneally with either ID8 cells, a murine ovarian cancer cell line, or ID8-p53-/- cells. Mouse omentums were collected and weighed for evaluation of tumor growth. Representative histology slides were stained for H&amp;E. Western blots for β-catenin baseline levels were performed on the cell lines. TopFlash Assays with WNT3A stimulation were used for analysis of cell line nuclear β-catenin levels. Flow cytometry was performed on mouse omentums for TME evaluation. Results: Treatment with CGX-1321 decreased tumor size via omentum weight with both cell lines, p = 0.0098 with ID8 cells and p = 0.0855 with ID8-p53-/- cells. H&amp;E slides revealed a decreased tumor progression. Western blots confirmed a difference in baseline β-catenin levels between these two cell lines. However, TopFlash assays showed a response to stimulation. Changes in the TME between treated samples with ID8-p53-/- cells did now show significant differences via flow cytometry. Conclusions: In the syngeneic mouse model with a murine ovarian cancer cell line, with p53 knocked out, tumor size and proliferation were decreased with treatment with Wnt inhibition. Surprisingly, the TME changes were inconclusive via flow cytometry. Perhaps these cells do not rely upon the Wnt/ β-catenin pathway as heavily, as seen in changes to baseline β-catenin levels, or perhaps tumors progress quicker and the critical time point of TME change is being overlooked. More investigation needs to be performed to further elucidate these differences.

Genomic profiling in ovarian cancer retreated with platinum based chemotherapy presented homologous recombination deficiency and copy number imbalances of CCNE1 and RB1 genes

BackgroundOvarian carcinomas presenting homologous recombination deficiency (HRD), which is observed in about 50% of cases, are more sensitive to platinum and PARP inhibitor therapies. Although platinum resistant disease has a low chance to be responsive to platinum-based chemotherapy, a set of patients is retreated with platinum and some of them are responsive. In this study, we evaluated copy number alterations, HR gene mutations and HR deficiency scores in ovarian cancer patients with prolonged platinum sensitivity.MethodsIn this retrospective study (2005 to 2014), we selected 31 patients with platinum resistant ovarian cancer retreated with platinum therapy. Copy number alterations and HR scores were evaluated using the OncoScan® FFPE platform in 15 cases. The mutational profile of 24 genes was investigated by targeted-NGS.ResultsThe median values of the four HRD scores were higher in responders (LOH = 15, LST = 28, tAI = 33, CS = 84) compared with non-responders (LOH = 7.5, LST = 17.5, tAI = 23, CS = 47). Patients with high LOH, LST, tAI and CS scores had better response rates, although these differences were not statistically significant. Response rate to platinum retreatment was 22% in patients with CCNE1 gains and 83.5% in patients with no CCNE1 gains (p = 0.041). Furthermore, response rate was 54.5% in patients with RB1 loss and 25% in patients without RB1 loss (p = 0.569). Patients with CCNE1 gains showed a worse progression free survival (PFS = 11.1 months vs 3.7 months; p = 0.008) and a shorter overall survival (OS = 39.3 months vs 7.1 months; p = 0.007) in comparison with patients with no CCNE1 gains. Patients with RB1 loss had better PFS (9.0 months vs 2.6 months; p = 0.093) and OS (27.4 months vs 3.6 months; p = 0.025) compared with cases with no RB1 loss. Four tumor samples were BRCA mutated and tumor mutations were not associated with response to treatment.ConclusionsHR deficiency was found in 60% of our cases and HRD medium values were higher in responders than in non-responders. Despite the small number of patients tested, CCNE1 gain and RB1 loss discriminate patients with tumors extremely sensitive to platinum retreatment.

Comprehensive circular RNA expression profiles and the tumor-suppressive function of circHIPK3 in ovarian cancer.

With the development of next-generation sequencing (NGS), thousands of circular RNAs (circRNAs) have been found. Many circRNAs have been verified to play vital roles in carcinogenesis. However, whether circRNAs engage in the development and progression of ovarian cancer remains to be clarified. We analyzed circRNA expression profiling in epithelial ovarian cancer (EOC) and normal ovarian tissues (NOT) using NGS and validated six randomly selected circRNAs via quantitative real-time-PCR (qRT-PCR), reverse-transcription PCR (RT-PCR) and Sanger sequencing after RNase treatment. CircHIPK3, the most abundant circRNA in our sequencing data, was further knocked down by siRNA. The circHIPK3 function in proliferation, invasion, migration and apoptosis of ovarian cancer cells and normal ovarian epithelial cells was analyzed via cell counting-kit 8 (CCK8), wound healing, transwell and flow cytometry analyses after circHIPK3 was efficiently silenced. Altogether, we found 7333 circRNAs, of which 4505 (61.43%) were newly identified, 2431 were significantly upregulated and 3120 were remarkably downregulated. Six randomly selected differentially expressed circRNAs were examined in 18 EOC and 18 NOT. Furthermore, the results of RT-PCR and Sanger sequencing after RNase treatment confirmed head-to-tail back-splicing. Silencing of circHIPK3 promoted proliferation, migration, and invasion and inhibited apoptosis of ovarian cancer cells (A2780 and SKOV3) and normal ovarian epithelial cells (IOSE80). Additionally, the circHIPK3-miRNA-mRNA axis was predicted as the possible mechanism using bioinformatic approaches. We identified the circRNA expression profile in ovarian cancer tissues and further verified the existence and expression of six randomly selected differentially expressed circRNAs. Besides, we also found that circHIPK3 is an important regulator of ovarian cancer progression.

A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients.

Ovarian cancer (OC) is the most common gynecologic malignancy with high incidence and mortality. The present study aimed to develop approaches for determining the recurrence type and identify potential miRNA markers for OC prognosis. The miRNA expression profile of OC (the training set, including 390 samples with recurrence information) was downloaded from The Cancer Genome Atlas database. The validation sets GSE25204 and GSE27290 were obtained from the Gene Expression Omnibus database. Prescreening of clinical factors was conducted using the survival package, and the differentially expressed miRNAs (DE-miRNAs) were identified using the limma package. Using the Caret package, the optimal miRNA set was selected to build a Support Vector Machine (SVM) classifier. The miRNAs and clinical factors independently related to prognosis were analyzed using the survival package, and the risk score system was constructed. Finally, the miRNA-target regulatory network was built by Cytoscape software, and enrichment analysis was performed. There were 46 DE-miRNAs between the recurrent and non-recurrent samples. After the optimal 19-miRNA set was selected for constructing the SVM classifier, 6 DE-miRNAs (miR-193b, miR-211, miR-218, miR-505, miR-508 and miR-514) independently related to prognosis were further extracted to build the risk score system. The neoplasm cancer status was independently correlated with the prognosis and conducted with stratified analysis. Additionally, the target genes in the regulatory network were enriched in the regulation of actin cytoskeleton and the TGF-β signaling pathway. The 6-miRNA signature may serve as a potential biomarker for OC prognosis, particularlyfor recurrence.