Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Immunotherapy for ovarian cancer: towards a tailored immunophenotype-based approach

Despite documented evidence that ovarian cancer cells express immune-checkpoint molecules, such as PD-1 and PD-L1, and of a positive correlation between the presence of tumour-infiltrating lymphocytes and favourable overall survival outcomes in patients with this tumour type, the results of trials testing immune-checkpoint inhibitors (ICIs) in these patients thus far have been disappointing. The lack of response to ICIs can be attributed to tumour heterogeneity as well as inherent or acquired resistance associated with the tumour microenvironment (TME). Understanding tumour immunobiology, discovering biomarkers for patient selection and establishing optimal treatment combinations remains the hope but also a key challenge for the future application of immunotherapy in ovarian cancer. In this Review, we summarize results from trials testing ICIs in patients with ovarian cancer. We propose the implementation of a systematic CD8+ T cell-based immunophenotypic classification of this malignancy, followed by discussions of the preclinical data providing the basis to treat such immunophenotypes with combination immunotherapies. We posit that the integration of an accurate TME immunophenotype characterization with genetic data can enable the design of tailored therapeutic approaches and improve patient recruitment in clinical trials. Lastly, we propose a roadmap incorporating tissue-based profiling to guide future trials testing adoptive cell therapy approaches and assess novel immunotherapy combinations while promoting collaborative research.

T cell exhaustion and senescence for ovarian cancer immunotherapy

Ovarian cancer is a common gynecological malignancy, and its treatment remains challenging. Although ovarian cancer may respond to immunotherapy because of endogenous immunity at the molecular or T cell level, immunotherapy has so far not had the desired effect. The functional status of preexisting T cells is an indispensable determinant of powerful antitumor immunity and immunotherapy. T cell exhaustion and senescence are two crucial states of T cell dysfunction, which share some overlapping phenotypic and functional features, but each status possesses unique molecular and developmental signatures. It has been widely accepted that exhaustion and senescence of T cells are important strategies for cancer cells to evade immunosurveillance and maintain the immunosuppressive microenvironment. Herein, this review summarizes the phenotypic and functional features of exhaust and senescent T cells, and describes the key drivers of the two T cell dysfunctional states in the tumor microenvironment and their functional roles in ovarian cancer. Furthermore, we present a summary of the molecular machinery and signaling pathways governing T cell exhaustion and senescence. Possible strategies that can prevent and/or reverse T cell dysfunction are also explored. An in-depth understanding of exhausted and senescent T cells will provide novel strategies to enhance immunotherapy of ovarian cancer through redirecting tumor-specific T cells away from a dysfunctional developmental trajectory.

Immune modulatory microRNAs in tumors, their clinical relevance in diagnosis and therapy

The importance of the immune system in regulating tumor growth by inducing immune cell-mediated cytotoxicity associated with patients' outcomes has been highlighted in the past years by an increasing life expectancy in patients with cancer on treatment with different immunotherapeutics. However, tumors often escape immune surveillance, which is accomplished by different mechanisms. Recent studies demonstrated an essential role of small non-coding RNAs, such as microRNAs (miRNAs), in the post-transcriptional control of immune modulatory molecules. Multiple methods have been used to identify miRNAs targeting genes involved in escaping immune recognition including miRNAs targeting CTLA-4, PD-L1, HLA-G, components of the major histocompatibility class I antigen processing machinery (APM) as well as other immune response-relevant genes in tumors. Due to their function, these immune modulatory miRNAs can be used as (1) diagnostic and prognostic biomarkers allowing to discriminate between tumor stages and to predict the patients' outcome as well as response and resistance to (immuno) therapies and as (2) therapeutic targets for the treatment of tumor patients. This review summarizes the role of miRNAs in tumor-mediated immune escape, discuss their potential as diagnostic, prognostic and predictive tools as well as their use as therapeutics including alternative application methods, such as chimeric antigen receptor T cells.

The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancer

BACKGROUND

The clinicopathological parameters such as residual tumor, grade, the International Federation of Gynecology and Obstetrics (FIGO) score are often used to predict the survival of ovarian cancer patients, but the 5-year survival of high grade serous ovarian cancer (HGSOC) still remains around 30%. Hence, the relentless pursuit of enhanced prognostic tools for HGSOC, this study introduces an unprecedented gene expression-based molecular prognostic score (mPS). Derived from a novel 20-gene signature through Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression, the mPS stands out for its predictive prowess.

RESULTS

Validation across diverse datasets, including training and test sets (n = 491 each) and a large HGSOC patient cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium (n = 7542), consistently shows an area-under-curve (AUC) around 0.7 for predicting 5-year overall survival. The mPS's impact on prognosis resonates profoundly, yielding an adjusted hazard-ratio (HR) of 6.1 (95% CI: 3.65-10.3; p < 0.001), overshadowing conventional parameters-FIGO score, residual disease, and age. Molecular insights gleaned from mPS stratification uncover intriguing pathways, with focal-adhesion, Wnt, and Notch signaling upregulated, and antigen processing and presentation downregulated (p < 0.001) in high-risk HGSOC cohorts.

CONCLUSION

Positioned as a robust prognostic marker, the 20-gene signature-derived mPS emerges as a potential game-changer in clinical settings. Beyond its role in predicting overall survival, its implications extend to guiding alternative therapies, especially targeting Wnt/Notch signaling pathways and immune evasion-a promising avenue for improving outcomes in high-risk HGSOC patients.

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms

BACKGROUND

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals.

METHODS

We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

RESULTS

Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)].

CONCLUSIONS

Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

IMPACT

HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

A single institutional clinical outcome for stages III and IV ovarian cancer patients treated with dose-dense TC therapy in the frontline or first platinum-sensitive relapse setting

AIM

Dose-dense paclitaxel /carboplatin (ddTC) therapy was shown to be more effective against ovarian cancer than conventional tri-weekly TC in the JGOG3016 study. However, two phase III studies performed after JGOG3016 did not show the same positive results. Because we have been using ddTC in the frontline or first platinum-sensitive relapse of ovarian cancer, we investigated the clinical outcome of the patients treated with ddTC.

METHODS

We retrospectively examined the response rate (RR), progression free survival (PFS) and adverse events of the patients who were treated with ddTC for stage III and IV epithelial ovarian, tubal and peritoneal cancer from January 2012 to December 2018.

RESULTS

We analyzed 50 patients for frontline treatment and 11 patients for first platinum-sensitive relapse treatment, excluding those receiving maintenance therapy. Among the patients that received frontline ddTC treatment, RR was 82.9% for those in a neo-adjuvant chemotherapy (NACT) setting and 85.0% for those in an adjuvant setting. The median progression-free survival (PFS) was 20 months after initial therapy. Among 31 cases that achieved remission by frontline surgery and the following ddTC, 22 had a platinum-sensitive relapse. RR of 11 patients treated with ddTC therapy alone for the first platinum-sensitive relapse was 81.8%, and the median PFS of these patients was 22 months after the first recurrence.

CONCLUSIONS

ddTC therapy for advanced ovarian cancer achieved high response rates in all settings (NACT, adjuvant or platinum-sensitive relapse). ddTC therapy was effective for improving the prognosis of patients with stages III and IV of ovarian cancer.

The Progression and Prospects of the Gene Expression Profiling in Ovarian Epithelial Cancer

Ovarian cancer is one of the most common cancers with a high mortality rate among females worldwide. The understanding of the pathogenesis of the disease is highly important to provide personalized therapy to the patients. Ovarian cancer is as heterogeneous as colon and breast cancer which makes it difficult to treat. The development of gene signature is the only hope in providing targeted therapy to improve the survival of ovarian cancer patients. Malignant epithelial carcinomas are the most common cancers of the ovary with different histological and molecular subtypes and clinical behavior. The development of precursor lesions of ovarian carcinoma in the tubes and endometrium has provided a new dimension to the origin of ovarian cancers. The clinical utility of various gene signatures may not be logical unless validated. Validated gene signatures can aid the clinician in deciding the appropriate line of treatment.

The microRNA Let-7 and its exosomal form: Epigenetic regulators of gynecological cancers

Many types of gynecological cancer (GC) are often silent until they reach an advanced stage, and are therefore often diagnosed too late for effective treatment. Hence, there is a real need for more efficient diagnosis and treatment for patients with GC. During recent years, researchers have increasingly studied the impact of microRNAs cancer development, leading to a number of applications in detection and treatment. MicroRNAs are a particular group of tiny RNA molecules that regulate regular gene expression by affecting the translation process. The downregulation of numerous miRNAs has been observed in human malignancies. Let-7 is an example of a miRNA that controls cellular processes as well as signaling cascades to affect post-transcriptional gene expression. Recent research supports the hypothesis that enhancing let-7 expression in those cancers where it is downregulated may be a potential treatment option. Exosomes are tiny vesicles that move through body fluids and can include components like miRNAs (including let-7) that are important for communication between cells. Studies proved that exosomes are able to enhance tumor growth, angiogenesis, chemoresistance, metastasis, and immune evasion, thus suggesting their importance in GC management.

Molecular Pathology of Ovarian Epithelial Neoplasms: Predictive, Prognostic, and Emerging Biomarkers

This review focuses on the diagnostic, prognostic, and predictive molecular biomarkers in ovarian epithelial neoplasms in the context of their morphologic classifications. Currently, most clinically actionable molecular findings are reported in high-grade serous carcinomas; however, the data on less common tumor types are rapidly accelerating. Overall, the advances in genomic knowledge over the last decade highlight the significance of integrating molecular findings with morphology in ovarian epithelial tumors for a wide-range of clinical applications, from assistance in diagnosis to predicting response to therapy.

MRI-based radiomics model to preoperatively predict mesenchymal transition subtype in high-grade serous ovarian cancer

AIM

To develop a magnetic resonance imaging (MRI)-based radiomics model for the preoperative identification of mesenchymal transition (MT) subtype in high-grade serous ovarian cancer (HGSOC).

MATERIALS AND METHODS

One hundred and eighty-nine patients with histopathologically confirmed HGSOC were enrolled retrospectively. Among the included patients, 55 patients were determined as the MT subtype and the remaining 134 were non-MT subtype. After extracting a total of 204 features from T2-weighted imaging (T2WI) and contrast-enhanced (CE)-T1WI images, the Mann-Whitney U-test, Spearman correlation test, and Boruta algorithm were adopted to select the optimal feature set. Three classifiers, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were trained to develop radiomics models. The performance of established models was evaluated from three aspects: discrimination, calibration, and clinical utility.

RESULTS

Seven radiomics features relevant to MT subtypes were selected to build the radiomics models. The model based on the RF algorithm showed the best performance in predicting MT subtype, with areas under the curves (AUCs) of 0.866 (95 % confidence interval [CI]: 0.797-0.936) and 0.852 (95 % CI: 0.736-0.967) in the training and testing cohorts, respectively. The calibration curves, supported with Brier scores, indicated very good consistency between observation and prediction. Decision curve analysis (DCA) showed that the RF-based model could provide more net benefit, which suggested favorable utility in clinical application.

CONCLUSION

The RF-based radiomics model provided accurate identification of MT from the non-MT subtype and may help facilitate personalised management of HGSOC.

Evaluation of the prognostic potential of histopathological subtyping in high-grade serous ovarian carcinoma

High-grade serous ovarian carcinoma (HGSOC) can be categorized into four gene expression-based subtypes, with supposedly distinct prognoses and treatment responses. Murakami et al. translated these gene expression-based subtypes into the histopathological mesenchymal, immunoreactive, solid and proliferative, and papilloglandular subtypes, showing differences in survival outcomes. Miyagawa et al. refined these criteria to improve the interobserver concordance. The current retrospective study evaluated the interobserver variability and the prognostic differences between the histopathologic subtypes using the criteria of both Murakami et al. and Miyagawa et al. in 208 HGSOC cases. The mesenchymal subtype was considered first, followed by the immunoreactive subtype. Non-conforming cases were categorized as solid and proliferative or papilloglandular. The mesenchymal subtype was identified in 122 patients (58.7%) for both criteria. Using the criteria of Murakami et al., 10 cases (4.8%) were immunoreactive, 26 (12.5%) solid and proliferative, and 50 (24%) papilloglandular, with a concordance rate of 62.5% (κ = 0.34, p < .001). Using the Miyagawa et al. criteria, 23 cases (11%) were immunoreactive, 20 (9.6%) solid and proliferative, and 43 (20.7%) papilloglandular. No survival differences were observed between the subtypes. The fair reproducibility of the histopathological subtype classification of HGSOC and the lack of survival differences among these subtypes indicate the need for further refinement of the criteria and exploration of their correlation with overall survival outcomes before clinical application.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

A predictive and prognostic model for surgical outcome and prognosis in ovarian cancer computed by clinico-pathological and serological parameters (CA125, HE4, mesothelin)

OBJECTIVES

Numerous prognostic models have been proposed for ovarian cancer, extending from single serological factors to complex gene-expression signatures. Nonetheless, these models have not been routinely translated into clinical practice. We constructed a robust and readily calculable model for predicting surgical outcome and prognosis of ovarian cancer patients by exploiting commonly available clinico-pathological factors and three selected serum parameters.

METHODS

Serum CA125, human epididymis protein 4 (HE4) and mesothelin (MSL) were quantified by Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio) in a total of 342 serum samples from 190 ovarian cancer patients, including 152 paired pre- and post-operative samples.

RESULTS

Detection of pre-operative HE4 and CA125 was the optimal marker combination for blood-based prediction of surgical outcome (AUC=0.86). We constructed a prognostic model, computed by serum levels of pre-operative CA125, post-operative HE4, post-operative MSL and surgical outcome. Prognostic performance of our model was superior to any of these parameters alone and was independent from BRCA1/2 mutational status. We subsequently transformed our model into a prognostic risk index, stratifying patients as "lower risk" or "higher risk". In "higher risk" patients, relapse or death was predicted with an AUC of 0.89 and they had a significantly shorter progression free survival (HR: 9.74; 95 % CI: 5.95-15.93; p<0.0001) and overall survival (HR: 5.62; 95 % CI: 3.16-9.99; p<0.0001) compared to "lower risk" patients.

CONCLUSIONS

We present a robust predictive/prognostic model for ovarian cancer, which could readily be implemented into routine diagnostics in order to identify ovarian cancer patients at high risk of recurrence.

Residual ANTXR1+ myofibroblasts after chemotherapy inhibit anti-tumor immunity via YAP1 signaling pathway

Although cancer-associated fibroblast (CAF) heterogeneity is well-established, the impact of chemotherapy on CAF populations remains poorly understood. Here we address this question in high-grade serous ovarian cancer (HGSOC), in which we previously identified 4 CAF populations. While the global content in stroma increases in HGSOC after chemotherapy, the proportion of FAP+ CAF (also called CAF-S1) decreases. Still, maintenance of high residual CAF-S1 content after chemotherapy is associated with reduced CD8+ T lymphocyte density and poor patient prognosis, emphasizing the importance of CAF-S1 reduction upon treatment. Single cell analysis, spatial transcriptomics and immunohistochemistry reveal that the content in the ECM-producing ANTXR1+ CAF-S1 cluster (ECM-myCAF) is the most affected by chemotherapy. Moreover, functional assays demonstrate that ECM-myCAF isolated from HGSOC reduce CD8+ T-cell cytotoxicity through a Yes Associated Protein 1 (YAP1)-dependent mechanism. Thus, efficient inhibition after treatment of YAP1-signaling pathway in the ECM-myCAF cluster could enhance CD8+ T-cell cytotoxicity. Altogether, these data pave the way for therapy targeting YAP1 in ECM-myCAF in HGSOC.

Tumor-Stroma Proportion to Predict Chemoresistance in Patients With Ovarian Cancer

IMPORTANCE

Platinum-based chemotherapy is the backbone of standard-of-care treatment for patients with advanced-stage, high-grade serous carcinoma (HGSC), the most common form of ovarian cancer; however, one-third of patients have or acquire chemoresistance toward platinum-based therapies.

OBJECTIVE

To demonstrate the utility of tumor-stroma proportion (TSP) as a predictive biomarker of chemoresistance of HGSC, progression-free survival (PFS), and overall survival (OS).

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study leveraged tumors from patients with HGSC in The Cancer Genome Atlas (TCGA) cohort (1993-2013) and an independent cohort of resected clinical specimens from patients with HGSC (2004-2014) available in diagnostic and tissue microarray formats from the University of Tübingen in Germany. Data analysis was conducted from January 2021 to January 2024.

EXPOSURE

Diagnosis of HGSC.

MAIN OUTCOMES AND MEASURES

Principal outcome measures were the ability of TSP to predict platinum chemoresistance, PFS, and OS. Using hematoxylin and eosin-stained slides from the Tübingen cohort (used for routine diagnostic assessment from surgical specimens) as well as tissue microarrays, representative sections of tumors for scoring of TSP were identified using previously evaluated cutoffs of 50% stroma or greater (high TSP) and less than 50% stroma (low TSP). Digitized slides from the TCGA Cohort were analyzed and scored in a similar fashion. Kaplan-Meier time-to-event functions were fit to estimate PFS and OS.

RESULTS

The study included 103 patients (mean [SD] age, 61.6 [11.1] years) from the TCGA cohort and 192 patients (mean [SD] age at diagnosis, 63.7 [11.1] years) from the Tübingen cohort. In the TCGA cohort, there was no significant association of TSP levels with chemoresistance, PFS, or OS. However, in the Tübingen cohort, high TSP was associated with significantly shorter PFS (HR, 1.586; 95% CI, 1.093-2.302; P = .02) and OS (hazard ratio [HR], 1.867; 1.249-2.789; P = .002). Patients with chemoresistant tumors were twice as likely to have high TSP as compared to patients with chemosensitive tumors (HR, 2.861; 95% CI, 1.256-6.515; P = .01). In tissue microarrays from 185 patients from the Tübingen cohort, high TSP was again associated with significantly shorter PFS (HR, 1.675; 95% CI, 1.012-2.772 P = .04) and OS (HR, 2.491; 95% CI, 1.585-3.912; P < .001).

CONCLUSIONS AND RELEVANCE

In this prognostic study, TSP was a consistent and reproducible marker of clinical outcome measures of HGSC, including PFS, OS, and platinum chemoresistance. Accurate and cost-effective predictive biomarkers of platinum chemotherapy resistance are needed to identify patients most likely to benefit from standard treatments, and TSP can easily be implemented and integrated into prospective clinical trial design and adapted to identify patients who are least likely to benefit long-term from conventional platinum-based cytotoxic chemotherapy treatment at the time of initial diagnosis.

Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements

Ovarian cancer (OC) is the female genital malignancy with the highest lethality. Patients present a poor prognosis mainly due to the late clinical presentation allied with the common acquisition of chemoresistance and a high rate of tumour recurrence. Effective screening, accurate diagnosis, and personalised multidisciplinary treatments are crucial for improving patients' survival and quality of life. This comprehensive narrative review aims to describe the current knowledge on the aetiology, prevention, diagnosis, and treatment of OC, highlighting the latest significant advancements and future directions. Traditionally, OC treatment involves the combination of cytoreductive surgery and platinum-based chemotherapy. Although more therapeutical approaches have been developed, the lack of established predictive biomarkers to guide disease management has led to only marginal improvements in progression-free survival (PFS) while patients face an increasing level of toxicity. Fortunately, because of a better overall understanding of ovarian tumourigenesis and advancements in the disease's (epi)genetic and molecular profiling, a paradigm shift has emerged with the identification of new disease biomarkers and the proposal of targeted therapeutic approaches to postpone disease recurrence and decrease side effects, while increasing patients' survival. Despite this progress, several challenges in disease management, including disease heterogeneity and drug resistance, still need to be overcome.

Exploring the tumor micro-environment in primary and metastatic tumors of different ovarian cancer histotypes

Ovarian cancer is a highly heterogeneous disease consisting of at least five different histological subtypes with varying clinical features, cells of origin, molecular composition, risk factors, and treatments. While most single-cell studies have focused on High grade serous ovarian cancer, a comprehensive landscape of the constituent cell types and their interactions within the tumor microenvironment are yet to be established in the different ovarian cancer histotypes. Further characterization of tumor progression, metastasis, and various histotypes are also needed to connect molecular signatures to pathological grading for personalized diagnosis and tailored treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allowed us to deconstruct the histotypes and tumor site-specific expression patterns of cells in the tumor, and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving precision disease stratification and optimizing treatment options.

CX3CR1 is a potential biomarker of immune microenvironment and prognosis in epithelial ovarian cancer

Immunotherapy is less efficient for epithelial ovarian cancer and lacks ideal biomarkers to select the best beneficiaries for immunotherapy. CX3CR1 as chemokine receptor mainly expressed on immune cell membranes, and combined with its unique ligand CX3CL1, mediates tissue chemotaxis and adhesion of immune cells. However, the immune functional and prognostic value of CX3CR1 in epithelial ovarian cancer has not been clarified. A comprehensive retrospective analysis was performed by using the online database to identify the underlying immunological mechanisms and prognostic value of CX3CR1. The Human Protein Atlas, gene expression profiling interactive analysis, and TISIDB (an integrated repository portal for tumor-immune system interactions) database showed that CX3CR1 expressed higher in epithelial ovarian cancer than that in normal ovarian tissue. Four hundred twenty-two cases from Gene Expression Profiling Interactive Analysis and 1656 cases from Kaplan-Meier plotter database showed higher expression of CX3CR1 (above median) was associated with unfavorable overall survival. TIMER, UALCAN, and TISIDB database were applied to validate CX3CR1 negative impact on overall survival. In addition, correlation analysis showed that the expression level of CX3CR1 was positive association with infiltrating levels of B cells (R = 0.31, P = 3.10e-12), CD8+ T cells (R = 0.26, P = 7.93e-09), CD4+ T cells (R = 0.11, P = 1.41e-02), macrophages (R = 0.32, P = 4.29e-13), dendritic cells (R = 0.27, P = 2.98e-09), and neutrophil (R = 0.25, P = 3.25e-08) in epithelial ovarian cancer. Therefore, CX3CR1 involved in reshaping the immune microenvironment for epithelial ovarian cancer and maybe a potential immunotherapy target and prognostic marker for ovarian cancer.

Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer

Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes between the clusters by using 10 machine-learning algorithms (101 combinations). Patients with high MLDPS had unfavourable survival rates and have good prediction performance in all cohorts and in-house cohorts. The MLDPS exhibited robust and dramatically superior capability than 21 published signatures. Of note, low MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells. Additionally, patients suffering from OV with low MLDIS were more sensitive to immunotherapy. Meanwhile, patients with low MLDIS might benefit from chemotherapy, and 19 compounds that may be potential agents for patients with low MLDIS were identified. MLDIS presents an appealing instrument for the identification of patients at high/low risk. This could enhance the precision treatment, ultimately guiding the clinical management of OV.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms

Introduction

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals.

Methods

We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

Results

Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]).

Conclusions

A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

Long noncoding RNA FTX promotes epithelial-mesenchymal transition of epithelial ovarian cancer through modulating miR-7515/TPD52 and activating Met/Akt/mTOR

Overexpressed long noncoding RNA FTX is associated with low survival rate of epithelial ovarian cancer (EOC) patients, and enhances tumor infiltration. Thus, we aim to illuminate the undefined underlying mechanisms. Real-time quantitative polymerase chain reaction was applied to detect the expressions of FTX, miR-7515, miR-342-3p, miR-940, miR-150-5p, miR-205-5p and tumor protein D52 (TPD52). Cell counting kit-8 and transwell assays were utilized to explore the cell viability, migration or invasion of EOC cells. Western blot was conducted to measure the expressions of E-cadherin, N-cadherin, Met, phosphorylated (p)-Met, Akt, p-Akt, mTOR and p-mTOR. LncBase and TargetScan predicted the binding of miR-7515 with FTX, and the binding of TPD52 with miR-7515, respectively. The two bindings were further validated by dual luciferase reporter assay. As a result, FTX sponged miR-7515 and miR-7515 targeted to TPD52. FTX was overexpressed in four EOC cell lines. Overexpressed FTX enhanced the cell viability, migration or invasion of EOC cells, elevated N-cadherin and TPD52 expressions, phosphorylated Met/Akt/mTOR, and inhibited E-cadherin expression. All these influences were subsequently reversed by miR-7515 mimic. Collectively, FTX regulates miR-7515/TPD52 to facilitate the migration, invasion or epithelial-mesenchymal transition of EOC through activating Met/Akt/mTOR signaling pathway.

Deciphering tumor immune microenvironment differences between high-grade serous and endometrioid ovarian cancer to investigate their potential in indicating immunotherapy response

BACKGROUND

Ovarian cancer is a significant public health concern with a poor prognosis for epithelial ovarian cancer. To explore the potential of immunotherapy in treating epithelial ovarian cancer, we investigated the immune microenvironments of 52 patients with epithelial ovarian cancer, including 43 with high-grade serous ovarian cancer and 9 with endometrioid ovarian cancer.

RESULTS

Fresh tumor tissue was analyzed for genetic mutations and various parameters related to immune evasion and infiltration. The mean stromal score (stromal cell infiltration) in high-grade serous ovarian cancer was higher than in endometrioid ovarian cancer. The infiltration of CD8 T cells and exhausted CD8 T cells were found to be more extensive in high-grade serous ovarian cancer. Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and cancer-associated fibroblasts (CAF) scores were also higher in the high-grade serous ovarian cancer group, suggesting that the number of cytotoxic lymphocytes in the tumor microenvironment of high-grade serous ovarian cancer is likely lower compared to endometrioid ovarian cancer.

CONCLUSIONS

The high mean stromal score and more extensive infiltration and exhaustion of CD8 T cells in high-grade serous ovarian cancer indicate that high-grade serous ovarian cancer exhibits a higher level of cytotoxic T cell infiltration, yet these T cells tend to be in a dysfunctional state. Higher Tumor Immune Dysfunction and Exclusion scores, T cell exclusion scores, and CAF scores in high-grade serous ovarian cancers suggest that immune escape is more likely to occur in high-grade serous ovarian cancer, thus endometrioid ovarian cancer may be more conducive to immunotherapy. Therefore, it is crucial to design immunotherapy clinical trials for ovarian cancer to distinguish between high-grade serous and endometrioid ovarian cancer from the outset. This distinction will help optimize treatment strategies and improve outcomes for patients with different subtypes.

HDACs alters negatively to the tumor immune microenvironment in gynecologic cancers

The role of histone deacetylases (HDACs) in the tumor immune microenvironment of gynecologic tumors remains unexplored. We integrated data from The Cancer Genome Atlas and Human Protein Atlas to examine HDAC expression in breast, cervical, ovarian, and endometrial cancers. Elevated HDAC expression correlated with poor prognosis and highly malignant cancer subtypes. Gene Set Enrichment Analysis revealed positive associations between HDAC expression and tumor proliferation signature, while negative associations were found with tumor inflammation signature. Increased HDAC expression was linked to reduced infiltration of natural killer (NK), NKT, and CD8+ T cells, along with negative associations with the expression of PSMB10, NKG7, CCL5, CD27, HLA-DQA1, and HLA-DQB1. In a murine 4T1 breast cancer model, treatment with suberoylanilide hydroxamic acid (SAHA; HDAC inhibitor) and PD-1 antibody significantly inhibited tumor growth and infiltration of CD3+ and CD8+ T cells. Real-time polymerase chain reaction revealed upregulated expressions of Psmb10, Nkg7, Ccl5, Cd8a, Cxcr6, and Cxcl9 genes, while Ctnnb1 and Myc genes were inhibited, indicating tumor suppression and immune microenvironment activation. Our study revealed that HDACs play tumor-promoting and immunosuppressive roles in gynecologic cancers, suggesting HDAC inhibitors as potential therapeutic agents for these cancers.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

A Novel Role of Connective Tissue Growth Factor in the Regulation of the Epithelial Phenotype

BACKGROUND

Epithelial-mesenchymal transition (EMT) is a biological process where epithelial cells lose their adhesive properties and gain invasive, metastatic, and mesenchymal properties. Maintaining the balance between the epithelial and mesenchymal stage is essential for tissue homeostasis. Many of the genes promoting mesenchymal transformation have been identified; however, our understanding of the genes responsible for maintaining the epithelial phenotype is limited. Our objective was to identify the genes responsible for maintaining the epithelial phenotype and inhibiting EMT.

METHODS

RNA seq was performed using an vitro model of EMT. CTGF expression was determined via qPCR and Western blot analysis. The knockout of CTGF was completed using the CTGF sgRNA CRISPR/CAS9. The tumorigenic potential was determined using NCG mice.

RESULTS

The knockout of CTGF in epithelial ovarian cancer cells leads to the acquisition of functional characteristics associated with the mesenchymal phenotype such as anoikis resistance, cytoskeleton remodeling, increased cell stiffness, and the acquisition of invasion and tumorigenic capacity.

CONCLUSIONS

We identified CTGF is an important regulator of the epithelial phenotype, and its loss is associated with the early cellular modifications required for EMT. We describe a novel role for CTGF, regulating cytoskeleton and the extracellular matrix interactions necessary for the conservation of epithelial structure and function. These findings provide a new window into understanding the early stages of mesenchymal transformation.

Tumor biology and impact on timing of surgery in advanced epithelial ovarian cancer

Recent advances in epithelial ovarian cancer research have led to a shift in treatment strategy from the traditional 'organ-centric' to a personalized tumor biology-based approach. Nevertheless, we are still far behind an individualized approach for cytoreductive surgery in advanced ovarian cancer; the gold standard of primary treatment in combination with systemic agents. The impact of tumor biology on treatment sequence is still understudied. It is obvious, that response to platinum-based therapy is crucial for the success of neoadjuvant chemotherapy. While high-grade serous and endometrioid tumors are commonly characterized by an excellent response, other subtypes are considered poor responders or even resistant to platinum. Undoubtedly, neoadjuvant chemotherapy may filter poor responders, but to date, we still do not have appropriate alternatives to platinum-based chemotherapy in the neoadjuvant and first-line setting and 'adjusting' systemic treatment in cases of poor response to neoadjuvant chemotherapy remains elusive. Primary cytoreduction is still considered the gold standard for fit patients with operable tumor dissemination patterns, especially for those ovarian cancer subtypes that show poor response to platinum. Of note, even in high-grade serous ovarian cancer, approximately 20% of tumors are platinum resistant and the benefit of neoadjuvant chemotherapy in this subgroup is limited. Interestingly, these tumors are associated with the mesenchymal molecular subtype, which in turn correlates with high risk for residual disease after cytoreductive surgery and is characterized by the worst survival outcome among high-grade ovarian cancers. This leads to the question, how to best tailor surgical radicality at the onset of patients' presentation to avoid associated morbidity and with a moderate benefit. Here, we give an overview of recent advances of interaction between tumor biology and surgery in ovarian cancer.

Construction of an Immunophenoscore-Related Signature for Evaluating Prognosis and Immunotherapy Sensitivity in Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological malignancy in the world, and immunotherapy is emerging as a promising treatment. Immunophenoscore (IPS) is a robust biomarker distinguishing sensitive responders from immunotherapy. In this study, we aimed to construct a prognostic model for predicting overall survival (OS) and identifying patients who would benefit from immunotherapy. First, we combined The Cancer Genome Atlas (TCGA) and The Cancer Immune Atlas (TCIA) data sets and incorporated 229 OC samples into a training cohort. The validation cohort included 240 OC samples from the Gene Expression Omnibus (GEO) cohort. The training cohort was divided into high- and low-IPS subgroups to obtain differentially expressed genes (DEGs). DEGs with OS were identified by Univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct the prognostic model. Then, immune and mutation analyses were performed to explore the relationship between the model and the tumor microenvironment (TME) and tumor mutation burden (TMB). Eighty-three DEGs were obtained between the high-and low-IPS subgroups, where 17 DEGs were associated with OS. The five essential genes were selected to establish the prognostic model, which showed high accuracy for predicting OS and could be an independent survival indicator. OC samples that were divided by risk scores showed distinguished immune status, TME, TMB, immunotherapy response, and chemotherapy sensitivity. Similar results were validated in the GEO cohort. We developed an immunophenoscore-related signature associated with the TME to predict OS and response to immunotherapy in OC.

A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance

Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.

Batch-effect correction with sample remeasurement in highly confounded case-control studies

Batch effects are pervasive in biomedical studies. One approach to address the batch effects is repeatedly measuring a subset of samples in each batch. These remeasured samples are used to estimate and correct the batch effects. However, rigorous statistical methods for batch-effect correction with remeasured samples are severely underdeveloped. Here we developed a framework for batch-effect correction using remeasured samples in highly confounded case-control studies. We provided theoretical analyses of the proposed procedure, evaluated its power characteristics and provided a power calculation tool to aid in the study design. We found that the number of samples that need to be remeasured depends strongly on the between-batch correlation. When the correlation is high, remeasuring a small subset of samples is possible to rescue most of the power.

Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters-three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.

Clinical Significance of the Stromatic Component in Ovarian Cancer: Quantity Over Quality in Outcome Prediction

Background

The tumor stroma is composed of a complex network of non-cancerous cells and extracellular matrix elements that collectively are crucial for cancer progression and treatment response. Within the realm of ovarian cancer, the expression of the stromal gene cluster has been linked to poorer progression-free and overall survival rates. However, in the age of precision medicine and genome sequencing, the notion that the simple measurement of tumor-stroma proportion alone can serve as a biomarker for clinical outcome is a topic that continues to generate controversy and provoke discussion. Our current study reveals that it is the quantity of stroma, rather than its quality, that serves as a clinically significant indicator of patient outcome in ovarian cancer.

Methods

This study leveraged the High-Grade-Serous-Carcinoma (HGSC) cohort of the publicly accessible Cancer Genome Atlas Program (TCGA) along with an independent cohort comprising HGSC clinical specimens in diagnostic and Tissue Microarray formats. Our objective was to investigate the correlation between the Tumor-Stroma-Proportion (TSP) and progression-free survival (PFS), overall survival (OS), and response to chemotherapy. We assessed these associations using H&E-stained slides and tissue microarrays. Our analysis employed semi-parametric models that accounted for age, metastases, and residual disease as controlling factors.

Results

We found that high TSP (>50% stroma) was associated with significantly shorter progression-free survival (PFS) (p=0.016) and overall survival (OS) (p=0.006). Tumors from patients with chemoresistant tumors were twice as likely to have high TSP as compared to tumors from chemosensitive patients (p=0.012). In tissue microarrays, high TSP was again associated with significantly shorter PFS (p=0.044) and OS (p=0.0001), further confirming our findings. The Area Under the ROC curve for the model predicting platinum was estimated at 0.7644.

Conclusions

In HGSC, TSP was a consistent and reproducible marker of clinical outcome measures, including PFS, OS, and platinum chemoresistance. Assessment of TSP as a predictive biomarker that can be easily implemented and integrated into prospective clinical trial design and adapted to identify, at time of initial diagnosis, patients who are least likely to benefit long-term from conventional platinum-based cytotoxic chemotherapy treatment.

"DEPHENCE" system-a novel regimen of therapy that is urgently needed in the high-grade serous ovarian cancer-a focus on anti-cancer stem cell and anti-tumor microenvironment targeted therapies

Ovarian cancer, especially high-grade serous type, is the most lethal gynecological malignancy. The lack of screening programs and the scarcity of symptomatology result in the late diagnosis in about 75% of affected women. Despite very demanding and aggressive surgical treatment, multiple-line chemotherapy regimens and both approved and clinically tested targeted therapies, the overall survival of patients is still unsatisfactory and disappointing. Research studies have recently brought some more understanding of the molecular diversity of the ovarian cancer, its unique intraperitoneal biology, the role of cancer stem cells, and the complexity of tumor microenvironment. There is a growing body of evidence that individualization of the treatment adjusted to the molecular and biochemical signature of the tumor as well as to the medical status of the patient should replace or supplement the foregoing therapy. In this review, we have proposed the principles of the novel regimen of the therapy that we called the "DEPHENCE" system, and we have extensively discussed the results of the studies focused on the ovarian cancer stem cells, other components of cancer metastatic niche, and, finally, clinical trials targeting these two environments. Through this, we have tried to present the evolving landscape of treatment options and put flesh on the experimental approach to attack the high-grade serous ovarian cancer multidirectionally, corresponding to the "DEPHENCE" system postulates.

BLM overexpression as a predictive biomarker for CHK1 inhibitor response in PARP inhibitor-resistant BRCA-mutant ovarian cancer

Poly(ADP-ribose) polymerase inhibitors (PARPis) have changed the treatment paradigm in breast cancer gene (BRCA)-mutant high-grade serous ovarian carcinoma (HGSC). However, most patients eventually develop resistance to PARPis, highlighting an unmet need for improved therapeutic strategies. Using high-throughput drug screens, we identified ataxia telangiectasia and rad3-related protein/checkpoint kinase 1 (CHK1) pathway inhibitors as cytotoxic and further validated the activity of the CHK1 inhibitor (CHK1i) prexasertib in PARPi-sensitive and -resistant BRCA-mutant HGSC cells and xenograft mouse models. CHK1i monotherapy induced DNA damage, apoptosis, and tumor size reduction. We then conducted a phase 2 study (NCT02203513) of prexasertib in patients with BRCA-mutant HGSC. The treatment was well tolerated but yielded an objective response rate of 6% (1 of 17; one partial response) in patients with previous PARPi treatment. Exploratory biomarker analyses revealed that replication stress and fork stabilization were associated with clinical benefit to CHK1i. In particular, overexpression of Bloom syndrome RecQ helicase (BLM) and cyclin E1 (CCNE1) overexpression or copy number gain/amplification were seen in patients who derived durable benefit from CHK1i. BRCA reversion mutation in previously PARPi-treated BRCA-mutant patients was not associated with resistance to CHK1i. Our findings suggest that replication fork-related genes should be further evaluated as biomarkers for CHK1i sensitivity in patients with BRCA-mutant HGSC.

MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

Biological and clinical impact of membrane EGFR expression in a subgroup of OC patients from the phase IV ovarian cancer MITO-16A/MANGO-OV2A trial

BACKGROUND

Validated prognostic biomarkers for anti-angiogenic therapy using the anti-VEGF antibody Bevacizumab in ovarian cancer (OC) patients are still an unmet clinical need. The EGFR can contribute to cancer-associated biological mechanisms in OC cells including angiogenesis, but its targeting gave disappointing results with less than 10% of OC patients treated with anti-EGFR compounds showing a positive response, likely due to a non adequate selection and stratification of EGFR-expressing OC patients.

METHODS

EGFR membrane expression was evaluated by immunohistochemistry in a cohort of 310 OC patients from the MITO-16A/MANGO-OV2A trial, designed to identify prognostic biomarkers of survival in patients treated with first line standard chemotherapy plus bevacizumab. Statistical analyses assessed the association between EGFR and clinical prognostic factors and survival outcomes. A single sample Gene Set Enrichment-like and Ingenuity Pathway Analyses were applied to the gene expression profile of 195 OC samples from the same cohort. In an OC in vitro model, biological experiments were performed to assess specific EGFR activation.

RESULTS

Based on EGFR-membrane expression, three OC subgroups of patients were identified being the subgroup with strong and homogeneous EGFR membrane localization, indicative of possible EGFR out/in signalling activation, an independent negative prognostic factor for overall survival of patients treated with an anti-angiogenic agent. This OC subgroup resulted statistically enriched of tumors of histotypes different than high grade serous lacking angiogenic molecular characteristics. At molecular level, among the EGFR-related molecular traits identified to be activated only in this patients' subgroup the crosstalk between EGFR with other RTKs also emerged. In vitro, we also showed a functional cross-talk between EGFR and AXL RTK; upon AXL silencing, the cells resulted more sensitive to EGFR targeting with erlotinib.

CONCLUSIONS

Strong and homogeneous cell membrane localization of EGFR, associated with specific transcriptional traits, can be considered a prognostic biomarker in OC patients and could be useful for a better OC patients' stratification and the identification of alternative therapeutic target/s in a personalized therapeutic approach.

Multi-omics analysis of the Indian ovarian cancer cohort revealed histotype-specific mutation and gene expression patterns

Introduction: In India, OVCa is women’s third most common and lethal cancer type, accounting for 6.7% of observed cancer incidences. The contribution of somatic mutations, aberrant expression of gene and splice forms in determining the cell fate, gene networks, tumour-specific variants, and the role of immune fraction infiltration have been proven essential in understanding tumorigenesis. However, their interplay in OVCa in a histotype-specific manner remains unclear in the Indian context. In the present study, we aimed to unravel the Indian population histotype-specific exome variants, differentially expressed gene modules, splice events and immune profiles of OVCa samples.

Methods: We analysed 10 tumour samples across 4 ovarian cancer histotypes along with 2 normal patient samples. This included BCFtool utilities and CNVkit for exome, WGCNA and DESeq2 for obtaining differential module hub genes and dysregulated miRNA targets, CIBERSORTx for individual immune profiles and rMATS for tumour specific splice variants.

Result: We identified population-specific novel mutations in Cancer Gene Census Tier1 and Tier2 genes. MUC16, MUC4, CIITA, and NCOR2 were among the most mutated genes, along with TP53. Transcriptome analysis showed significant overexpression of mutated genes MUC16, MUC4, and CIITA, whereas NCOR2 was downregulated. WGCNA revealed histotype-specific gene hubs and networks. Among the significant pathways, alteration in the immune system was one of the pathways, and immune profiling using CIBERSORTx revealed histotype-specific immune cell fraction. miRNA analysis revealed miR-200 family, miR-200a and miR-429 were upregulated in HGSOCs.Splice factor abrasion caused splicing perturbations, with the most abundant alternative splice event being exon skipping and the most spliced gene, SNHG17. Pathway analysis of spliced genes revealed translational elongation and Base excision repair as the pathways altered in OVCa.

Conclusion: Integrated exome, transcriptome, and splicing patterns revealed different population-specific molecular signatures of ovarian cancer histotypes in the Indian Cohort.

Methylation Signature Implicated in Immuno-Suppressive Activities in Tubo-Ovarian High-Grade Serous Carcinoma

BACKGROUND

Better understanding of prognostic factors in tubo-ovarian high-grade serous carcinoma (HGSC) is critical, as diagnosis confers an aggressive disease course. Variation in tumor DNA methylation shows promise predicting outcome, yet prior studies were largely platform-specific and unable to evaluate multiple molecular features.

METHODS

We analyzed genome-wide DNA methylation in 1,040 frozen HGSC, including 325 previously reported upon, seeking a multi-platform quantitative methylation signature that we evaluated in relation to clinical features, tumor characteristics, time to recurrence/death, extent of CD8+ tumor-infiltrating lymphocytes (TIL), gene expression molecular subtypes, and gene expression of the ATP-binding cassette transporter TAP1.

RESULTS

Methylation signature was associated with shorter time to recurrence, independent of clinical factors (N = 715 new set, hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.10-2.46; P = 0.015; N = 325 published set HR, 2.87; 95% CI, 2.17-3.81; P = 2.2 × 10-13) and remained prognostic after adjustment for gene expression molecular subtype and TAP1 expression (N = 599; HR, 2.22; 95% CI, 1.66-2.95; P = 4.1 × 10-8). Methylation signature was inversely related to CD8+ TIL levels (P = 2.4 × 10-7) and TAP1 expression (P = 0.0011) and was associated with gene expression molecular subtype (P = 5.9 × 10-4) in covariate-adjusted analysis.

CONCLUSIONS

Multi-center analysis identified a novel quantitative tumor methylation signature of HGSC applicable to numerous commercially available platforms indicative of shorter time to recurrence/death, adjusting for other factors. Along with immune cell composition analysis, these results suggest a role for DNA methylation in the immunosuppressive microenvironment.

IMPACT

This work aids in identification of targetable epigenome processes and stratification of patients for whom tailored treatment may be most beneficial.

Poor-prognosis molecular subtypes in adenocarcinomas of pancreato-biliary and gynecological origin: A systematic review

Pancreato-biliary and gynecological adenocarcinomas need better tools to predict clinical outcome. Potential prognostic mesenchymal(-like) transcriptome-based subtypes have been identified in these cancers. In this systematic review, we include studies into molecular subtyping and summarize biological and clinical features of the subtypes within and across sites of origin, searching for suggestions to improve classification and prognostication. PubMed and Embase were searched for original research articles describing potential mesenchymal(-like) mRNA-based subtypes in pancreato-biliary or gynecological adenocarcinomas. Studies limited to supervised clustering were excluded. Fourty-four studies, discussing cholangiocarcinomas, gallbladder, ampullary, pancreatic, ovarian, and endometrial adenocarcinomas were included. There was overlap in molecular and clinical features in mesenchymal(-like) subtypes across all adenocarcinomas. Approaches including microdissection were more likely to identify prognosis-associating subtypes. To conclude, molecular subtypes in pancreato-biliary and gynecological adenocarcinomas share biological and clinical characteristics. Furthermore, separation of stromal and epithelial signals should be applied in future studies into biliary and gynecological adenocarcinomas.

HOXB9 Overexpression Confers Chemoresistance to Ovarian Cancer Cells by Inducing ERCC-1, MRP-2, and XIAP

The purpose of this study was to identify the role of HOXB9 and associated molecular mechanism in acquiring chemoresistance to ovarian cancer cells. After establishing HOXB9-overexpressing cells (HOXB9-OE/SKOV3), cisplatin resistance-induced cells (Cis-R/SKOV3), and an ovarian cancer xenograft mouse model, the effects of HOXB9 were evaluated in vitro and in vivo. Expression levels of ERCC-1, MRP-2, XIAP, and Bax/Bcl-2 were assessed as putative mechanisms mediating chemoresistance. Cisplatin-induced apoptosis was significantly decreased in HOXB9-OE/SKOV3 compared to SKOV3. Cisplatin treatment of SKOV3 strongly induced ERCC-1, MRP-2, and XIAP, and apoptosis was strongly induced through the inhibition of Bcl-2 and activation of Bax. ERCC-1, MRP-2, XIAP, and Bcl-2 were also strongly induced in HOXB9 OE/SKOV3. In contrast to SKOV3, cisplatin treatment alone of HOXB9 OE/SKOV3 did not affect the expression of Bcl-2 and Bax, and consequently, there was no increase in apoptosis. HOXB9 knockdown suppressed the expression of ERCC-1 and XIAP, but did not affect MRP-2 and Bcl-2/Bax expression in HOXB9 OE/SKOV3 and Cis-R/SKOV3, and caused a small increase in apoptosis. Treatment of SKOV3 with both cisplatin and siRNA_HOXB9 led to complete suppression of ERCC-1, MRP-2, and XIAP, and significantly increased apoptosis through inhibition of Bcl-2 expression and activation of Bax. The results observed in Cis-R/SKOV3 were similar to that in HOXB9 OE/SKOV3. Our data suggest that HOXB9 overexpression may cause chemoresistance in ovarian cancer cells by differential induction of ERCC-1, MRP-2, and XIAP depending on the strength of HOXB9 expression through inhibition of the mitochondrial pathway of apoptosis, including Bax/Bcl-2.

Interstitial brachytherapy combined with PARP inhibitors in the treatment of chemoresistant recurrent epithelial ovarian cancer: A case report

Background

Chemoresistance generally develops in patients with advanced epithelial ovarian cancer, and the prognosis is still very poor, with an expected survival time of less than one year. For this population of individuals, there is currently no standard protocol for clinical benefit.

Case presentation

We report a case of an elderly woman diagnosed with stage IIIC high-grade serous ovarian cancer (HGSOC). During a follow-up time of 6 years, the patient initially received multiple sequential courses of chemotherapy with platinum-based regimens and with no maintenance therapy. Similar to most patients with advanced HGSOC, she developed platinum resistance and experienced poor treatment results with a short progression-free survival (PFS). Ultimately, we gave the patient traditional non-platinum-based chemotherapy with bevacizumab and high-dose-rate interstitial brachytherapy followed by olaparib as a maintenance therapy. Up to now, the patient did response well to the treatment, and the PFS had exceeded 12 months.

Conclusion

High-dose-rate interstitial brachytherapy combination with PARP inhibitors may be an option for isolated chemoresistant recurrent epithelial ovarian cancer.

Molecular Management of High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.

Exploration of the underlying biological differences and targets in ovarian cancer patients with diverse immunotherapy response

Background

Preclinical trials of immunotherapy in ovarian cancer (OC) have shown promising results. This makes it meaningful to prospectively examine the biological mechanisms explaining the differences in response performances to immunotherapy among OC patients.

Methods

Open-accessed data was obtained from the Cancer Genome Atlas and Gene Expression Omnibus database. All the analysis was conducted using the R software.

Results

We firstly performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. The machine learning algorithm LASSO logistic regression and SVM-RFE were used to identify the characteristic genes. The genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were selected for molecular typing. Our result showed that the patients in Cluster1 might have a better prognosis and might be more sensitive to immunotherapy, including PD-1 and CTLA4 therapy options. Pathway enrichment analysis showed that in Cluster2, the pathway of EMT, TNFα/NF-kB signaling, IL2/STAT5 signaling, inflammatory response, KRAS signaling, apical junction, complement, interferon-gamma response and allograft rejection were significantly activated. Also, genomic instability analysis was performed to identify the underlying genomic difference between the different Cluster patients. Single-cell analysis showed that the DPT, COL6A6, LSAMP and RUNX1T1 were mainly expressed in the fibroblasts. We then quantified the CAFs infiltration in the OC samples. The result showed that patients with low CAFs infiltration might have a lower TIDE score and a higher proportion of immunotherapy responders. Also, we found all the characteristic genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were upregulated in the patients with high CAFs infiltration. Immune infiltration analysis showed that the patients in Cluster2 might have a higher infiltration of naive B cells, activated NK cells and resting Dendritic cells.

Conclusions

In summary, our study provides new insights into ovarian cancer immunotherapy. Meanwhile, specific targets DPT, RUNX1T1, PTPRN, LSAMP, FDCSP, COL6A6 and CAFs were identified for OC immunotherapy.

Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma

Background

As one of the key criteria to differentiate benign vs. malignant tumors in ovarian and other solid cancers, tumor-stroma reaction (TSR) is long observed by pathologists and has been found correlated with patient prognosis. However, paucity of study aims to overcome subjective bias or automate TSR evaluation for enabling association analysis to a large cohort.

Materials and methods

Serving as positive and negative sets of TSR studies, H&E slides of primary tumors of high-grade serous ovarian carcinoma (HGSOC) (n = 291) and serous borderline ovarian tumor (SBOT) (n = 15) were digitally scanned. Three pathologist-defined quantification criteria were used to characterize the extents of TSR. Scores for each criterion were annotated (0/1/2 as none-low/intermediate/high) in the training set consisting of 18,265 H&E patches. Serial of deep learning (DL) models were trained to identify tumor vs. stroma regions and predict TSR scores. After cross-validation and independent validations, the trained models were generalized to the entire HGSOC cohort and correlated with clinical characteristics. In a subset of cases tumor transcriptomes were available, gene- and pathway-level association studies were conducted with TSR scores.

Results

The trained models accurately identified the tumor stroma tissue regions and predicted TSR scores. Within tumor stroma interface region, TSR fibrosis scores were strongly associated with patient prognosis. Cancer signaling aberrations associated 14 KEGG pathways were also found positively correlated with TSR-fibrosis score.

Conclusion

With the aid of DL, TSR evaluation could be generalized to large cohort to enable prognostic association analysis and facilitate discovering novel gene and pathways associated with disease progress.

Peritoneal dissemination of high-grade serous ovarian cancer: pivotal roles of chromosomal instability and epigenetic dynamics

Epithelial ovarian cancer remains the lethal gynecological malignancy in women. The representative histotype is high-grade serous carcinoma (HGSC), and most patients with HGSC present at advanced stages with peritoneal dissemination. Since the peritoneal dissemination is the most important factor for poor prognosis of the patients, complete exploration for its molecular mechanisms is mandatory. In this narrative review, being based on the clinical, pathologic, and genomic findings of HGSC, chromosomal instability and epigenetic dynamics have been discussed as the potential drivers for cancer development in the fallopian tube, acquisition of cancer stem cell (CSC)-like properties, and peritoneal metastasis of HGSC. The natural history of carcinogenesis with clonal evolution, and adaptation to microenvironment of peritoneal dissemination of HGSC should be targeted in the novel development of strategies for prevention, early detection, and precision treatment for patients with HGSC.

Targeting Tyrosine Kinases in Ovarian Cancer: Small Molecule Inhibitor and Monoclonal Antibody, Where Are We Now?

Ovarian cancer is one of the most lethal gynaecological malignancies worldwide. Despite high success rates following first time treatment, this heterogenous disease is prone to recurrence. Oncogenic activity of receptor tyrosine kinases is believed to drive the progression of ovarian cancer. Here we provide an update on the progress of the therapeutic targeting of receptor tyrosine kinases in ovarian cancer. Broadly, drug classes that inhibit tyrosine kinase/pathways can be classified as small molecule inhibitors, monoclonal antibodies, or immunotherapeutic vaccines. Small molecule inhibitors tested in clinical trials thus far include sorafenib, sunitinib, pazopanib, tivantinib, and erlotinib. Monoclonal antibodies include bevacizumab, cetuximab, pertuzumab, trastuzumab, and seribantumab. While numerous trials have been carried out, the results of monotherapeutic agents have not been satisfactory. For combination with chemotherapy, the monoclonal antibodies appear more effective, though the efficacy is limited by low frequency of target alteration and a lack of useful predictive markers for treatment stratification. There remain critical gaps for the treatment of platinum-resistant ovarian cancers; however, platinum-sensitive tumours may benefit from the combination of tyrosine kinase targeting drugs and PARP inhibitors. Immunotherapeutics such as a peptide B-cell epitope vaccine and plasmid-based DNA vaccine have shown some efficacy both as monotherapeutic agents and in combination therapy, but require further development to validate current findings. In conclusion, the tyrosine kinases remain attractive targets for treating ovarian cancers. Future development will need to consider effective drug combination, frequency of target, and developing predictive biomarker.