Artificial intelligence-based histopathological subtyping of high-grade serous ovarian cancer.

Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of the classification depended on the assessor's experience. This study aimed to develop a universal algorithm for HGSC-subtype classification using deep learning techniques. An artificial intelligence (AI)-based classification algorithm, which replicates the consensus diagnosis of pathologists, was formulated to analyze the morphological patterns and tumor-infiltrating lymphocyte counts for each tile extracted from whole slide images of ovarian HGSC available in The Cancer Genome Atlas (TCGA) dataset. The accuracy of the algorithm was determined using the validation set from the Japanese Gynecologic Oncology Group 3022A1 (JGOG3022A1) and Kindai and Kyoto University (Kindai/Kyoto) cohorts. The algorithm classified the four HGSC-subtypes with mean accuracies of 0.933, 0.910, and 0.862 for the TCGA, JGOG3022A1, and Kindai/Kyoto cohorts, respectively. To compare Mesenchymal Transition (MT) with non-MT groups, overall survival analysis was performed in the TCGA dataset. The AI-based prediction of HGSC-subtype classification in TCGA cases showed that the MT group had a worse prognosis than the non-MT group (p = 0.017). Furthermore, Cox proportional hazard regression analysis identified AI-based MT subtype classification prediction as a contributing factor along with residual disease after surgery, stage, and age. In conclusion, a robust AI-based HGSC-subtype classification algorithm was established using virtual slides of ovarian HGSC.

Characterization of immortalized ovarian epithelial cells with BRCA1/2 mutation.

We aimed to elucidate the mechanism underlying carcinogenesis by comparing normal and BRCA1/2-mutated ovarian epithelial cells established via Sendai virus-based immortalization. Ovarian epithelial cells (normal epithelium: Ovn; with germline BRCA1 mutation: OvBRCA1; with germline BRCA2 mutation: OvBRCA2) were infected with Sendai virus vectors carrying three immortalization genes (Bmi-1, hTERT, and SV40T). The immunoreactivity to anti-epithelial cellular adhesion molecule (EpCAM) antibodies in each cell line and cells after 25 passages was confirmed using flow cytometry. Chromosomes were identified and karyotyped to detect numerical and structural abnormalities. Total RNA extracted from the cells was subjected to human transcriptome sequencing. Highly expressed genes in each cell line were confirmed using real-time polymerase chain reaction. Immortalization techniques allowed 25 or more passages of Ovn, OvBRCA1, and OvBRCA2 cells. No anti-EpCAM antibody reactions were observed in primary cultures or after long-term passages of each cell line. Structural abnormalities in the chromosomes were observed in each cell line; however, the abnormal chromosomes were successfully separated from the normal structures via cloning. Only normal cells from each cell line were cloned. MMP1, CCL2, and PAPPA were more predominantly expressed in OvBRCA1 and OvBRCA2 cells than in Ovn cells. Immortalized ovarian cells derived from patients with germline BRCA1 or BRCA2 mutations showed substantially higher MMP1 expression than normal ovarian cells. However, the findings need to be validated in the future.

Static magnetic field reduces cisplatin resistance via increasing apoptosis pathways and genotoxicity in cancer cell lines.

Cisplatin is a chemotherapy drug widely used in cancer treatment. Alongside its clinical benefits, however, it may inflict intolerable toxicity and other adverse effects on healthy tissues. Due to the limitation of administering a high dose of cisplatin as well as cancer drug resistance, it is necessary to utilize new methods optimizing treatment modalities through both higher therapeutic efficacy and reduced administered doses of radiation and drugs. In this study, sensitive (A2780) and resistant (A2780CP) ovarian carcinoma cells underwent treatment with cisplatin + static magnetic field (SMF). First, the levels of genotoxicity after treatment were evaluated by Comet assay. Then, cell cycle analysis and apoptosis assay were conducted by a flow cytometer. Lastly, the expression levels of genes involved in apoptosis and cellular drug uptake were investigated by PCR. After treating different groups of cells for 24, 48, and 96 h, the co-treatment of SMF and cisplatin as a combination managed to increase the amount of DNA damage in both sensitive and resistant cell lines. A considerable increase in mortality of cells was also observed mostly in the form of apoptosis, which was caused by inhibition of the cell cycle. The combination also increased the expression levels of apoptotic genes, namely P53 and P21; however, it did not have much effect on the expression levels of BCL2. Besides, the levels of CTR1 gene expression increased significantly in the groups receiving the aforementioned combination. Our study suggests that the combination of cisplatin + SMF might have clinical potential which needs further investigations through future studies.

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

OBJECTIVE: To compare the capability of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) histogram analysis in epithelial ovarian tumor categorization. METHODS: We retrospectively recruited 52 patients with pathologically proven ovarian serous epithelial cancer from our institution. ADC histogram analysis was performed using FeAture Explorer software after outlining the whole lesion area on the ADC map. The ADC histogram parameter difference between subgroups was compared; the correlation between the quantitative parameters on MRI and Ki-67 expression was calculated for both groups. RESULTS: The repeatability of ADC measurements across the two methods was good; the area method (ADCarea) had better performance in repeatability than the ROI method (ADCroi). The ADCroi, ADCarea, Ktrans, and Kep values significantly differed between the groups (P < 0.05). The histogram parameters (percent10, entropy, minimum, range and variance) and DCE parameter (Ktrans) were strongly correlated with Ki-67 expression (P = 0.000), while the conventional ADC measurements were not significantly correlated with Ki-67 expression (P > 0.05). Overall, Ktrans had the best diagnostic performance for discriminating type I with type II ovarian cancers (AUC = 0.826). CONCLUSION: In the present study, both diffusion-weighted imaging (DWI) and DCE MRI could help classify ovarian cancer patients with high accuracy. ADC histogram analysis could accurately reflect the proliferative capability of tumor cells to some extent.

Endometrioid Adenocarcinoma within an Endometriotic Cyst in the Mesosalpinx: A Case Report and Review of the Literature.

Endometriosis-related neoplasms may arise within endometriotic cysts of organs of the female genital tract or other organ systems. Herein, we present a case of endometrioid adenocarcinoma arising within an endometriotic cyst in the mesosalpinx. A 27-year-old single female presented to the clinic with a history of right hypochondrial pain. Pelvic MRI revealed a bulky uterus with three intramural uterine fibroids and a complex cyst in the mesosalpinx. The excised cyst showed predominantly confluent growth of relatively well-defined glands resembling proliferative-phase endometrium in a background of endometriosis. The tumor was diagnosed as endometrioid adenocarcinoma within an endometriotic cyst, pathologic stage pT1a. She was referred to the oncology team, where a positron emission tomography scan showed unremarkable results. Although these neoplasms have been reported in various locations within the female genital tract, endometrioid adenocarcinoma arising from endometriosis in the mesosalpinx is seldom reported.

A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence.

PURPOSE: Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity. METHODS: Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s). RESULTS: The average recorded accuracy of the proposed automated classification system was equal to 0.86. CONCLUSION: The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging.

OBJECTIVE: We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization. METHODS: Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis. RESULTS: After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type. CONCLUSION: We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Histogram Analysis of Apparent Diffusion Coefficient on Diffusion Weighted Magnetic Resonance Imaging in Differentiation between Low and High Grade Serous Ovarian Cancer.

BACKGROUND: Ovarian cancer is a leading cause of death in gynecological malignancies. Being the most common subtype in OEC, ovarian serous cancer also includes two subtypes: low grade serous ovarian cancer (LGSC) and high grade serous ovarian cancer (HGSC) (1). PURPOSE: The study aims to assess the capability of apparent diffusion coefficient (ADC) histogram analysis and conventional measurements on magnetic resonance imaging (MRI) in differentiating between LGSC and HGSC. METHODS: We retrospectively recruited 38 patients with pathologically proven ovarian serous epithelial cancer. The mean ADC value was measured by one technician using two methods on post-processed workstation. The ADC value and histogram parameter difference between LGSC and HGSC groups were compared. The correlation between the ADC value and the Ki-67 expression was calculated across both groups. RESULTS: The repeatability of ADC measurements across two methods was good; the ROI method (ADC-roi) had better performance repeatability than the area method (ADC-area). The value of ADC-mean , ADC-min, ADC-max, and ADC-area significantly differed between both groups (p < 0.001). The value of ADC-area correlated inversely with ki-67 expression in the whole group (Pearson coefficient = -0.382, p = 0.02). The 3D computerized-diagnostic model had the best discriminative performance in determining HGSC than 2D and conventional ADC measurements. The 3D model yielded a sensitivity of 100%, a specificity of 95.45%, and an accuracy of 97.73%. CONCLUSION: In the present study, the 3D ADC histogram model help differentiate HGSC from LGSC with a better performance than conventional ADC measurements.

The role of urine and serum biomarkers in the early detection of ovarian epithelial tumours.

Ovarian cancer (OC) is one of the leading causes of gynaecological cancer mortality in women worldwide. If detected at an early stage (I, II), OC has a 90% 5-year survival rate; nevertheless, symptoms are often hidden, leading to late-stage (III, IV) diagnosis and a poor prognosis. The current diagnostic procedures, such as a pelvic exam, transvaginal ultrasound, CA-125 blood tests, serum HE4 tests and multivariate index assays (MIA), are insufficient. Sadly, surgery is frequently required to confirm a positive diagnosis. Therefore, there has been an increased interest in different biomarkers using a non-invasive test as a tool for the earlier diagnosis of OC to resolve the need for precise and non-invasive diagnostic methods. This review article aims to investigate how biomarkers influence early OC detection and to emphasise the role of using a combination of serum biomarkers panel rather than a single biomarker. In addition, this review provides insights into the current serum biomarkers, urine biomarkers and other emerging biomarkers in the early detection of OC for better specificity and sensitivity and to improve the overall survival (OS) rate.

Pentraxin 3 is a diagnostic and prognostic marker for ovarian epithelial cancer patients based on comprehensive bioinformatics and experiments.

BACKGROUND: Ovarian epithelial cancer is one of the leading malignant tumors in gynecology and lacks effective diagnostic and prognostic markers. Our study aims to screen and verify ovarian epithelial cancer biomarkers. METHODS: GSE18520 and GSE26712 were downloaded from the GEO database. The "limma" and "WGCNA" packages were used to explore hub genes. The Kaplan-Meier Plotter database was used for survival analysis of the hub genes. Immunohistochemical analysis was used to identify the expression level of Pentraxin 3 in ovarian epithelial cancer samples. RESULTS: In this study, we integrated and analyzed two datasets, GSE18520 and GSE26712, and a total of 238 differentially expressed genes (DEGs) were screened out. Enrichment analysis showed that these DEGs were related to collagen-containing extracellular matrix and other pathways. Further application of WGCNA (weighted gene coexpression network analysis) identified 15 gene modules, with the purple module showing the highest correlation with ovarian epithelial cancer. Twenty-five genes were shared between the purple module and DEGs, 13 genes were related to the prognosis of ovarian epithelial cancer patients, and the PTX3 gene had the highest hazardous risk (HR) value. We performed immunohistochemical analyses on the 255 Pentraxin-3 (PTX3)-based clinical samples. PTX3 was found to be overexpressed in ovarian epithelial cancer and related to the degree of differentiation. The Cox proportional hazard model indicates that high PTX3 expression is an independent risk factor for the prognosis of ovarian epithelial cancer patients. CONCLUSIONS: In conclusion, through WGCNA and a series of comprehensive bioinformatics analyses, PTX3 was first identified as a novel diagnostic and prognostic biomarker for ovarian epithelial cancer.

An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer.

This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADCmean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.

Recommendations on the pharmacological treatment of epithelial ovarian cancer in Mexico.

Ovarian cancer is the third most frequent gynaecological malignancy worldwide and in Mexico, with a high mortality rate, due to that in many cases its diagnosis is made in advanced stages. Prognosis is important for determining the subtype and the degree of evolution. During lasts years, the management of ovarian cancer has undergone an important evolution with the incorporation of new therapeutic options, which in turn represent an increase in the survival of these patients. We present recommendations for the management of ovarian cancer developed by an expert panel Mexican based on available evidence so far and the characteristics of health care in the country.

El cáncer de ovario es la tercera neoplasia maligna ginecológica más frecuente globalmente y también en México, con una elevada tasa de mortalidad debido a que en muchos casos su diagnóstico se realiza en etapas avanzadas. Para establecer su pronóstico es importante la determinación del subtipo y del grado de evolución. En los últimos años, el manejo del cáncer de ovario ha sufrido una importante evolución con la incorporación de nuevas opciones terapéuticas, que a su vez representan un incremento en la supervivencia de estas pacientes. Se presentan las recomendaciones para el manejo del cáncer de ovario elaboradas por un panel de expertos mexicanos basadas en la evidencia disponible hasta el momento y en las características de la atención sanitaria del país.

Recomendaciones sobre el tratamiento farmacológico del cáncer de ovario epitelial en México / Recommendations on the pharmacological treatment of epithelial ovarian cancer in Mexico

Resumen El cáncer de ovario es la tercera neoplasia maligna ginecológica más frecuente globalmente y también en México, con una elevada tasa de mortalidad debido a que en muchos casos su diagnóstico se realiza en etapas avanzadas. Para establecer su pronóstico es importante la determinación del subtipo y del grado de evolución. En los últimos años, el manejo del cáncer de ovario ha sufrido una importante evolución con la incorporación de nuevas opciones terapéuticas, que a su vez representan un incremento en la supervivencia de estas pacientes. Se presentan las recomendaciones para el manejo del cáncer de ovario elaboradas por un panel de expertos mexicanos basadas en la evidencia disponible hasta el momento y en las características de la atención sanitaria del país.

Abstract Ovarian cancer is the third most frequent gynaecological malignancy worldwide and in Mexico, with a high mortality rate, due to that in many cases its diagnosis is made in advanced stages. Prognosis is important for determining the subtype and the degree of evolution. During lasts years, the management of ovarian cancer has undergone an important evolution with the incorporation of new therapeutic options, which in turn represent an increase in the survival of these patients. We present recommendations for the management of ovarian cancer developed by an expert panel Mexican based on available evidence so far and the characteristics of health care in the country.

LRRC17 Is Linked to Prognosis of Ovarian Cancer Through a p53-dependent Anti-apoptotic Function.

BACKGROUND/AIM: Since pathways involving LRRC17 are related to the survival of patients with various cancers, we analyzed LRRC17 as a prognostic gene in serous ovarian cancer. MATERIALS AND METHODS: Data were collected from Gene Expression Omnibus (GSE9891, GSE13876, and GSE26712) and The Cancer Genome Atlas (TCGA). We performed survival analyses using C statistics, area under the curve, survival plot with optimal cutoff level, and cox proportional regression. Zebrafish embryos were used as an in vivo model. RESULTS: The prognosis of patients with high LRRC17 expression was poorer than that of patients with low LRRC17 expression. Multivariate regression analysis showed that LRRC17 expression, age, and stage were independently related with survival. Knockdown of lrrc17 reduced survival rate and delayed development in zebrafish embryos. We also found that lrrc17 is important for cell viability by protecting from p53-dependent apoptosis. CONCLUSION: LRRC17 could be a prognostic gene in ovarian cancer as it regulates cancer cell viability through the p53 pathway.

How far should we go in optimal cytoreductive surgery for ovarian cancer?

The treatment of ovarian cancer should be appropriate, since clinical and surgical decisions may affect the prognosis; the surgery must be performed by an expert oncological surgeon or gynecological oncologist, it's fundamental roles are cancer staging and cytoreduction. The concept of staging surgery in early stages has its justification in the fact that up to 11% of "early ovarian cancers" will have metastasis in different sites of the peritoneal cavity at the time of diagnosis. In advanced stages of epithelial ovarian cancer, the goal is the complete cytoreduction of all visible macroscopic disease, since this variable is the most strongly associated with increased overall survival and disease-free period. The ideal time for cytoreductive surgery in relation to chemotherapy (before or after) is still under debate. In 2010 a randomized trial (EORTC) was published, comparing 310 patients initially operated (followed by adjuvant chemotherapy) versus 322 patients initially treated with neoadjuvant chemotherapy (followed by cytoreductive surgery); no significant differences in overall survival between groups were found. Another important factor playing a role in survival and in the probability of surgical cytoreductive success is tumor biology; there has been described a clear difference between serous and mucinous tumors, but some groups advocate that maximal surgical effort in mucinous tumors may compensate morbidity with an increase in survival. The extension of resection in cytoreduction is still controversial; some authors have confirmed that the most important factor is the residual disease and that radical surgery is superior to non-radical surgery in terms of overall survival. The need and extent of lymphadenectomy in advanced cancer will be treated in another chapter of this issue. Undoubtedly, an important factor is to perform procedures in specialized centers.

Overexpression of DUSP6 enhances chemotherapy-resistance of ovarian epithelial cancer by regulating the ERK signaling pathway.

Objective: DUSP6 is a negative regulator of the ERK signaling pathway and plays an important role in chemotherapy-resistance. Previously we showed that DUSP6 is overexpressed in ovarian cancer side population (SP) cells that possess cancer stem cell-like properties and are quiescent and chemotherapy-resistant. Here, we explore the effects of DUSP6 on chemotherapy-resistance by examining its regulation of the ERK signaling pathway and G0/G1 cell cycle arrest. Methods: mRNA and protein expression of DUSP6 and G0/G1 cell cycle checkpoint regulating proteins (CyclinD1, CyclinD3 and CyclinE2) was evaluated among ovarian cancer cell lines and tissue samples. Ovarian cancer cells were transiently transfected to overexpress DUSP6. After treatment with cisplatin, cell viability was measured by the MTS assay at 48 hours and the half maximal inhibitory concentration (IC50) for each cell line was calculated. Subcellular localization and cell cycle analysis were determined by using immunofluorescence and FACS, respectively. Results: SKOV3 and OVCAR8 SP cells were shown to express higher levels of DUSP6 and lower levels of CyclinD3 compared with non-SP (NSP) cells (P<0.001). Among 39 ovarian cancer tissue samples, expression of DUSP6 in the chemotherapy-resistant group (12 samples) was higher than in the chemotherapy-sensitive group (27 samples) (P<0.05). While a lower level of expression of CyclinD3 was seen in the chemotherapy-resistant group, it was not statistically different from the chemotherapy-sensitive group. HO8910 cells where shown to have higher IC50 to cisplatin than SKOV3 or OVCAR8 cells, and this correlated with higher levels of DUSP6 expression. Overexpression of DUSP6 in SKOV3 cells led to an increase in cisplatin IC50 values (P<0.05), and also markedly reduced the expression levels of phospho-ERK1/2 and CyclinD3 and to the predominance of cells in the G0/G1 phase. Conclusion: Our findings reveal an enhancement of chemotherapy-resistance and a predominance of cells in G1 cell cycle arrest in DUSP6-overexpressing ovarian cancer cells. This suggests that overexpression of DUSP6 promotes chemotherapy-resistance through the negative regulation of the ERK signaling pathway, increasing the G0/G1 phase ratio among ovarian cancer cells, and leading to cellular quiescence.

Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging.

PURPOSE: To investigate the value of CT and MR imaging features in differentiating borderline ovarian tumor (BOT) from type I ovarian epithelial cancer (OEC), which could be significant for suitable clinical treatment and assessment of the prognosis of the patient. METHODS: Thirty-three patients with BOTs and 35 patients with type I OECs proven by pathology were retrospectively evaluated. The clinico-pathological information (age, premenopausal status, CA-125, and Ki-67) and imaging characteristics were compared between two groups of ovarian tumors. The diagnostic performance of the imaging features was evaluated using receiver operating characteristic analysis. The best predictor variables for type I EOCs were recognized via multivariate analyses. RESULTS: BOTs are more likely to involve younger patients and frequently show lower CA-125 values and lower proliferation indices (Ki-67 < 15%) than type I OECs. Compared with type I OECs, BOTs were more often purely cystic (15/33, 45.45% vs. 1/35, 2.86%; p < 0.001) and displayed less frequent mural nodules (16/33, 48.48% vs. 28/35, 80.00%; p = 0.007), less frequently unclear margin (3/33, 9.09% vs. 11/35, 31.43%; p = 0.023), smaller solid portion (0.56 ± 2.66 vs. 4.51 ± 3.88; p < 0.001), and thinner walls (0.3 ± 0.17 vs. 0.55 ± 0.24; p < 0.001). The maximum wall thickness presented the largest area under the curve (AUC, 0.848). Multivariate analysis revealed that the solid portion size (OR 10.822, p = 0.002) and maximum wall thickness (OR 9.130, p = 0.001) were independent indicators for the differential diagnosis between the two groups of lesions. CONCLUSION: The solid portion size and maximum wall thickness significantly influenced the classification of the two groups of ovarian tumors.

Trametinib response in heavily pretreated high-grade ovarian cancer: One step towards precision medicine.

Traditional treatment failure in recurrent ovarian cancer remains a challenge for clinicians. Tumor genetic testing is a promising tool which has been proved able to identify sensitivity profiles in patients affected by cancers. This may be helpful in choosing targeted systemic treatments, aiming to overcome histology boundaries and to avoid unnecessary toxicity. We describe the case of a patient affected by recurrent high-grade serous ovarian cancer responsive to MEK-inhibitors, who had undergone multiple lines of therapy. To our knowledge, this is the first reported case of recurrent high-grade ovarian cancer showing remarkable clinical, radiologic and biochemical response to trametinib. This report suggests that trametinib could be effective in high-grade serous ovarian cancer, although most of promising scientific data on this molecule have focused on low-grade ovarian cancer. Molecular profiling has gradually become part of care for patients affected by recurrent ovarian cancer, however further randomized studies are needed to prove its efficacy in everyday clinical practice.

Pharmacokinetics and toxicity of carboplatin used for hyperthermic intraperitoneal chemotherapy (HIPEC) in treatment of epithelial ovarian cancer.

OBJECTIVES: Carboplatin is frequently used in various doses for hyperthermic intraperitoneal chemotherapy (HIPEC) in the treatment of epithelial ovarian cancer (EOC) although its pharmacokinetics, including focus on the perfusion time, has not been evaluated when used in modern era cytoreductive surgery (CRS). The aim was to evaluate the pharmacokinetics and hematological toxicity of carboplatin used for HIPEC with a perfusion time of 90 min. METHODS: Fifteen patients with stage III-IV primary EOC received CRS and 90 min of HIPEC with carboplatin at dose 800 mg/m2. For the pharmacokinetic analysis, perfusate and blood samples were obtained during HIPEC and up to 48 h after HIPEC (blood only). Hematological toxicity within 30 days was graded according to Common Terminology Criteria for Adverse Events. Severe toxicity (grades 3-5) is reported. RESULTS: Mean maximum concentration of carboplatin was 12 times higher in perfusate than plasma (mean CmaxPF=348 µg/mL (range: 279-595 µg/mL) versus mean CmaxPL=29 µg/mL (range: 21-39 µg/mL)). Mean terminal half-life of carboplatin in perfusate was 104 min (range: 63-190 min) and mean intraperitoneal-to-plasma area under the concentration-time curve (AUC) ratio was 12.3 (range: 7.4-17.2). Two patients (13%) had grade 3 neutropenia within 30 days. No grade 4-5 hematological toxicities were identified. CONCLUSIONS: Carboplatin has a favorable pharmacokinetic profile for 90 min HIPEC administration, and the hematological toxicity was acceptable at dose 800 mg/m2. Large interindividual differences were found in the pharmacokinetic parameters, making risk of systemic exposure difficult to predict.

The power of the Risk of Ovarian Malignancy Algorithm considering menopausal status: a comparison with CA 125 and HE4.

OBJECTIVE: To identify the power of tumor markers for predicting ovarian cancer according to menopausal status. METHODS: The medical records of 876 women with ovarian cysts were retrospectively reviewed. Cancer antigen 125 (CA 125), human epididymis protein 4 (HE4), and Risk of Ovarian Malignancy Algorithm (ROMA) were analyzed. Sensitivity, specificity, and the receiver operating characteristic (ROC) curve analyses of these tumor markers were evaluated. RESULTS: The sensitivity of ROMA was 66.7% and the specificity was 86.8% to detect ovarian malignancy. The patients were divided into 2 groups according to menopausal status: premenopause (n=532, 60.7%) and postmenopause (n=344, 39.3%). For diagnostic accuracy, ROMA was lower than HE4 in premenopausal women (82.7% vs. 91.4%) and lower than CA 125 in postmenopausal women (86.9% vs. 88.7%). The ROC curve analysis revealed that the power of ROMA was not significantly better than that of HE4 in premenopausal women (area under the curve [AUC], 0.731 vs. 0.732, p=0.832), and it was also not significantly better than that of CA 125 in postmenopausal women (AUC, 0.871 vs. 0.888, p=0.440). CONCLUSION: The discrimination power of tumor markers for ovarian cancer was different according to menopausal status. In predicting ovarian malignancy, ROMA was neither superior to HE4 in premenopausal women nor superior to CA 125 in postmenopausal women.