Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors.

BACKGROUND: Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. PURPOSE: To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. METHODS: A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. RESULTS: The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. CONCLUSIONS: Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal ( https://cptac-data-portal.georgetown.edu/ ).

Primary ovarian mucinous carcinoid arising in a mature cystic teratoma-A rare entity.

Mature cystic teratomas are benign unilateral tumors often diagnosed in young females. Carcinoid tumors are slow-growing tumors originating from neuroendocrine cells. A thorough histopathological study of the tumor is mandatory and the surgical treatment is adapted according to the characteristics of the patient. The present case was considered as a primary mucinous carcinoid tumor of the ovary because it was confined to the ovary, had an intact capsule, no vascular invasion, or other suspicious lesions were noted in the abdominal cavity. This case is notable due to the rarity of its occurrence and the age of presentation.

Parity is associated with better prognosis in ovarian germ cell tumors, but not in other ovarian cancer subtypes.

Ovarian cancer is influenced by reproductive factors, with a reduced risk of epithelial ovarian cancer in parous women. Nonepithelial ovarian cancer frequently affects young women and often precedes or occurs during the childbearing years. However, the impact of reproductive factors on ovarian cancer survival remains unclear: in epithelial ovarian cancer, data are conflicting, and subtype-specific associations have not been examined, and in nonepithelial ovarian cancer, it has not been studied. Using Swedish registers, we evaluated associations between women's reproductive history and cancer-specific mortality by subtype of epithelial and nonepithelial ovarian cancer in 3791 women born 1953 and later, diagnosed from 1990 to 2018. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated using Cox-proportional hazard models. Parity was associated with a 78% decreased risk of cause-specific mortality in 243 women with germ cell tumors (GCTs) (parous vs nulliparous, adjusted for age at diagnosis: HR: 0.22 [95% CI 0.07-0.62]), with a decreased risk with increasing number of births (per birth: HR: 0.60 [95% CI 0.38-0.95]). We found no evidence of associations between parity and cause-specific mortality among the 334 patients with sex-cord stromal tumors, nor among the 3214 patients with epithelial ovarian cancer; neither overall, nor by subtype. In conclusion, in our large, population-based study, parity was associated with a clearly better prognosis in GCTs but not in the other ovarian cancer subtypes. Future research on how hormone exposure impacts GCT development may lead to a better understanding of mechanisms affecting survival.

Estimating the risk of malignancy of adnexal masses: validation of the ADNEX model in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China.

BACKGROUND: This study aims to validate the diagnostic accuracy of the International Ovarian Tumor Analysis (IOTA) the Assessment of Different NEoplasias in the adneXa (ADNEX) model in the preoperative diagnosis of adnexal masses in the hands of nonexpert ultrasonographers in a gynaecological oncology centre in China. METHODS: This was a single oncology centre, retrospective diagnostic accuracy study of 620 patients. All patients underwent surgery, and the histopathological diagnosis was used as a reference standard. The masses were divided into five types according to the ADNEX model: benign ovarian tumours, borderline ovarian tumours (BOTs), stage I ovarian cancer (OC), stage II-IV OC and ovarian metastasis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of the ADNEX model to classify tumours into different histological types with and without cancer antigen 125 (CA 125) results. RESULTS: Of the 620 women, 402 (64.8%) had a benign ovarian tumour and 218 (35.2%) had a malignant ovarian tumour, including 86 (13.9%) with BOT, 75 (12.1%) with stage I OC, 53 (8.5%) with stage II-IV OC and 4 (0.6%) with ovarian metastasis. The AUC of the model to differentiate benign and malignant adnexal masses was 0.97 (95% CI, 0.96-0.98). Performance was excellent for the discrimination between benign and stage II-IV OC and between benign and ovarian metastasis, with AUCs of 0.99 (95% CI, 0.99-1.00) and 0.99 (95% CI, 0.98-1.00), respectively. The model was less effective at distinguishing between BOT and stage I OC and between BOT and ovarian metastasis, with AUCs of 0.54 (95% CI, 0.45-0.64) and 0.66 (95% CI, 0.56-0.77), respectively. When including CA125 in the model, the performance in discriminating between stage II-IV OC and stage I OC and between stage II-IV OC ovarian metastasis was improved (AUC increased from 0.88 to 0.94, P = 0.01, and from 0.86 to 0.97, p = 0.01). CONCLUSIONS: The IOTA ADNEX model has excellent performance in differentiating benign and malignant adnexal masses in the hands of nonexpert ultrasonographers with limited experience in China. In classifying different subtypes of ovarian cancers, the model has difficulty differentiating BOTs from stage I OC and BOTs from ovarian metastases.

SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study.

Epithelial ovarian cancer (EOC) is the most common cause of death from gynecological cancer. The outcomes of EOC are complicated, as it is often diagnosed late and comprises several heterogenous subtypes. As such, upfront treatment can be highly challenging. Although many significant advances in EOC management have been made over the past several decades, further work must be done to develop early detection tools capable of distinguishing between the various EOC subtypes. In this paper, we present a sophisticated analytical pipeline based on solid-phase microextraction (SPME) and three orthogonal LC/MS acquisition modes that facilitates the comprehensive mapping of a wide range of analytes in serum samples from patients with EOC. PLS-DA multivariate analysis of the metabolomic data was able to provide clear discrimination between all four main EOC subtypes: serous, endometrioid, clear cell, and mucinous carcinomas. The prognostic performance of discriminative metabolites and lipids was confirmed via multivariate receiver operating characteristic (ROC) analysis (AUC value > 88% with 20 features). Further pathway analysis using the top 57 dysregulated metabolic features showed distinct differences in amino acid, lipid, and steroids metabolism among the four EOC subtypes. Thus, metabolomic profiling can serve as a powerful tool for complementing histology in classifying EOC subtypes.

Classification of 101 BRCA1 and BRCA2 variants of uncertain significance by cosegregation study: A powerful approach.

Up to 80% of BRCA1 and BRCA2 genetic variants remain of uncertain clinical significance (VUSs). Only variants classified as pathogenic or likely pathogenic can guide breast and ovarian cancer prevention measures and treatment by PARP inhibitors. We report the first results of the ongoing French national COVAR (cosegregation variant) study, the aim of which is to classify BRCA1/2 VUSs. The classification method was a multifactorial model combining different associations between VUSs and cancer, including cosegregation data. At this time, among the 653 variants selected, 101 (15%) distinct variants shared by 1,624 families were classified as pathogenic/likely pathogenic or benign/likely benign by the COVAR study. Sixty-six of the 101 (65%) variants classified by COVAR would have remained VUSs without cosegregation data. Of note, among the 34 variants classified as pathogenic by COVAR, 16 remained VUSs or likely pathogenic when following the ACMG/AMP variant classification guidelines. Although the initiation and organization of cosegregation analyses require a considerable effort, the growing number of available genetic tests results in an increasing number of families sharing a particular variant, and thereby increases the power of such analyses. Here we demonstrate that variant cosegregation analyses are a powerful tool for the classification of variants in the BRCA1/2 breast-ovarian cancer predisposition genes.

TCGA molecular subgroups of endometrial carcinoma in ovarian endometrioid carcinoma: A quantitative systematic review.

BACKGROUND: Ovarian endometrioid carcinoma (OEC) shares morphological and molecular features with endometrial endometrioid carcinoma (EEC). Several studies assessed the four TCGA groups of EEC, i.e. POLE-mutated (POLEmut), mismatch repair-deficient (MMRd), no specific molecular profile (NSMP) and p53-abnormal (p53abn), in OEC; however, it is unclear whether the TCGA groups have the same distribution and clinicopathological features between OEC and EEC. OBJECTIVE: To assess the distribution and clinicopathological features of the TCGA groups in OEC. METHODS: A systematic review and meta-analysis was carried out by searching 7 electronic databases from January 2013 to April 2021 for studies assessing the TCGA classification in OEC. Prevalence of each TCGA group in OEC and of FIGO grade 3 and stage>I was pooled using a random-effect model. Prevalence of TCGA groups was compared between OEC and EEC, extracting EEC data from a previous meta-analysis. Kaplan-Meier and Cox regression survival analyses were performed for progression-free survival (PFS). A significant p-value<0.05 was adopted. RESULTS: Four studies with 785 patients were included. The frequency of the TCGA groups in OEC vs EEC was: POLEmut = 5% vs 7.6% (p = 0.594); MMRd = 14.6% vs 29.2% (p < 0.001); p53abn = 14% vs 7.8% (p = 0.097); NSMP = 66.4% vs 55.4% (p = 0.002). The pooled prevalence of FIGO grade 3 was: POLEmut = 19.2%; MMRd = 18.3%; p53abn = 38.1%; NSMP = 14.5%. The pooled prevalence of FIGO stage >I was: POLEmut = 31.6%; MMRd = 42.8%; p53abn = 48.5%; NSMP = 24.6%. Two-, 5- and 10-year PFS was: POLEmut = 100%, 100%, and 100%; MMRd = 89.1%, 82.2% and 73.3%; p53abn = 61.7%, 50.2% and 39.6%; NSMP = 87.7%, 79.6% and 65.5%. The hazard ratio for disease progression (reference = NSMP) was: POLEmut = not estimable (no events); MMRd = 0.825 (p = 0.626); p53abn = 2.786 (p = 0.001). CONCLUSION: The prognostic value of the TCGA groups was similar between OEC and EEC, despite the differences in the frequency and pathological features of each group.

Classification of High-Grade Serous Ovarian Carcinoma by Epithelial-to-Mesenchymal Transition Signature and Homologous Recombination Repair Genes.

High-grade serous ovarian cancer (HGSOC) is one of the deadliest cancers that can occur in women. This study aimed to investigate the molecular characteristics of HGSOC through integrative analysis of multi-omics data. We used fresh-frozen, chemotherapy-naïve primary ovarian cancer tissues and matched blood samples of HGSOC patients and conducted next-generation whole-exome sequencing (WES) and RNA sequencing (RNA-seq). Genomic and transcriptomic profiles were comprehensively compared between patients with germline BRCA1/2 mutations and others with wild-type BRCA1/2. HGSOC samples initially divided into two groups by the presence of germline BRCA1/2 mutations showed mutually exclusive somatic mutation patterns, yet the implementation of high-dimensional analysis of RNA-seq and application of epithelial-to-mesenchymal (EMT) index onto the HGSOC samples revealed that they can be divided into two subtypes; homologous recombination repair (HRR)-activated type and mesenchymal type. Patients with mesenchymal HGSOC, characterized by the activation of the EMT transcriptional program, low genomic alteration and diverse cell-type compositions, exhibited significantly worse overall survival than did those with HRR-activated HGSOC (p = 0.002). In validation with The Cancer Genome Atlas (TCGA) HGSOC data, patients with a high EMT index (≥the median) showed significantly worse overall survival than did those with a low EMT index (

BMI-1 Expression Heterogeneity in Endometriosis-Related and Non-Endometriotic Ovarian Carcinoma.

BMI-1 is a key component of stem cells, which are essential for normal organ development and cell phenotype maintenance. BMI-1 expression is deregulated in cancer, resulting in the alteration of chromatin and gene transcription repression. The cellular signaling pathway that governs BMI-1 action in the ovarian carcinogenesis sequences is incompletely deciphered. In this study, we set out to analyze the immunohistochemical (IHC) BMI-1 expression in two different groups: endometriosis-related ovarian carcinoma (EOC) and non-endometriotic ovarian carcinoma (NEOC), aiming to identify the differences in its tissue profile. METHODS: BMI-1 IHC expression has been individually quantified in epithelial and in stromal components by using adapted scores systems. Statistical analysis was performed to analyze the relationship between BMI-1 epithelial and stromal profile in each group and between groups and its correlation with classical clinicopathological characteristics. RESULTS: BMI-1 expression in epithelial tumor cells was mostly low or negative in the EOC group, and predominantly positive in the NEOC group. Moreover, the stromal BMI-1 expression was variable in the EOC group, whereas in the NEOC group, stromal BMI-1 expression was mainly strong. We noted statistically significant differences between the epithelial and stromal BMI-1 profiles in each group and between the two ovarian carcinoma (OC) groups. CONCLUSIONS: Our study provides solid evidence for a different BMI-1 expression in EOC and NEOC, corresponding to the differences in their etiopathogeny. The reported differences in the BMI-1 expression of EOC and NEOC need to be further validated in a larger and homogenous cohort of study.

Risk of epithelial ovarian cancer Type I and II after hysterectomy, salpingectomy and tubal ligation-A nationwide case-control study.

The proposed different origins and pathways to of the dualistic model of epithelial ovarian cancer (EOC) may affect and alter the potential risk reduction related to hysterectomy, salpingectomy and tubal ligation. The aim of our study was to analyze associations between hysterectomy, salpingectomy or tubal ligation and risk reduction of EOC Type I and II. In this nationwide register-based case-control study, women diagnosed with EOC, Fallopian tube or primary peritoneal cancer between 2008 and 2014 were included. Cases were classified into Type I and II according to histology and predefined criteria. The exposure variables: hysterectomy, salpingectomy and tubal ligation were identified from national registries. Conditional logistic regression analyses were performed to evaluate associations between Type I and II EOC and the exposure variables. Among 4669 registered cases, 4040 were eligible and assessed for subtyping resulting in 1033 Type I and 3007 Type II. Ten controls were randomly assigned to each case from the register of population. In regression analyses, women with previous salpingectomy had a significantly lower risk of EOC Type II (odds ratio [OR] 0.62; 95% confidence interval [95%CI] 0.45-0.85) but not Type I (OR 1.16; 95%CI 0.75-1.78). Hysterectomy was associated with a reduced risk of both EOC Type I (OR 0.71; 95%CI 0.52-0.99) and Type II (OR 0.81; 95%CI 0.68-0.96). Similar estimates were obtained for tubal ligation, although without statistical significance. The association between salpingectomy and reduced risk of EOC Type II supports the proposed theory of high-grade serous cancer originating from the tubal fimbriae.

Integrated multi-omics analysis of ovarian cancer using variational autoencoders.

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer.

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK-STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.

PD-L1 expression with QR1 and E1L3N antibodies according to histological ovarian cancer subtype: A series of 232 cases.

Therapeutic strategies for epithelial ovarian cancers are evolving with the advent of immunotherapy, such as PD-L1 inhibitors, with encouraging results. However, little data are available on PDL-1 expression in ovarian cancers. Thus, we set out to determine the PD-L1 expression according to histological subtype. We evaluated the expression of two PD-L1 clones - QR1 and E1L3N - with two scores, one based on the percentage of labeled tumor cells (tumor proportion score, TPS) and the other on labeled immune cells (combined proportion score, CPS) in a consecutive retrospective series of 232 ovarian cancers. PD-L1 expression was more frequent in high grade serous carcinoma (27.5% with E1L3N clone and 41.5% with QR1 clone), grade 3 endometrioid carcinoma (25% with E1L3N clone and 50% with QR1 clone), and clear-cell carcinomas (27.3% with E1L3N clone and 29.6% with QR1 clone) than other histological subtypes with CPS score. Using the CPS score, 17% of cases were labeled with E1L3N vs 28% with QR1. Using the TPS score, 14% of cases were positive to E1L3N vs 17% for QR1. For TPS and CPS, respectively, 77% and 78% of the QR1 cases were concordant with E1L3N for the thresholds of 1%. Overall and progression-free survival between PD-L1 positive and PD-L1 negative patients were not different across all histological types, and each subtype in particular for serous carcinomas expressing PD-L1. Expression of PD-L1 is relatively uncommon in epithelium ovarian tumors. When positive, usually <10% of tumor cells are labeled. QR1 clone and CPS appear the best tools to evaluate PD-L1 expression.

Association between PD-L1 expression and 18F-FDG uptake in ovarian cancer.

OBJECTIVE: Immunotherapy for programmed cell death 1 (PD-1) and its ligand, PD-L1, has been considered an effective treatment for ovarian cancer. 18F-labeled fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is a widely used noninvasive imaging tool for diagnosing several cancers. In this study, we investigated the association between PD-L1 expression and the maximum standardized uptake value (SUVmax) using 18F-FDG PET/CT. METHODS: We retrospectively analyzed clinical data of patients with ovarian cancer who underwent 18F-FDG PET/CT. Patients were categorized into two groups according to PD-L1 expression results. The relationship between clinicopathological characteristics of patients with ovarian cancer and PD-L1 expression was examined. RESULTS: SUVmax was significantly higher in PD-L1-positive tumors than in PD-L1-negative tumors (16.1 ± 5.2 and 12.7 ± 7.0, respectively; p = 0.026). There were no significant differences in age, histologic type, and tumor grade between the PD-L1-negative and PD-L1-positive groups. The receiver operating characteristic curve analysis demonstrated that the highest accuracy (61.8%) for predicting PD-L1 expression was obtained with an SUVmax cutoff value of 10.5. CONCLUSION: There was a significant correlation between 18F-FDG uptake and PD-L1 expression, suggesting a role of 18F-FDG PET/CT in selecting ovarian cancer candidates for anti-PD-L1 antibody therapy.

Adenylate kinase 7 is a prognostic indicator of overall survival in ovarian cancer.

ABSTRACT: Ovarian cancer (OC), a common malignant heterogeneous gynecological tumor, is the primary cause of cancer-related death in women worldwide. Adenylate kinase (AK) 7 belongs to the adenylate kinase (AK) family and is a cytosolic isoform of AK. Recent studies have demonstrated that AK7 is expressed in several human diseases, including cancer. However, there is a scarcity of reports on the relationship between AK7 and OC. Here, we compared the expression of AK7 in normal and cancerous ovarian tissues from The Cancer Genome Atlas database and used the c2 test to assess the correlation between AK7 levels and the clinical symptoms of OC. Finally, the prognostic significance of AK7 in OC was determined using the Kaplan-Meier analyses and Cox regression and performed gene set enrichment analysis to detect any relevant signaling pathways. We found that AK7 levels were substantially downregulated in OC than that in normal ovarian tissues (P < .001). Low AK7 levels were related to the patients' age (P = .0093) in OC. The median overall survival (OS) of patients with low AK7-expressing OC was shorter than patients with high AK7-expressing OC (P = .019). The Cox regression analysis (multivariate) identified low AK7 levels were independently related to the prognosis of OC (HR 1.34; P = .048). Our study demonstrated that the downregulated levels of AK7 could serve as an independent prognostic indicator for the OS in OC. Additionally, gene set enrichment analysis revealed that EMT, apical junction, TGF-b signaling, UV response, and myogenesis were associated in the low AK7 expression phenotype (NOM P < .05).

Correlation of World Health Organization 2010 Classification for Gastroenteropancreatic Neuroendocrine Neoplasms with the Prognosis of Ovarian Neuroendocrine Neoplasms: Kansai Clinical Oncology Group-Protocol Review Committee/Intergroup Study.

BACKGROUND: In 2014, the World Health Organization (WHO) released a classification system introducing neuroendocrine neoplasms (NENs) of the female reproductive tract, excluding the ovaries. This study aimed to evaluate whether retrospective adaption of the gastroenteropancreatic (GEP)-NEN classification is feasible for ovarian NENs (O-NENs) and correlates with prognosis. METHODS: Sixty-eight patients diagnosed with carcinoid, small cell carcinoma (pulmonary type), paraganglioma, non-small/large cell neuroendocrine carcinoma (NEC), mixed NEC, or undifferentiated carcinomas at 20 institutions in Japan were included in this retrospective cross-sectional study. We identified O-NENs through central pathological review using a common slide set, followed by reclassification according to WHO 2010 guidelines for GEP-NENs. A proportional hazards model was used to assess the association of prognostic factors (age, stage, performance status, histology, and residual disease) with overall survival (OS) and progression-free survival (PFS). RESULTS: Of the 68 enrolled patients, 48 were eligible for analysis. All carcinoids (n = 32) were reclassified as NET G1/G2, whereas 14 of 16 carcinomas were reclassified as NEC/mixed adeno-NEC (MANEC) (Fisher's exact test; p < 0.01). The OS/PFS was 49.0/42.5 months and 6.5/3.9 months for NET G1/G2 and NEC/MANEC, respectively. Histology revealed that NEC/MANEC was associated with increased risk of death (HR = 48.0; 95% CI, 3.93-586; p < 0.01) and disease progression (HR = 51.6; 95% CI, 5.54-480; p < 0.01). CONCLUSION: Retrospective adaption of GEP-NEN classification to O-NENs is feasible and correlates well with the prognosis of O-NENs. This classification could be introduced for ovarian tumors.

Classification of serous ovarian carcinoma based on immunogenomic profiling.

Treatment of serous ovarian cancer (SOC) remains a clinical challenge. Classification of SOC based on immunogenomic profiling is important for establishing immunotherapy strategies. We extracted RNA-seq data of SOC from TCGA-OV. The samples were ultimately classified into high immune (Immunity_H) group and low immune (Immunity_L) group based on the immunogenomic profiling of 29 immune signatures by using unsupervised machine learning methods and modified by multifaceted characterization of immune response. High immune group showed the lower tumor purity and higher anti-tumor immune activity, and the higher expressions of PDCD1, CD274 and CTLA4. Furthermore, the overall survival time and the progression-free interval were significantly longer in high-immun group. The differentially expressed genes were mainly enriched in some immune response related functional terms and PI3K-AKT signaling pathway. According to ImmuCellAI, the abundance of various T cell subtypes in high immune group were significantly higher than those in low immune group. This novel immunotyping shows promise for prognostic and immunotherapeutic stratification in SOC patients.

Assessing the predictive response of a simple and sensitive blood-based biomarker between estrogen-negative solid tumors.

PURPOSE: We investigated Nw-hydroxy l-Arginine (NOHA) predictive response in serous ovarian carcinoma based on estrogen-hormone receptor expression status; and assessed the distinctive NOHA response between estrogen-receptor-negative (ER-) tumor subtypes of ovarian and breast cancer. MATERIALS/METHODS: Three-dimensional (3D) spheroids models of ER- and estrogen-receptor-positive (ER+) from breast and ovarian tumor, cultured for 9 weeks, were assayed for cellular levels of inducible nitric oxide synthase (NOS2), nitric oxide (as total nitrite) and l-Arginine, and compared to NOHA in culture medium. Statistical difference was set at p < 0.01. RESULTS: Nine-week in vitro studies showed a progressive NOHA reduction in culture medium by at least 0.4-0.8 fold, and 0.65-0.92 fold only in the ER- breast tumor and ER- ovarian tumor 3D spheroids, respectively; with increases in cellular NOS2 and nitric-oxide levels, by at least 1.0-2.45 fold in both ER- tumor subtype 3D spheroids (p < 0.01; n = 6). Within ER- subtypes, medium NOHA decreased by ≥ 38.9% in ovarian cancer over breast cancer 3D-spheroids, with cellular increases in NOS2 (by ≥ 17.4%), and nitric oxide (by ≥ 18.8%). Cellular l-Arginine to medium NOHA ratio was higher, and by at least 6.5-22.5 fold in ER- breast tumor 3D-spheroids, and at least 10-70 fold in ER- ovarian tumor 3D spheroids, than in ER+ and control conditions; and was ≥48% higher in ER- ovarian cancer than in ER- breast cancer 3D-spheroids. CONCLUSIONS: The present study shows NOHA as a sensitive and selective indicator differentiating and distinguishing ER- subtypes based on the tumor grade.