Ovarian Tumor Samples Research Articles

Computational Identification of Migrating T cells in Spatial Transcriptomics Data.

T cells are the central players in antitumor immunity, and effective tumor killing depends on their ability to infiltrate into the tumor microenvironment (TME) while maintaining normal cytotoxicity. However, late-stage tumors develop immunosuppressive mechanisms that impede T cell movement and induce exhaustion. Investigating T cell migration in human tumors in vivo could provide novel insights into tumor immune escape, although it remains a challenging task. In this study, we developed ReMiTT, a computational method that leverages spatial transcriptomics data to track T cell migration patterns within tumor tissue. Applying ReMiTT to ovarian tumor samples, we identified key genes and pathways that enriched on algorithm-identified T cell migration trails, including leukocyte chemotaxis, cell-cell adhesion, and ECM remodeling. We also characterized the phenotypes of T cells on the migrating trails, suggesting that regulatory T cells may accompany cytotoxic T cells during migration. Our findings provide a novel approach to studying T cell migration and interactions within the TME, offering new insights into tumor-immune dynamics.

Expression of gonadotropin-releasing hormone receptor type-II correlates with proliferation activity in tissue microarray of rare ovarian tumor

Activation of Gonadotropin-Releasing Hormone type II Receptors (GnRHR-II) exhibits antiproliferative activity. GnRHR-II is not only expressed exclusively in the pituitary, but also in a variety of tumors. To date, the clinical relevance of GnRHR-II in ovarian tumors is unclear. In addition, there is a lack of literature addressing GnRHR-II in ovarian tumors, especially rare types. This study was conducted to investigare the correlation between GnRHR-II expression with clinicopathology and proliferative activity of rare ovarian tumors. The purpose of this study was an analytic observational study with a cross-sectional design that utilized 18 ovarian tumor samples on tissue microarray (TMA). The expression of GnRHR-II and Ki67 was assessed using immunohistochemical staining (IHC) and observed using the IHC profiler plugin ImageJ software to obtain their respective H-scores. The data was analyzed using independent t-test, ANOVA, Pearson's test, and Fisher's exact test based on data types. The value of p<0.05 was considered statistically significant. GnRHR-II is expressed in various forms in ovarian tumors, including extrapituitary expression. GnRHR-II expression was highest in the sex cord stromal tumor (SCST) group, 110.30 ± 23.89 (p<0.0001). In addition, there was also a significant difference between GnRHR-II expression with age (p<0.001) and the primary tumor (p<0.05), but not with tumor type (p=0.101). There is a correlation between GnRHR-II expression and proliferative activity (r=-0.043, p=0.866). Elevated GnRHR-II expression is significantly correlation with SCST, individuals over 40 years of age, and tumors confined to the ovary and it is correlates with lower proliferative activity, although this correlation is very weak.

Tepilamide Fumarate as a Novel Potentiator of Virus-Based Therapy.

Oncolytic virotherapy, using viruses such as vesicular stomatitis virus (VSVΔ51) and Herpes Simplex Virus-1 (HSV-1) to selectively attack cancer cells, faces challenges such as cellular resistance mediated by the interferon (IFN) response. Dimethyl fumarate (DMF) is used in the treatment of multiple sclerosis and psoriasis and is recognized for its anti-cancer properties and has been shown to enhance both VSVΔ51 and HSV-1 oncolytic activity. Tepilamide fumarate (TPF) is a DMF analog currently undergoing clinical trials for the treatment of moderate-to-severe plaque psoriasis. The aim of this study was to evaluate the potential of TPF in enhancing the effectiveness of oncolytic viruses. In vitro, TPF treatment rendered 786-0 carcinoma cells more susceptible to VSVΔ51 infection, leading to increased viral replication. It outperformed DMF in both increasing viral infection and increasing the killing of these resistant cancer cells and other cancer cell lines tested. Ex vivo studies demonstrated TPF's selective boosting of oncolytic virus infection in cancer cells without affecting healthy tissues. Effectiveness was notably high in pancreatic and ovarian tumor samples. Our study further indicates that TPF can downregulate the IFN pathway through a similar mechanism to DMF, making resistant cancer cells more vulnerable to viral infection. Furthermore, TPF's impact on gene therapy was assessed, revealing its ability to enhance the transduction efficiency of vectors such as lentivirus, adenovirus type 5, and adeno-associated virus type 2 across various cell lines. This data underscore TPF's potential role in not only oncolytic virotherapy but also in the broader application of gene therapy. Collectively, these findings position TPF as a promising agent in oncolytic virotherapy, warranting further exploration of its therapeutic potential.

Characterization of innate lymphoid cell subsets infiltrating melanoma and epithelial ovarian tumors

ABSTRACT The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46+ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity.

Shared biological features between ovarian cancer and endometriosis based on co-expression networks analysis

Background: Endometriosis is a gynecologic disease common among younger females, impacts adversely the quality of life, and results in infertility. The evidence demonstrates that patients with endometriosis have a higher risk of developing ovarian cancer. Due to this similarity, we aimed to identify common molecular markers between ovarian cancer and endometriosis. Methods: We used publicly available microarray data (GSE9899) including ovarian tumor samples and alongside normal controls, common differentially expressed genes (DEGs) were identified in ovarian samples compared to controls. We constructed a co-expression network based on ovarian cancer and then extracted the hub genes. We validated the similarities between ovarian cancer and endometriosis by examining the expression of hub genes in endometriosis patients referred to Iranian Rasoul Akram Hospital. Results: In total 1000 DEGs were identified in grade 2 and grade 3 of ovarian cancer. We identified 34 co-expression modules in grade 3 ovarian cancer and 31 in grade 2. Modules were mostly enriched in inflammatory response and plasma membrane-bounded cell projection, mostly. To validate the comorbidity of ovarian cancer and endometriosis, we examined the expression of six significant hubs (resulting in an ovarian cancer co-expression network) in women with endometriosis. Conclusion: Our results suggest that patients with endometriosis have a higher risk of developing ovarian cancer. These findings will improve the diagnosis of ovarian cancer in endometriosis patients. More research is required to find the exact mechanism of hub genes affecting the development of ovarian cancer.

Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer

RAD51C and RAD51D are involved in DNA repair by homologous recombination. Germline pathogenic variants (PVs) in these genes are associated with an increased risk of ovarian and breast cancer. Understanding the homologous recombination deficiency (HRD) status of tumors from patients with germline PVs in RAD51C/D could guide therapeutic decision-making and improve survival. To characterize the clinical and tumor characteristics of germline RAD51C/D PV carriers, including the evaluation of HRD status. This retrospective cohort study included 91 index patients plus 90 relatives carrying germline RAD51C/D PV (n = 181) in Spanish hospitals from January 1, 2014, to December 31, 2021. Genomic and functional HRD biomarkers were assessed in untreated breast and ovarian tumor samples (n = 45) from June 2022 to February 2023. Clinical and pathologic characteristics were assessed using descriptive statistics. Genomic HRD by genomic instability scores, functional HRD by RAD51, and gene-specific loss of heterozygosity were analyzed. Associations between HRD status and tumor subtype, age at diagnosis, and gene-specific loss of heterozygosity in RAD51C/D were investigated using logistic regression or the t test. A total of 9507 index patients were reviewed, and 91 patients (1.0%) were found to carry a PV in RAD51C/D; 90 family members with a germline PV in RAD51C/D were also included. A total of 157 of carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, mainly breast cancer or ovarian cancer. The most prevalent PVs were c.1026+5_1026+7del (11 of 56 [19.6%]) and c.709C>T (9 of 56 [16.1%]) in RAD51C and c.694C>T (20 of 35 [57.1%]) in RAD51D. In untreated breast cancer and ovarian cancer, the prevalence of functional and genomic HRD was 55.2% (16 of 29) and 61.1% (11 of 18) for RAD51C, respectively, and 66.7% (6 of 9) and 90.0% (9 of 10) for RAD51D. The concordance between HRD biomarkers was 91%. Tumors with the same PV displayed contrasting HRD status, and age at diagnosis did not correlate with the occurrence of HRD. All breast cancers retaining the wild-type allele were estrogen receptor positive and lacked HRD. In this cohort study of germline RAD51C/D breast cancer and ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD were explained by retention of the wild-type allele, which was more frequent among estrogen receptor-positive breast cancers. Understanding which tumors are associated with RAD51C/D and HRD is key to identify patients who can benefit from targeted therapies, such as PARP (poly [adenosine diphosphate-ribose] polymerase) inhibitors.

MicroRNA Expression Profiles in Human Samples and Cell Lines Revealed Nine miRNAs Associated with Cisplatin Resistance in High-Grade Serous Ovarian Cancer.

Metastasis and drug resistance are major contributors to cancer-related fatalities worldwide. In ovarian cancer (OC), a staggering 70% develop resistance to the front-line therapy, cisplatin. Despite proposed mechanisms, the molecular events driving cisplatin resistance remain unclear. Dysregulated microRNAs (miRNAs) play a role in OC initiation, progression, and chemoresistance, yet few studies have compared miRNA expression in OC samples and cell lines. This study aimed to identify key miRNAs involved in the cisplatin resistance of high-grade-serous-ovarian-cancer (HGSOC), the most common gynecological malignancy. MiRNA expression profiles were conducted on RNA isolated from formalin-fixed-paraffin-embedded human ovarian tumor samples and HGSOC cell lines. Nine miRNAs were identified in both sample types. Targeting these with oligonucleotide miRNA inhibitors (OMIs) reduced proliferation by more than 50% for miR-203a, miR-96-5p, miR-10a-5p, miR-141-3p, miR-200c-3p, miR-182-5p, miR-183-5p, and miR-1206. OMIs significantly reduced migration for miR-183-5p, miR-203a, miR-296-5p, and miR-1206. Molecular pathway analysis revealed that the nine miRNAs regulate pathways associated with proliferation, invasion, and chemoresistance through PTEN, ZEB1, FOXO1, and SNAI2. High expression of miR-1206, miR-10a-5p, miR-141-3p, and miR-96-5p correlated with poor prognosis in OC patients according to the KM plotter database. These nine miRNAs could be used as targets for therapy and as markers of cisplatin response.

Abstract 7347: A transcriptome correlation network analysis of the high grade serous ovarian tumor immune microenvironment

Abstract High Grade Serous Ovarian Cancer (HGSOC) affects many women and is the most common and lethal histopathological type of ovarian cancer. The molecular subtypes delineated by TCGA–mesenchymal, proliferative, immunoreactive, and differentiated subtypes, offer a sophisticated and innovative framework for HGSOC classification. These subtypes not only provide a detailed molecular characterization but also contribute to understanding the HGSOC Tumor Immune Microenvironment (TIME), comprised of both immune and tumor cells. The interplay within the TIME significantly influences pro- or anti-tumor responses, creating an important link between molecular landscape and dynamic immune-tumor interactions. Currently, there is a lack of understanding for the key drivers of clinical outcomes, molecular phenotypes, and diagnostic biomarkers for HGSOC. Here, we answer the question, does immune cell type, and proportion, drive clinical and molecular subtyping of primary HGSOC? Primary ovarian tumor samples from The Cancer Genome Atlas, TCGA (n=358) samples were collected and RNA-sequenced. FPKM values were used to construct the Weighted Gene Correlation Network Analysis (WGCNA), Gene Ontology (GO) and Proportion Estimations of Immune and Cancer cells (EPIC). Enrichment of HGSOC network module overlaps using a Fischer Exact Test were used to identify novel components of HGSOC progression that were significantly associated with clusters of highly correlated gene expression profiles, HGSOC stage, grade, and molecular phenotypes. We show genes associated with T cell proliferation (CD84) and B cell regulation (CD37), epithelial to mesenchymal transition (DOCK2), adaptor proteins present in T cells and myeloid cells (LCP2), inflammatory and metabolic signaling modulators (AIF1,CD53, SASH3), immunosuppressors of T lymphocytes and dendritic cells (FGL2), cell proliferation (PI3K) and relevant tertiary lymphoid structure gene (TNFAIP8L2) significantly correlated with HGSOC progression in both estimated tumor and proportions of immune cells. Future studies aim to confirm phosphorylation cascades associated with the aggressive molecular phenotypes of HGSOC and immune cell pathobiology. Citation Format: Kaylin M. Carey, Eric B. Dammer, Corey Young, Rajesh Singh, Ti'ara Griffen, James W. Lillard. A transcriptome correlation network analysis of the high grade serous ovarian tumor immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7347.

DNA Methylation of a Group of Long Non-Coding RNA Genes at Different Stages of Ovarian Cancer Dissemination.

There are three types of metastases in ovarian cancer: lymphogenous, hematogenous, and peritoneal. Dissemination of the tumor in the peritoneum is directly related with the development of ascites and a poor prognosis. The purpose of this study is to determine changes in the methylation level of a group of long non-coding RNA (lncRNA) genes at different stages of ovarian cancer progression. The methylation level of 7 lncRNA genes (LINC00472, LINC00886, MAFG-DT, SNHG1, SNHG6, TP53TG1, and TUG1) was studied by quantitative methyl-specific PCR in 93 samples of ovarian tumors and 75 paired samples of histologically normal tissue, as well as in 29 peritoneal macroscopic metastases. Using the nonparametric Mann-Whitney test, a significant (p<0.001) increase in the level of methylation of the LINC00886, SNHG1, SNHG6, and TUG1 genes in the tumor tissue was shown. For the LINC00472, LINC00886, and SNHG6 genes, a significant relationship was found with the clinical stage (p≤0.001), as well as with the appearance of metastases for the LINC00472 (p<0.001) and SNHG6 (p=0.005) genes. There was a significant increase in the level of methylation of MAFG-DT and TP53TG1 (p<0.001) genes, as well as a decrease in LINC00886 (p=0.003) in peritoneal metastases relative to the primary focus. Methylation of the LINC00472 and SNHG6 genes can be considered as a factor in initiating ovarian cancer metastasis, and methylation of the LINC00886, MAFG-DT, and TP53TG1 genes as a colonization factor for metastases in the peritoneum. Thus, a relationship between methylation of a group of lncRNA genes at different stages of ovarian cancer dissemination was shown, which is important for understanding the mechanisms of these processes and for developing innovative approaches to ovarian cancer therapy.

Fibroblast Growth Factor Receptors and Ligands in Context of Bevacizumab Response in Ovarian Carcinoma: An Exploratory Analysis of AGO-OVAR11/ICON-7

With more novel drugs being approved for the treatment of ovarian carcinoma, the question remains to what extent patients benefit from antiangiogenic treatment with bevacizumab, either in combination with poly-(ADP-ribose) polymerase inhibitors or as single-agent maintenance. As fibroblast growth factor receptors and their ligands (FGFRs/FGFs) are key players in angiogenic signaling and have been linked to resistance to several drugs, we investigated the prognostic or predictive potential of FGFs/FGFRs signaling in the context of bevacizumab treatment within the prospective phase III AGO-OVAR11/ICON-7 study. FGFR1, FGFR2, FGFR3, FGFR4, FGF1, and FGF19 gene expressions were determined in 380 ovarian carcinoma tumor samples collected from German centers in the multicenter phase III AGO-OVAR11 trial/ICON-7 trial. All patients received carboplatin and paclitaxel, administered every 3 weeks for 6 cycles, and were randomized to bevacizumab. Expressions of FGFR1, FGFR2, FGF1, and FGF19 were associated with progression-free survival in both uni- and multivariate (FGFR1: HR, 1.6, P < .001; FGFR2: HR, 1.6, P = .002; FGF1: HR, 2.3, P < .001; and FGF19: HR, 0.7; P = .007) analysis. A signature built by FGFR1, FGFR4, and FGF19 defined a subgroup (n = 62) of patients that derived the greatest bevacizumab-associated improvement of progression-free survival (HR, 0.3; P = .004). In this exploratory analysis of a prospective randomized phase III trial, we provide evidence that the expression of FGFRs/FGFs might have independent prognostic values. An FGFR/FGF-based gene signature identified in our study appears to predict long-term benefit from bevacizumab. This observation is hypothesis-generating and requires validation on independent cohorts.

Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer.

The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making. Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC). GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33-0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31-0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74-1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07-0.72). GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.

High BMP7 expression is associated with poor prognosis in ovarian cancer.

Bone Morphogenetic Protein 7 (BMP7) is an extracellular signalling protein that belongs to the transforming growth factor-β (TGF- β) superfamily. Previous transcriptomic data suggested that BMP7 expression may be disrupted in ovarian carcinoma and may play an important role in the aggressiveness of the disease. However, the protein expression in patient tumours has not been well studied. The current study aimed to assess BMP7 protein expression in a large cohort of ovarian carcinoma patient tumour samples to establish its associations with different clinical endpoints. Ovarian carcinoma tissue samples from 575 patients who underwent surgery for different subtypes of ovarian cancer were used. BMP7 protein expression was analysed by immunohistochemistry using tissue microarray and full face tumour sections. High BMP7 expression is associated with aggressive ovarian cancer clinicopathological variables including advanced FIGO stage, high grade, residual disease and poor overall survival. Elevated cytoplasmic and nuclear BMP7 expression was significantly associated with advanced FIGO stage, high tumour grade, presence of residual tumours and high-grade serous carcinomas (p = 0.001, 0.005, 0.004, <0.001 and p < 0.001, <0.001, 0.002, 0.001 respectively). Increased cytoplasmic and nuclear BMP7 expression was also significantly associated with an adverse overall survival (p = 0.001 and 0.046 respectively). The study highlights the potential of BMP7 as a prognostic tool and as a potential novel target for ovarian cancer therapies to limit disease progression.

Cancer Stem Cell Markers-Clinical Relevance and Prognostic Value in High-Grade Serous Ovarian Cancer (HGSOC) Based on The Cancer Genome Atlas Analysis.

Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients' clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10-3; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10-3). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.

Abstract 3017: Breastfeeding and ovarian cancer risk by tumor immune profiles

Abstract Introduction: Breastfeeding is associated with decreased ovarian cancer risk, yet the biological mechanisms are not fully understood. We conducted an agnostic investigation of tumor immune profiles to better understand the role of the tumor microenvironment in the protective association between breastfeeding and ovarian cancer risk. Methods: Multiplex immunofluorescence was used to measure the abundance of T cells and B cells in ovarian tumor samples on tissue microarrays from the Nurses’ Health Study (NHS; n=337), NHSII (n=127), and New England Case Control Study (NECC; n=214). We averaged the percent of cells within the tumor epithelium that were positive for the immune cell of interest across 3 cores per tumor and categorized tumors as having high or low immune cell abundance based on the median value across all tumors. Controls were non-cases and did not provide tissue samples (n=2,045). Self-reported history of breastfeeding was used to define ever/never and total duration of breastfeeding. Polytomous logistic regression models adjusted for age, birth cohort, race, parity, oral contraception use, and study, was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for breastfeeding and ovarian cancer risk by immune cell level. Results: Overall, breastfeeding was associated with a reduced risk of tumors with low, but not high, subtypes of T cell abundance. Among parous women, history of ever breastfeeding decreased the risk of tumors with low helper T cells (CD3+CD4+; ORlow: 0.56, 95% CI: 0.41-0.77), but not high helper T cells (ORhigh: 0.85, 95% CI: 0.63-1.35; p-het=0.06). For total B cells (CD19+), having ever breastfed was associated with decreased risk of both low (ORlow: 0.64, 95% CI: 0.47-0.87) and high abundance in the tumor (ORhigh: 0.70, 95% CI: 0.62-0.96, p-het=0.67) among parous women. Similar results were observed for CD138+ plasma cells (ORlow:0.76, 95% CI: 0.56-0.99; ORhigh: 0.59, 95% CI: 0.37-0.94; p-het=0.34). Longer duration of breastfeeding was associated with decreased risk of developing tumors with low abundance of total B cells (CD19+, p-trend=0.02) and regulatory B cells (CD19+TNFR2+, p-trend=0.05), but not high abundance of CD19+ or CD19+TNFR2+ (p-het=0.01 and 0.03, respectively). Conclusion: These results indicate that among parous women breastfeeding is associated with reduced risk of ovarian tumors with low infiltration of helper T cells while no heterogeneity was observed for risk by total B cells or plasma cells. Longer breastfeeding duration was associated with reduced risk of tumors low in regulatory B cells (CD19+TNFR+) with a dose-dependent response. Together, these results suggest breastfeeding may play a role in the activation of the tumor immune response. Further efforts are ongoing to disentangle the relationships between breastfeeding and parity with the tumor immune microenvironment. Citation Format: Jennifer Mongiovi, Mary Townsend, Allison Vitonis, Ana Babic, Jonathan Hecht, Jose Conejo-Garcia, Brooke Fridley, Shelley Tworoger, Kathryn Terry, Naoko Sasamoto. Breastfeeding and ovarian cancer risk by tumor immune profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3017.

Abstract 1121: Patient-centric Discovery by Active Learning (PeDAL) platform to predict drug response in a heterogeneous patient population

Abstract Even after thorough preclinical testing, only 5% of oncology drug candidates entering clinical trials ultimately get approved for use in patients. To find one drug that will be effective in the clinic, drug developers have to test 20 agents, 19 of which will ultimately prove ineffective. Such high attrition rates drive up the cost of treatment and delay getting effective drugs to the market. Here we describe the Patient-centric Discovery by Active Learning (PeDAL) platform and present results of a study that shows it can predict the effectiveness of a drug or a set of drugs in a patient population with 92% accuracy in only 11 weeks while only performing in vitro assays on a fraction of the drug-patient combinations. The power of PeDAL is derived from a combination of AI technology, an extensive drug response and biomarker database, and a biobank of 150,000 tumor samples from 137 tumor types. The study consists of two phases, the first demonstrates that PeDAL’s predictive model efficiently reveals drug-response patterns that provide insight into the treatment of ovarian cancers. The second phase focuses on the in vitro validation of the accuracy of the predictions. The experimental space was 175 FDA-approved drugs and 130 primary ovarian tumor samples. PeDAL’s active machine learning algorithm iteratively optimizes predictive models by learning from in vitro experiments which take into account tumor-stromal interactions and cellular heterogeneity. Importantly, the ability to model patient-drug combinations across a diverse population of primary samples adds a patient heterogeneity dimension to the generated drug response predictions. After performing 720 high throughput experiments, PeDAL was able to make drug-response predictions for an additional 4,600 drug/tumor predictions. Additional in vitro validation experiments confirm the reproducibility and accuracy of the high-confidence predictions generated by PeDAL with area under the curve (AUC) values &amp;gt;0.95. These results have strong implications not only for the new drug discovery process, but also for identifying opportunities for drug repurposing. We demonstrate that the PeDAL platform is a highly efficient tool to make confident predictions about drug-tumor pairings allowing the discovery of many more drug-tumor responses than are practical to test in vitro. Citation Format: Robert J. Montgomery, Aaron W. Gilkey, Joshua D. Kangas, Robert F. Murphy, Arlette H. Uihlein. Patient-centric Discovery by Active Learning (PeDAL) platform to predict drug response in a heterogeneous patient population [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1121.

Pan-cancer analysis highlights the role of PSENEN in the prognosis and immunology of cancer

BackgroundPresenilin enhancer-2 (PSENEN, PEN-2), one of the four components of the γ-secretase complex, has been increasingly revealed to be significant in cancer types such as pancreatic cancer, gastric cancer, and breast cancer. However, the pan-cancer clinical relevance of PSENEN remains unclear. MethodsRaw data on PSENEN expression in normal and cancer tissues were obtained from The Cancer Genome Atlas (TGCA) database and Genotype-Tissue Expression Project (GTEx). The difference of PSENEN expression was analyzed using data from the TCGA repository and TIMER2 database. Meanwhile, Cox regression analysis and KM plotter were used to analyze the pan-cancer prognostic significance of PSENEN. We also analyzed the correlation between PSENEN expression and tumor immune infiltration using the TIMER and XCELL algorithms. Moreover, we used the TISIDB database to determine PSENEN expression in different immune and molecular subtypes of human cancers. Pan-cancer analysis of genetic alteration of PSENEN was performed using online tools such as cBioPortal and UALCAN. Finally, six Gene Expression Omnibus datasets from the Gene Expression Profiling Interactive Analysis database were used to validate the expression level of PSENEN in lung adenocarcinoma (LUAD). ResultsContrary to nonmalignant tissues, the expression level of PSENEN was significantly upregulated in the cancerous tissues in 22 cancer types. Elevated PSENEN expression was correlated with worse overall survival in 7 cancer types and with worse disease-specific survival in 8 of them. By using xCell algorithm, TIMER algorithm, and Spearman's correlation analysis, we found that PSENEN expression was closely correlated with the infiltration of immune cells across all cancer types. Moreover, aberrant PSENEN expression was associated with 60 immune checkpoint pathway-related genes, microsatellite instability, and tumor mutation burden in various cancer types. Besides, PSENEN expression was significantly associated with different immune subtypes and molecular subtypes of various human cancers. Notably, ovarian epithelial tumor samples demonstrated the highest frequency of PSENEN mutation among all cancer types. ConclusionsOur pan-cancer analysis demonstrated that PSENEN might serve as a prognostic biomarker and revealed the importance of an in-depth understanding of the oncogenic role of PSENEN in various malignancies.

Identification and Validation of a Novel Prognostic Signature Based on Ferroptosis-Related Genes in Ovarian Cancer.

Ovarian cancer is the most lethal gynecological tumor, with a poor prognosis due to the lack of early symptoms, resistance to chemotherapy, and recurrence. Ferroptosis belongs to the regulated cell death family, and is characterized by iron-dependent processes. Here, comprehensive bioinformatics analysis was applied to explore a valuable prognostic model based on ferroptosis-related genes, which was further validated in clinical OC samples. mRNA data of normal and ovarian tumor samples were obtained separately from the GTEx and TCGA databases. The least absolute shrinkage and selection operator (LASSO) cox regression was applied to construct the prognostic model based on ferroptosis-associated genes. Expression of ALOX12 in OC cell lines, as well as cell functions, including proliferation and migration, were examined. Finally, the prognostic efficiency of the model was assessed in the clinical tissues of OC patients. A gene signature consisting of ALOX12, RB1, DNAJB6, STEAP3, and SELENOS was constructed. The signature divided TCGA, ICGC, and GEO cohorts into high-risk and low-risk groups separately. Receiver operating characteristic (ROC) curves and independent prognostic factor analysis were carried out, and the prognostic efficacy was validated. The expression levels of ALOX12 in cell lines were examined. Inhibition of ALOX12 attenuated cell proliferation and migration in HEY cells. Moreover, the prognostic value of ALOX12 expression was examined in clinical samples of OC patients. This work constructed a novel ferroptosis-associated gene model. Furthermore, the clinical predictive role of ALOX12 was identified in OC patients, suggesting that ALOX12 might act as a potential prognostic tool and therapeutic target for OC patients.

Patient-Derived Primary Cancer-Associated Fibroblasts Mediate Resistance to Anti-Angiogenic Drug in Ovarian Cancers.

Ovarian cancers rank first in both aggressiveness and dismal prognosis among gynecological neoplasms. The poor outcome is explained by the fact that most patients present with late-stage disease and progress through the first line of treatment. Ovarian neoplasms, especially epithelial ovarian cancers, are diagnosed at advanced/metastatic stages, often with a high angiogenesis index, one of the hallmarks of ovarian cancers with rapid progression and poor outcome as resistance to anti-angiogenic therapy develops. Despite therapy, the metastatic progression of aggressive ovarian cancer is a spectacularly selective function of tumor cells aided and abetted by the immune, mesenchymal and angiogenic components of the tumor microenvironment (TME) that enforces several pro-metastatic event(s) via direct and indirect interactions with stromal immune cells, cancer-associated fibroblasts (CAFs), and vascular endothelial cells. Since transdifferentiation of tumor endothelium is one of the major sources of CAFs, we hypothesized that ovarian CAF plays a critical role in resisting anti-angiogenic effects via direct crosstalk with endothelium and hence plays a direct role in the development of resistance to anti-angiogenic drugs. To test the hypothesis, we set up a hybrid ex vivo model for co-culture comprising Patient-Derived ex vivo primary CAFs from ovarian tumor samples and human umbilical vein endothelial cells (HUVEC). Patient-Derived CAFs were characterized by the mRNA and protein expression of positive (SMA, S100A4, TE-7, FAP-A, CD90/THY1), negative (EpCAM, CK 8,18, CD31, CD44, CD45), functional (PDGFRA, TGFB1, TGFB2, TGFRA) and immunological markers (PD-L1, PD-L2, PD-1) associated with CAFs by qRT-PCR, flow cytometry, Western blot, and ICC. Data from our HUVEC-on-CAF ex vivo Hybrid Co-Culture (HyCC) study demonstrate the pro-angiogenic effect of Patient-Derived ovarian CAFs by virtue of their ability to resist the effect of anti-angiogenic drugs, thereby aiding the development of resistance to anti-angiogenic drugs. Ascertaining direct experimental proof of the role of CAFs in developing resistance to specific anti-angiogenic drugs will provide an opportunity to investigate new drugs for counteracting CAF resistance and "normalizing/re-educating" TME in aggressive ovarian cancers. Our data provide a unique experimental tool for the personalized testing of anti-angiogenic drugs, positively predicting the development of future resistance to anti-angiogenic drugs well before it is clinically encountered in patients.

Machine learning-based ovarian cancer prediction with XGboost and stochastic gradient boosting models

Ovarian cancer is one of the most common types of gynecological malignancies with its high mortality rate, silent and occult tumor growth, late onset of symptoms and diagnosis in advanced stages. Therefore, the need to develop new diagnostic techniques to predict the course of the disease and the prognosis of this malignancy has increased. In this study, ovarian cancer and benign ovarian tumor samples will be classified to create an accurate diagnostic predictive model using the machine learning method XGBoost and Stochastic Gradient Boosting and disease-related risk factors will be determined. This current study considered the open-access ovarian cancer and benign ovarian tumor samples data set. For this purpose, data from 349 patients were included. The data set was divided as 80:20 as a training and test dataset. XGBoost and Stochastic Gradient Boosting were constructed for the classification via five-fold cross-validation. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, and negative predictive value performance metrics were evaluated for model performance. Among the performance criteria in the test stage obtained from the XGBoost model that has the best classification result; accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were obtained as 89.5%, 88.7%, 85.7%, 91.7%, 85.7%, 91.7%, and 85.7%, respectively. According to the variable importance obtained as a result of the model, the variables most associated with the diagnosis were CA72-4, HE4, LYM%, ALB, EO%, BUN, RBC, NEU, and MCV, respectively. The applied machine learning model successfully classified ovarian cancer and created a highly accurate diagnostic prediction model. The results from the study revealed effective parameters that can diagnose ovarian cancer with high accuracy. With the parameters determined as a result of the modeling, the clinician will be able to simplify and facilitate the decision-making process for the diagnosis of ovarian cancer.

Integrated multi-omic analysis of low-grade ovarian serous carcinoma collected from short and long-term survivors

BackgroundLow-grade serous ovarian cancer (LGSOC) is a rare disease that occurs more frequently in younger women than those with high-grade disease. The current treatment is suboptimal and a better understanding of the molecular pathogenesis of this disease is required. In this study, we compared the proteogenomic analyses of LGSOCs from short- and long-term survivors (defined as < 40 and > 60 months, respectively). Our goal was to identify novel mutations, proteins, and mRNA transcripts that are dysregulated in LGSOC, particularly in short-term survivors.MethodsInitially, targeted sequencing of 409 cancer-related genes was performed on 22 LGSOC and 6 serous borderline ovarian tumor samples. Subsequently, whole-genome sequencing analysis was performed on 14 LGSOC samples (7 long-term survivors and 7 short-term survivors) with matched normal tissue samples. RNA sequencing (RNA-seq), quantitative proteomics, and phosphoproteomic analyses were also performed.ResultsWe identified single-nucleotide variants (SNVs) (range: 5688–14,833 per sample), insertion and deletion variants (indels) (range: 880–1065), and regions with copy number variants (CNVs) (range: 62–335) among the 14 LGSOC samples. Among all SNVs and indels, 2637 mutation sites were found in the exonic regions. The allele frequencies of the detected variants were low (median12%). The identified recurrent nonsynonymous missense mutations included KRAS, NRAS, EIF1AX, UBR5, and DNM3 mutations. Mutations in DNM3 and UBR5 have not previously been reported in LGSOC. For the two samples, somatic DNM3 nonsynonymous missense mutations in the exonic region were validated using Sanger sequencing. The third sample contained two missense mutations in the intronic region of DNM3, leading to a frameshift mutation detected in RNA transcripts in the RNA-seq data. Among the 14 LGSOC samples, 7754 proteins and 9733 phosphosites were detected by global proteomic analysis. Some of these proteins and signaling pathways, such as BST1, TBXAS1, MPEG1, HBA1, and phosphorylated ASAP1, are potential therapeutic targets.ConclusionsThis is the first study to use whole-genome sequencing to detect somatic mutations in LGSOCs with matched normal tissues. We detected and validated novel mutations in DNM3, which were present in 3 of the 14 samples analyzed. Additionally, we identified novel indels, regions with CNVs, dysregulated mRNA, dysregulated proteins, and phosphosites that are more prevalent in short-term survivors. This integrated proteogenomic analysis can guide research into the pathogenesis and treatment of LGSOC.