Prognosis In High-grade Serous Ovarian Cancer Research Articles

Abstract 165: PVRL2 upregulation by tumor-associated macrophages confers a regulatory phenotype and is associated with poor prognosis in high-grade serous ovarian cancer

Abstract Advanced or recurrent high-grade serous ovarian cancer (HGSOC) are associated with poor prognosis. While HGSOC is moderately immunogenic, anti-PD-1/L1 therapy has fared poorly in trials. PVRL2 (Nectin2) is both a cell-adhesion molecule as well as a ligand for PVRIG, a novel co-inhibitory receptor. While PVRL2 is constitutively expressed by epithelial/tumor cells, it can also be upregulated by macrophages. Whether macrophage-expressed PVRL2 functions exclusively as an immunoregulatory ligand in HGSOC is not known. CD45+ tumor infiltrate from 6 treatment-naïve and 3 neoadjuvant chemotherapy-treated patients with HGSOC was evaluated by CITE-seq. Multiplex immunofluorescence (mIF) of PanCK, CD68 and PVRL2 was performed on 28 FFPE sections. In vivo, we monitored survival of wild-type and PVRL2−/− mice as well as corresponding bone marrow chimeras orthotopically implanted with ID8-Vegf-Defb29 ovarian tumors. CITE-seq was run on CD45+ cells sorted from wild-type vs PVRL2−/− tumors and ascites. In tumor-bearing mice, macrophages, CD8 or NK cells were depleted and differences in survival assessed. In vitro, M1 and M2 macrophages were differentiated from steady-state bone marrow progenitors and myeloid gene profiles were analyzed. CITE-seq of patient-derived immune cells revealed that PVRL2 expression was restricted to macrophage clusters. Macrophages with low PVRL2 expression showed M1 polarity and abundant expression of inflammatory cytokines and chemokines. Overall macrophage counts and PVRL2 expression were lower in post-chemotherapy cases. In mIF, PVRL2 staining was observed on CD68-positive cells as well as tumor cells. In addition, PVRL2-expressing macrophages accumulated in tumor areas, especially in residual tumor areas after chemotherapy. PVRL2−/− mice and PVRL2 bone marrow chimeras had significantly improved survival compared to their respective control cohorts. Macrophage depletion significantly shortened the prognosis of PVRL2−/− mice compared to wild-type mice. In contrast, depletion of CD8 T cells or NK cells had no effect on survival of wild-type and PVRL2−/− mice. M1 macrophages from PVRL2−/− mice expressed higher levels of inflammatory cytokines and chemokines compared to their wild-type counterparts. Furthermore, CITE-seq showed that tumor-infiltrating macrophages in PVRL2−/− mice exhibited M1 polarization compared to those from wild-type tumors. However, the inflammatory phenotype appeared to be linked more with the tumor-infiltrating macrophages than with ascites macrophages. In conclusion, our research suggests that PVRL2 upregulation by tumor-infiltrating macrophages polarizes towards a pro-tumor phenotype and affects prognosis of HGSOC. PVRL2-deficient macrophages exert their antitumor effects via inflammatory tumor killing and not via antigen-presentation to effector T cells. Citation Format: Kosuke Murakami, Hirofumi Ando, Michele Doucet, Dacheng Ding, William Huang, Sudipto Ganguly. PVRL2 upregulation by tumor-associated macrophages confers a regulatory phenotype and is associated with poor prognosis in high-grade serous ovarian cancer [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 165.

Abstract B073: A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer

Abstract Background & objective: High grade serous ovarian cancer (HGSC) is the most lethal type of ovarian cancer malignancies. Around 50% of HGSCs are DNA-repair homologous recombination deficient (HRD) and 15% have CCNE1 gene amplification. Previous results have shown that tumor microenvironment (TME) can impact clinical prognosis in HGSC. In this study our objective was to characterize the interplay of the TME with HRD and CCNE1 amplification in HGSC and its association with clinical outcomes. Materials and methods: We collected TME spatial protein expression, genomics, and clinical data from 250 HGSC patients. We determined BRCA1/2 germline and somatic mutation status using targeted sequencing and promoter hyper-methylation. For the tumors without BRCA1/2 mutations, we performed shallow whole genome sequencing, and estimated the CCNE1 amplification and HRD status using shallowHRD and BRCAness copy number profiles. Immune cell infiltration was assessed also using IHC for CD8, CD20, CD68, and CD103. The single-cell spatial landscapes were analyzed for 1000 tumor cores from the tumor center and tumor periphery using cyclic immunofluorescence (tCycIF) imaging for 34 different protein markers. We categorized and spatially analyzed the single cells from the TME to distinct cellular subpopulations using the TRIBUS and scimap software. Results: In our study of the TME using highly multiplexed tissue imaging we identified a total of 4.8 million single cells of them 1.6 million were immune cells belonging to eight biological subpopulations. The immune cell proportion detected in each sample showed high concordance with the IHC estimation by a pathologist. Interestingly, the CD8+, CD20+, CD11c+, and CD15+ high immune cell infiltrations were higher in BRCA1/2 mutated as compared to the CCNE1 amplified tumors. High immune infiltration was associated with prolonged overall survival only in samples debulked before chemotherapy but not in samples debulked after chemotherapy. Using consensus clustering for cell protein expression and cell morphology, we identified five distinct tumoral and stromal cell clusters which showed significant heterogeneity among the tumor genotypes and association with clinical outcomes. Then, after clustering of the spatial single-cell proximity data, we identified 13 distinct recurrent cellular neighborhoods (RCNs). The two RCNs related to the tumor-stroma and tumor-immune interfaces exhibited the highest differences between HRD and CCNE1 samples. Finally, by using random forest machine learning classifiers of the cells and its neighborhoods, and using an approach to minimize overfitting, we were able to detect the features more representative in HRD samples, for example, MHC-I expression in cancer cells and in stromal cells. Conclusion: The integration of multi-omics data revealed distinct TMEs associated with HRD and clinical outcomes; this has the potential to be used to discover biomarkers for precision oncology in HGSCs. Citation Format: Fernando Perez-Villatoro, Lilian van Wagensveld, Aleksandra Shabanova, Siamak Hajizadeh, Julia Casado, Ella Anttila, Maaike A. van der Aa, Roy F.P.M. Kruitwagen, Gabe S. Sonke, Koen K. Van de Vijver, Peter K. Sorger, Hugo M. Horlings, Anniina Färkkilä. A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B073.

C5aR1 blockade reshapes immunosuppressive tumor microenvironment and synergizes with immune checkpoint blockade therapy in high-grade serous ovarian cancer

ABSTRACT High-grade serous ovarian cancer (HGSC), with a modest response to immune checkpoint blockade (ICB) targeting PD-1/PD-L1 monotherapy, is densely infiltrated by M2-polarized tumor-associated macrophages (TAMs) and regulatory T (Treg) cells. The complement C5a/C5aR1 axis contributes to the programming of the immunosuppressive phenotype of TAMs in solid tumors and represents a promising immunomodulatory target for treating HGSCs. Here, we aimed to identify the relevance of C5aR1 in prognosis, immune microenvironment, and immunotherapy response in HGSCs. The expression and relationship of C5aR1 with tumor-infiltrating immune cells were assessed by immunohistochemistry and flow cytometry in the training cohort (n = 120) and fresh HGSC tissues (n = 36). Transcriptomic analyses of the xenografts delineated the mechanisms driving the immunomodulatory activity of PMX53, an orally bioavailable C5aR1 inhibitor. Therapeutic relevance was confirmed in ex vivo tumor cultures and The Cancer Genome Atlas (TCGA) datasets. C5aR1 expression independently predicted dismal prognosis and was linked to the immunoevasive subtype of HGSC, characterized by increased infiltration of pro-tumor cells (Treg cells, M2-polarized macrophages, and neutrophils) and impaired CD8+T functions. PMX53 antagonized subcutaneous tumor growth, modulated immunosuppressive mechanisms and synergized with aPD-1 in several tumor types. Single-cell RNA-seq analysis revealed predominant C5aR1 expression in TAMs, with an immunosuppressive-related expression signature in C5aR1+TAMs. Furthermore, the combination of C5aR1 and PD-L1 was associated with specific molecular characteristics and matched clinical response annotations. Therefore, the abundance of C5aR1 could predict an inferior prognosis in HGSCs, and incorporating PD-L1 may serve as a novel predictive biomarker to guide therapeutic options.

Integrative evaluation and experimental validation of the immune-modulating potential of dysregulated extracellular matrix genes in high-grade serous ovarian cancer prognosis

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a challenging malignancy characterized by complex interactions between tumor cells and the surrounding microenvironment. Understanding the immune landscape of HGSOC, particularly the role of the extracellular matrix (ECM), is crucial for improving prognosis and guiding therapeutic interventions.Methods and resultsUsing univariate Cox regression analysis, we identified 71 ECM genes associated with prognosis in seven HGSOC populations. The ECMscore signature, consisting of 14 genes, was validated using Cox proportional hazards regression with a lasso penalty. Cox regression analyses demonstrated that ECMscore is an excellent indicator for prognostic classification in prevalent malignancies, including HGSOC. Moreover, patients with higher ECMscores exhibited more active stromal and carcinogenic activation pathways, including apical surface signaling, Notch signaling, apical junctions, Wnt signaling, epithelial-mesenchymal transition, TGF-beta signaling, and angiogenesis. In contrast, patients with relatively low ECMscores showed more active immune-related pathways, such as interferon alpha response, interferon-gamma response, and inflammatory response. The relationship between the ECMscore and genomic anomalies was further examined. Additionally, the correlation between ECMscore and immune microenvironment components and signals in HGSOC was examined in greater detail. Moreover, the expression of MGP, COL8A2, and PAPPA and its correlation with FAP were validated using qRT-PCR on samples from HGSOC. The utility of ECMscore in predicting the prospective clinical success of immunotherapy and its potential in guiding the selection of chemotherapeutic agents were also explored. Similar results were obtained from pan-cancer research.ConclusionThe comprehensive evaluation of the ECM may help identify immune activation and assist patients in HGSOC and even pan-cancer in receiving proper therapy.

A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer

We developed and validated a multimodal radiomic machine learning approach to noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase (LCK) expression and clinical prognosis of patients with high-grade serous ovarian cancer (HGSOC). We analyzed gene enrichment using 343 HGSOC cases extracted from The Cancer Genome Atlas. The corresponding biomedical computed tomography images accessed from The Cancer Imaging Archive were used to construct the radiomic signature (Radscore). A radiomic nomogram was built by combining the Radscore and clinical and genetic information based on multimodal analysis. We compared the model performances and clinical practicability via area under the curve (AUC), Kaplan–Meier survival, and decision curve analyses. LCK mRNA expression was associated with the prognosis of HGSOC patients, serving as a significant prognostic marker of the immune response and immune cells infiltration. Six radiomic characteristics were chosen to predict the expression of LCK and overall survival (OS) in HGSOC patients. The logistic regression (LR) radiomic model exhibited slightly better predictive abilities than the support vector machine model, as assessed by comparing combined results. The performance of the LR radiomic model for predicting the level of LCK expression with five-fold cross-validation achieved AUCs of 0.879 and 0.834, respectively, in the training and validation sets. Decision curve analysis at 60 months demonstrated the high clinical utility of our model within thresholds of 0.25 and 0.7. The radiomic nomograms were robust and displayed effective calibration. Abnormally high expression of LCK in HGSOC patients is significantly correlated with the tumor immune microenvironment and can be used as an essential indicator for predicting the prognosis of HGSOC. The multimodal radiomic machine learning approach can capture the heterogeneity of HGSOC, noninvasively predict the expression of LCK, and replace LCK for predictive analysis, providing a new idea for predicting the clinical prognosis of HGSOC and formulating a personalized treatment plan.

High EVI1 and PARP1 expression as favourable prognostic markers in high-grade serous ovarian carcinoma

BackgroundMechanisms of development and progression of high-grade serous ovarian cancer (HGSOC) are poorly understood. EVI1 and PARP1, part of TGF-ß pathway, are upregulated in cancers with DNA repair deficiencies with DNA repair deficiencies and may influce disease progression and survival. Therefore we questioned the prognostic significance of protein expression of EVI1 alone and in combination with PARP1 and analyzed them in a cohort of patients with HGSOC.MethodsFor 562 HGSOC patients, we evaluated EVI1 and PARP1 expression by immunohistochemical staining on tissue microarrays with QuPath digital semi-automatic positive cell detection.ResultsHigh EVI1 expressing (> 30% positive tumor cells) HGSOC were associated with improved progression-free survival (PFS) (HR = 0.66, 95% CI: 0.504–0.852, p = 0.002) and overall survival (OS) (HR = 0.45, 95% CI: 0.352–0.563, p < 0.001), including multivariate analysis. Most interestingly, mutual high expression of both proteins identifies a group with particularly good prognosis. Our findings were proven technically and clinically using bioinformatical data sets for single-cell sequencing, copy number variation and gene as well as protein expression.ConclusionsEVI1 and PARP1 are robust prognostic biomarkers for favorable prognosis in HGSOC and imply further research with respect to their reciprocity.

Clinical features of long-term survivors with advanced high-grade serous ovarian cancer.

e17575 Background: Most patients diagnosed with high-grade serous ovarian cancer (HGSOC) at advanced stages (FIGO III&amp;IV) have limited survival. However, approximately 30% of patients survive more than 5 years, some of them without relapse. A better understanding of these patients may uncover novel factors contributing to their long survival beyond those known to be related to HGSOC prognosis (age, stage and residual disease after surgery). This descriptive study aims to analyze the clinical characteristics of these exceptional survivors and describe them in contrast to patients with worse survival in a cohort of patients diagnosed before the introduction of PARP inhibitors as first-line maintenance therapy. Methods: A descriptive analysis of patients newly diagnosed with advanced HGSOC at our University Hospital between 2009 and 2018 was conducted. We report clinical characteristics of long-term survivors (LTS), defined as patients alive after 5 years(y) from the date of diagnosis and characteristics of patients who survived less than two years from the date of diagnosis, classified as short- term survivors (STS). Results: Among 195 patients with advanced HGSOC 42 (21,5%) survived less than 2y and 48 (24,6%) more than 5y, 18 of them (37,5%) without relapse. The Median age was 61.1y for LTS and 64.8 for STS. In the LTS group, 77.1% of patients were stage III, and 22.9% were stage IV. In the STS group, the distribution of patients with stage III and IV was 42.9% and 57.1%, respectively. A higher proportion of LTS underwent surgery n=44 (91,7%), most of them as interval debulking surgery (59.1%). Almost half of STS did not undergo surgery (45,2%), being interval debulking surgery the most frequent systematic approach when surgery was performed (73.9%). Only 9,1% of LTS who underwent surgery had residual disease compared to 27,3% of STS. Harboring mutations in the BRCA1/2 genes is a favorable prognostic factor also associated with chemotherapy sensitivity, in our serires 23% of LTS were BRCA1&amp;2 mutation carriers (11 patients (23%) patients with germline BRCA mutations being 7patients(15%) BRCA1mut and 4patients(8%) BRCA2mut with 4patients(8%) of patients not analyzed). Conclusions: In our cohort, stage III and cytoreduction surgery were more frequent among LTS. Classical clinical characteristics are not enough to identify advanced HGSOC LTS. More investigation on their molecular profile might help to to identify exceptional LTS in order to elucidate better management approaches and new treatments for patients with worse prognosis.

Plasma cfDNA methylation markers for the detection and prognosis of ovarian cancer.

SummaryBackgroundPlasma cell-free DNA (cfDNA) methylation has shown the potential in the detection and prognostic testing in multiple cancers. Herein, we thoroughly investigate the performance of cfDNA methylation in the detection and prognosis of ovarian cancer (OC).MethodsThe OC-specific differentially methylated regions (DMRs) were identified by sequencing ovarian tissue samples from OC (n = 61), benign ovarian disease (BOD, n = 49) and healthy controls (HC, n = 37). Based on 1,272 DMRs, a cfDNA OC detection model (OC-D model) was trained and validated in plasma samples from patients of OC (n = 104), BOD (n = 56) and HC (n = 56) and a prognostic testing model (OC-P model) was developed in plasma samples in patients with high-grade serous OC (HG-SOC) in the training cohort and then tested the rationality of this model with International Cancer Genome Consortium (ICGC) tissue methylation data. Mechanisms were investigated in the TCGA-OC cohort.FindingsIn the validation cohort, the cfDNA OC-D model consisting of 18 DMRs achieved a sensitivity of 94.7% (95% CI: 85.4%‒98.9%) at a specificity of 88.7% (95% CI: 78.7%‒94.9%), which outperformed CA 125 (AUC: 0.967 vs 0.905, P = 0.03). Then the cfDNA OC-P model consisting of 15 DMRs was constructed and associated with a better prognosis of HG-SOC in multivariable Cox regression analysis (HR: 0.29, 95% CI, 0.11‒0.78, P = 0.01) in the training cohort, which was also observed in the ICGC cohort using tissue methylation (HR: 0.56, 95% CI, 0.32‒0.98, P = 0.04). Investigation into mechanisms revealed that the low-risk group had higher homologous recombination deficiency and immune cell infiltration (P < 0.05).InterpretationOur study demonstrated the potential utility of cfDNA methylation in the detection and prognostic testing in OC. Future studies with a larger population are warranted.FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry.

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor β signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

Abstract CT113: A phase II study of prexasertib, a cell cycle checkpoint kinase 1 (CHK1) inhibitor, in platinum-resistant recurrent high-grade serous ovarian cancer (HGSOC) with BRCA wild-type (BRCAwt)

Abstract Background: Platinum-resistance is associated with a poor prognosis in HGSOC and has limited treatment options. Inhibition of CHK1 has shown activity in BRCAwt HGSOC. Here, we report the clinical activity and tolerability of the second generation CHK1 inhibitor, prexasertib, in platinum-resistant BRCAwt HGSOC (NCT02203513). Methods: Eligibility included platinum-resistant recurrent HGSOC women without germline or somatic BRCA mutation, ECOG PS 0-2, good end organ function, and measurable disease. The primary endpoint was response rate (RR). Secondary endpoints were progression-free survival (PFS) and safety. Prexasertib was given at 105 mg/m2 IV every 14 days in a 28-day cycle. CT scans were performed every 2 cycles for RECIST v1.1 evaluation, and safety evaluation using CTCAE v4.0 every cycle. CBC was performed on day 8 of cycle 1 for ANC nadir. Pretreatment clinical samples were collected for biomarker analysis including HR deficiency and circulating tumor cells (CTC). Results: Between January 2017 and November 2020, 49 patients (median age 64.1 years [IQR 56.8 - 69.9]) received at least one dose. 12 (24.5%) patients were primary platinum-resistant, and 37 (75.5%) were secondary platinum-resistant. All were heavily pretreated (median 4 prior systemic therapies [IQR 3 - 7]), including bevacizumab (79%), PARP inhibitors (44%), and both (2%). 39 patients were evaluable by RECIST and RR was 30.7%. The median PFS was 5.8 months (range 1.7-26.4 months). The median duration of response among PRs was 5.5 months (range 1-19.8 months). The clinical benefit rate (PR+CR+SD &amp;gt;4 months) was 84.6%. 10 patients were inevaluable due to: withdrawal of consent (n=2), intercurrent illness related to disease (tumor invasion of mainstem bronchus (n=1) or vaginal wall (n=1), small bowel obstruction (n=2), infection (n=2), failure to thrive (n=1), and pneumothorax secondary to thoracentesis (n=1) during cycle 1. The common (&amp;gt;10%) grade 3 or 4 adverse events (AEs) included neutropenia (85%, 42/49) based on the cycle 1 day 8 CBC at the time of ANC nadir, lymphocytopenia (46%, 23/49), thrombocytopenia (40%, 20/49), anemia (30%, 15/49), and febrile neutropenia (12%, 5/49). 83% (41/49) of patients received growth factors support to avoid subsequent treatment delays. There were no deaths on the study. No significant difference of RECIST response or PFS was observed in patients with high (≥2) vs low (&amp;lt;2) CTC at baseline. Conclusions: Prexasertib monotherapy resulted in clinical benefit in subgroups of heavily pretreated BRCAwt platinum-resistant HGSOC. Prexasertib was well tolerated with manageable grade 3/4 AEs. Further studies on predictive biomarkers are ongoing. Citation Format: Grant Zurcher, Ann McCoy, Brooke Solarz, Jay Nair, Min-Jung Lee, Jane B. Trepel, Christina M. Annunziata, Jung-Min Lee. A phase II study of prexasertib, a cell cycle checkpoint kinase 1 (CHK1) inhibitor, in platinum-resistant recurrent high-grade serous ovarian cancer (HGSOC) with BRCA wild-type (BRCAwt) [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 CT113.

Global characterization of extrachromosomal circular DNAs in advanced high grade serous ovarian cancer

High grade serous ovarian cancer (HGSOC) is the most aggressive subtype of ovarian cancer and HGSOC patients often appear with metastasis, leading to the poor prognosis. Up to date, the extrachromosomal circular DNAs (eccDNAs) have been shown to be involved in cancer genome remodeling but the roles of eccDNAs in metastatic HGSOC are still not clear. Here we explored eccDNA profiles in HGSOC by Circle-Sequencing analysis using four pairs of primary and metastatic tissues of HGSOC patients. Within the differentially expressed eccDNAs screened out by our analysis, eight candidates were validated by outward PCR and qRT-PCR analysis. Among them, DNMT1circle10302690-10302961 was further confirmed by FISH assay and BaseScope assay, as the most significantly down-regulated eccDNA in metastatic tumors of HGSOC. Lower expression of DNMT1circle10302690-10302961 in both primary and metastatic tumors was associated with worse prognosis of HGSOC. Taken together, our finding firstly demonstrated the eccDNAs landscape of primary and metastatic tissues of HGSOC. The eccDNA DNMT1circle10302690-10302961 can be considered as a potential biomarker or a therapeutically clinical target of HGSOC metastasis and prognosis.

DNA Repair Protein HELQ and XAB2 as Chemoresponse and Prognosis Biomarkers in Ascites Tumor Cells of High-Grade Serous Ovarian Cancer.

Nucleotide excision repair (NER) is an important mediator for responsiveness of platinum-based chemotherapy. Our study is aimed at investigating the NER-related genes expression in ascites tumor cells and its application in the prediction of chemoresponse in high-grade serous ovarian cancer (HGSC) patients. The relationship between 16 NER-related genes and the prognosis of ovarian cancer was analyzed in the TCGA database. NER-related genes including HELQ and XAB2 expressions were determined via immunocytochemistry in ascites cell samples from 92 ovarian cancer patients prior to primary cytoreduction surgery. Kaplan-Meier analysis and Cox model were used to investigate the association between NER-related gene expression and prognosis/chemotherapeutic response. Predicting models were constructed using a training cohort of 60 patients and validated in a validation cohort of 32 patients. We found that high expression of HELQ and XAB2 in the training cohort was associated with poor prognosis (for HELQ, P = 0.001, HR = 2.83, 95% CI: 1.46-5.49; for XAB2, P = 0.008, HR = 2.38, 95% CI: 1.23-4.63) and platinum resistance (for HELQ, P < 0.001; for XAB2, P = 0.006). In the validation cohort, the combination of HELQ and XAB2 (AUC = 0.863) showed the highest AUC. The expression levels of HELQ (RR 5.7, 95% CI 1.7-19.2) and XAB2 (RR 3.2, 95% CI 0.9-10.8) in ascites tumor cells were positively correlated to the risk of platinum resistance. In summary, we revealed that the expression levels of HELQ and XAB2 are candidate predictors for primary chemotherapy responsiveness and prognosis in HGSC. Ascites cytology is applicable as a promising method for chemosensitivity prediction in HGSC.

AKT Isoforms Interplay in High-Grade Serous Ovarian Cancer Prognosis and Characterization.

Simple SummaryNew therapeutical strategies are needed to improve survival in high-grade serous ovarian cancer (HGSOC) patients. AKT inhibitors are promising agents able to act in synergy with PARP inhibitors and platinum-based therapies, but the subset of patients who could benefit from this approach is still unclear. We analyzed AKT isoforms expression in a retrospective cohort and we identified four AKT expression groups related to patients’ survival, tumor morphology and the BRCA status that could help in stratifying patients for future clinical trials.High-grade serous ovarian cancer (HGSOC) is among the deadliest gynecological malignancies. The acquired resistance to platinum-based therapies and the intrinsic heterogeneity of the disease contribute to the low survival rate. To improve patients’ outcomes, new combinatorial approaches able to target different tumor vulnerabilities and enhance the efficacy of the current therapies are required. AKT inhibitors are promising antineoplastic agents able to act in synergy with PARP inhibitors, but the spectrum of patients who can benefit from this combination is unclear, since the role of the three different isoforms of AKT is still unknown. Here, we study the expression of AKT isoforms on a retrospective cohort of archive tissue by RT-droplet digital PCR (ddPCR) analyzing their association with the clinicopathological features of patients. Based on AKT1/AKT2 and AKT1/AKT3 ratios, we define four AKT classes which were related to patients’ survival, tumor morphology and BRCA1 expression. Moreover, our results show that high AKT3 expression levels were frequently associated with tumors having classic features, a low number of mitoses and the presence of psammoma bodies. Overall, our study obtains new insights on AKT isoforms and their associations with the clinicopathological features of HGSOC patients. These evidences could help to better define the subsets of patients who can benefit from AKT and PARP inhibitors therapy in future clinical trials.

Integrated weighted gene co-expression network analysis reveals biomarkers associated with prognosis of high-grade serous ovarian cancer.

BackgroundOvarian cancer is the gynecologic tumor with the highest fatality rate, and high‐grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for the improvement of treatment strategies and to ensure appropriate healthcare decisions.MethodsTo construct the co‐expression network of HGSOC samples, we applied weighted gene co‐expression network analysis (WGCNA) to assess the proteomic data obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and module‐trait relationship was then analyzed and plotted in a heatmap to choose key module associated with HGSOC. Subsequently, hub genes with high connectivity in key module were identified by Cytoscape software. Furthermore, the biomarkers were selected through survival analysis, followed by evaluation using the relative operating characteristic (ROC) analysis.ResultsA total of 9 modules were identified by WGCNA, and module‐trait analysis revealed that the brown module was significantly associated with HGSOC (cor = 0.7). Ten hub genes with the highest connectivity were selected by protein‐protein interaction analysis. After survival and ROC analysis, ALB, APOB and SERPINA1 were suggested to be the biomarkers, and their protein levels were positively correlated with HGSOC prognosis.ConclusionWe conducted the first gene co‐expression analysis using proteomic data from HGSOC samples, and found that ALB, APOB and SERPINA1 had prognostic value, which might be applied for the treatment of HGSOC in the future.

DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Role of tumor location on high-grade serous ovarian cancer prognosis.

Ovarian cancer is associated with the highest mortality of gynecologic cancers. Epidemiological and genetic factors of ovarian cancer development are clearly defined but prognostic factors have not been adequately identified. Right and left ovarian cancers seem to act different behaviors at high-grade serous ovarian cancer (HGSOC) prognosis. The aim of this study is to explain this prognostic role of its sidedness. The aim of this study is to explain this prognostic role of its sidedness. We reviewed 160 consecutive patients with Figo stage 1-3 HGSOCs and undergone surgery at two high-volume hospitals. Prognostic effects of primary tumor location onset were evaluated in terms of 5-year disease free survival and overall survival rate. One hundred-sixty patients with ovarian cancer records were analyzed using the Kaplan-Meier method, that demonstrated a significant difference in the 5-year disease-free survival rates between right and left-sided cancers for all stages (44.6% vs 78.5%, p < 0.001). Also, there was significant difference in the 5-year overall survival rates between the two groups (71.1% vs 91.9%, p = 0.020). Tumor location within the HGSOC seems to be a compelling prognostic factor ovarian cancer. Further prospective studies are needed in order to support our hypothesis.

Possible Role of Metformin as an Immune Modulator in the Tumor Microenvironment of Ovarian Cancer.

Growing evidence suggests that the immune component of the tumor microenvironment (TME) may be highly involved in the progression of high-grade serous ovarian cancer (HGSOC), as an immunosuppressive TME is associated with worse patient outcomes. Due to the poor prognosis of HGSOC, new therapeutic strategies targeting the TME may provide a potential path forward for preventing disease progression to improve patient survival. One such postulated approach is the repurposing of the type 2 diabetes medication, metformin, which has shown promise in reducing HGSOC tumor progression in retrospective epidemiological analyses and through numerous preclinical studies. Despite its potential utility in treating HGSOC, and that the immune TME is considered as a key factor in the disease’s progression, little data has definitively shown the ability of metformin to target this component of the TME. In this brief review, we provide a summary of the current understanding of the effects of metformin on leukocyte function in ovarian cancer and, coupled with data from other related disease states, posit the potential mechanisms by which the drug may enhance the anti-tumorigenic effects of immune cells to improve HGSOC patient survival.

Expression and clinical significance of methyl-CpG binding domain protein 2 in high-grade serous ovarian cancer.

Platinum resistance is an important cause of clinical recurrence and mortality of patients with high-grade serous ovarian cancer (HGSOC). Methyl-CpG binding domain protein 2 (MBD2) serves an important role in tumor progression; however, its role in HGSOC remains unclear. The aim of the present study was to investigate the expression of MBD2 in HGSOC and its role in drug resistance and prognosis of HGSOC. MBD2 expression was analyzed by immunohistochemical staining and western blotting. The associations between MBD2 expression and clinical pathological features, platinum resistance and patient prognosis were analyzed using a χ2 test, Kaplan-Meier analysis and Cox regression analysis. Positive MBD2 expression was detected in 73 (63.5%) of the HGSOC tissue samples, whereas it was undetectable in all 16 normal tissue samples (100%) analyzed, indicating a significantly higher expression level in tumor tissues compared with normal tissues (P<0.001). Additionally, MBD2 expression was significantly higher in platinum-resistant cases compared with that in platinum-sensitive cases (P<0.05). In addition, high expression of MBD2 was negatively associated with relapse-free survival (P<0.05). In conclusion, MBD2 was demonstrated to be a potential drug target and a biomarker for poor prognosis in HGSOC.

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis.

BackgroundHigh-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method.Material/MethodsFive gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizing the WGCNA package was established to define several hub genes from the key module. Furthermore, survival analysis was performed, followed by expression validation with datasets from TCGA and GTEx. Finally, we used single-gene GSEA to detect the function of prognostic hub genes.ResultsOut of the 1874 DEGs detected from 114 HGSOC versus 49 normal tissue samples, 956 were upregulated and 919 were downregulated. The functional annotation indicated that upregulated DEGs were mostly enriched in cell cycle, whereas the downregulated DEGs were enriched in the MAPK or Ras signaling pathway. Two modules significantly associated with HGSOC were excavated through WGCNA. After survival analysis and expression validation of hub genes, we found that 2 upregulated genes (MAD2L1 and PKD2) and 3 downregulated genes (DOCK5, FANCD2 and TBRG1) were positively correlated with HGSOC prognosis. GSEA for single-hub genes revealed that MAD2L1 and PKD2 were associated with proliferation, while DOCK5, FANCD2, and TBRG1 were associated with immune response.ConclusionsWe found that FANCD2, PKD2, TBRG1, and DOCK5 had prognostic value and could be used as potential biomarkers for HGSOC treatment.

Kallistatin inhibits tumour progression and platinum resistance in high-grade serous ovarian cancer

Ovarian cancer is the most lethal gynaecologic malignancy. Although there are various subtypes of ovarian cancer, high-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian cancer deaths. Chemoresistance is the primary reason for the unfavourable prognosis of HGSOC. Kallistatin (KAL), also known as SERPINA4, is part of the serpin family. Kallistatin has been discovered to exert multiple effects on angiogenesis, inflammation and tumour progression. However, the roles and clinical significance of kallistatin in HGSOC remain unclear. Here, we showed that kallistatin was significantly downregulated in HGSOC compared to normal fallopian tube (FT) tissues. Low expression of kallistatin was associated with unfavourable prognosis and platinum resistance in HGSOC. Overexpression of kallistatin significantly inhibited proliferation and metastasis, and enhanced platinum sensitivity and apoptosis in ovarian cancer cells. Collectively, these findings demonstrate that kallistatin serves as a prognostic predictor and provide a potential therapeutic target for HGSOC.