Molecular Studies on Pathogenesis, Prognostic Factors, and New Treatment Options for Ovarian Granulosa Cell Tumors

Granulosa cell tumor (GCT) is the second most common subtype of ovarian cancer, representing 5% of all ovarian malignancies. GCTs are characterized by an indolent course of disease, with a 5-year survival rate of over 90%. However, recurrences occur in 20-30% of patients, also those with early-stage disease, leading to increased mortality. The pathogenesis and factors affecting prognosis of GCTs are largely unknown. Further, the treatment of advanced or recurrent GCT is difficult underscoring the need for biologically targeted treatments for aggressive GCTs. The molecular mechanisms leading to GCT formation are likely to include regulators of granulosa cell proliferation and apoptosis. We studied granulosa cell regulators and tumor angiogenesis in GCTs utilizing tumor tissue and serum samples, and cell culture assays. The objectives of this study were to find new molecular prognostic factors and to identify targets for future biological treatments for GCT. GCTs express Anti-Müllerian Hormone (AMH) and knockout mouse models suggest that AMH acts as a growth inhibitor in GCT pathogenesis. We found that GCTs expressed AMH receptors, with the AMH type II receptor (AMHRII) being characteristic of GCTs. AMH expression was decreased in large GCTs, and recombinant AMH inhibited growth of GCT cells in vitro. The results support the premise that AMH acts as a growth inhibitor in GCTs, and AMH and AMHRII emerge as targets for treatment of GCT. Vascular Endothelial Growth Factor-A (VEGF) is a key factor in tumor angiogenesis that has been successfully targeted in cancer treatment. We found that VEGF and its functional receptor VEGFR-2 are highly expressed in GCTs; VEGFR-2 was also expressed in the active, phosphorylated form. GCTs produced significant amounts of VEGF that could also be detected in the serum of GCT patients. In cell culture assays, the inhibition of VEGF by soluble anti-VEGF antibody (bevacizumab) inhibited growth and induced apoptosis of GCT cells. These results indicate a pro-tumorigenic role of VEGF in GCTs and encourage targeting VEGF and VEGFR-2 in the treatment of GCTs. Transcription factor GATA4 associates with GCT pathogenesis and prognosis. We found that high tumor GATA4 expression was an independent prognostic factor for GCT recurrence. GATA4 was also prognostic of shorter disease-specific survival along with higher tumor stage (II-III) and nuclear atypia. These results suggest that GATA4 can be used as a new prognostic marker for GCT. Human Epidermal Growth Factor Receptor 2 (HER2) is a known oncogene and a target for treatment in breast and gastric cancer. We found that high expression of HER2 delineated an aggressive subset of GCTs and HER2 is thus a potential target for treatment also in this disease.Munasarjasyöpä on toiseksi yleisin gynekologinen syöpä, joka hoitojen kehityksestä huolimatta aiheuttaa merkittävää kuolleisuutta. Granuloosasolukasvain (GSK) on munasarjasyövän alatyyppi, jonka ennuste on useimmiten hyvä. Kasvain kuitenkin uusii 20-30%:lla potilaista, ja kuolleisuus uusiutuneeseen tautiin on korkea. GSK:n tautimekanismit ja ennustetekijät ovat pääosin tuntemattomia, eikä sen hoitoon ole käytössä kohdennettuja biologisia hoitomuotoja. GSK:n ajatellaan saavan alkunsa munasarjan granuloosasoluja säätelevien tekijöiden häiriöistä. Tässä tutkimuksessa selvitettiin granuloosasolujen kasvutekijöiden ja verisuonikasvutekijän roolia GSK:n syntymekanismeissa. Tutkimuksen tavoitteena oli selvittää uusia kohdemolekyylejä GSK potilaiden hoidon kehittämiseen sekä osoittaa uusia ennustetekijöitä potilaiden ennusteen arvioimiseen. Työssä käytettiin kudos- ja seeruminäytteitä, sekä solumalleja. Anti-Müllerian hormoni (AMH) on tärkeä granuloosasolujen säätelijähormoni. Tutkimustulosten mukaan AMH ilmentyi voimakkaasti pienissä GSK:ssa ja esti lisäksi kasvainsolujen kasvua osoittaen AMH:n toimivan kasvunrajoitetekijänä GSK:ssa. GSK:t ilmensivät vahvasti myös AMH:n reseptori II:ta, joka on mahdollinen kohde uusille syöpähoidoille. Verisuonikasvutekijä VEGF on tärkeä kasvainten verisuonistusta säätelevä tekijä, ja tärkeä kohde jo käytössä oleville syöpähoidoille. Tutkimuksen mukaan GSK:t ilmensivät vahvasti VEGF:ää ja sen reseptoreita. GSK solut tuottivat VEGF:ää ja se oli merkitsevästi koholla myös GSK potilaiden seerumissa. Solutöissä pystyimme aiheuttamaan GSK solujen kuoleman VEGF vasta-aineella (bevasitsumabi). Tulokset viittaavat VEGF:än toimivan kasvaimen kasvua edistävänä tekijänä, ja osoittavat VEGF-kohdennetun hoidon olevan vaihtoehto myös GSK:ten hoidossa. GATA4 on tärkeä granuloosasolujen geeninsäätelijä, joka on tämän tutkimuksen tulosten mukaan uusi itsenäinen ennustetekijä GSK:ssa. Vahvaan GATA4:n ilmentymiseen liittyi nelinkertainen riski taudin uusiutumiselle sekä kohonnut riski myös tautispesifiselle kuolemalle. HER2 on tunnettu syöpägeeni ja munasarjan toiminnan säätelijä, jota vastaan on käytössä kohdennettuja syöpähoitoja. Tutkimustulosten mukaan HER2:n vahva ilmentyminen ennusti GSK:n aggressiivista käyttäytymistä. HER2 on siten mahdollinen kohde biologisille syöpähoidoille myös GSK:ssa. Tässä tutkimuksessa tunnistettiin uusia molekyylejä, jotka osallistuvat GSK:n syntyyn. Näitä molekyylejä voidaan käyttää biologisten hoitojen kehittämiseen GSK potilaille. Tutkimuksessa löytyi myös kaksi uutta ennustetekijää GSK:n uusiutumiselle, joista GATA4:ää voidaan käyttää itsenäisenä tekijänä GSK potilaan ennusteen arvioimisessa

Virtual clinical trials identify effective combination therapies in ovarian cancer

A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.Peer reviewe

Other Primary Malignancies Among Women With Adult-Type Ovarian Granulosa Cell Tumors

Objective: The aim of this study was to determine the incidence of new primary malignancies after adult-type granulosa cell tumor (AGCT) and the incidence of AGCT after breast and uterine cancer using nationwide population-based registry data. Methods: We used the Finnish Cancer Registry to identify all patients diagnosed with AGCT in 1968 to 2013 (n = 986). The number of subsequent primary malignancies among women with AGCT and the number of AGCTs in women with previous breast or uterine cancer were compared with the expected number of cases and expressed as standardized incidence ratios (SIRs). Results: There were 122 cases of subsequent cancers diagnosed at least 6 months after the primary diagnosis of AGCT (SIR, 1.09; 95% confidence interval [CI], 0.91-1.3). In particular, the observed number of cancers of the soft tissue (SIR, 4.13; 95% CI, 1.33-12.8), thyroid (SIR, 3.42; 95% CI, 1.54-7.62), and leukemia (SIR, 2.67; 95% CI, 0.98-5.82) exceeded the number of expected cases. The SIR for breast cancers after AGCT was 1.26 (95% CI, 0.92-1.73), and the SIR for AGCT after breast cancer was 1.59 (95% CI, 1.04-2.29). The risk for subsequent AGCT was more than 2-fold in breast cancer patients younger than 50 years, and over 15 years after primary diagnosis. Conclusions: There is an increased risk for thyroid and soft tissue cancer as well as leukemia after AGCT, which may be associated with late effects of carcinogenic treatments and possibly shared risk factors. After breast cancer, the risk for AGCT was higher, which may indicate a shared hormonal etiology.Peer reviewe

Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis

Background and AimsPrimary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)-based models. MethodsA K7-AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7-stained PSC liver biopsy specimens (n = 295) were analyzed. A compound endpoint (liver transplantation, liver-related death, and cholangiocarcinoma) was applied in Kaplan-Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. ResultsThe K7-AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7-positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. ConclusionsThe K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7-AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials.Peer reviewe

Chronic cholestasis detection by a novel tool : automated analysis of cytokeratin 7-stained liver specimens

Background The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of 210 liver specimens. We aimed to study the correlation between the AI model's results and disease progression. The cohort of liver biopsies which served as a model of chronic cholestatic liver disease comprised of patients diagnosed with primary sclerosing cholangitis (PSC). Methods In a cohort of patients with PSC identified from the PSC registry of the University Hospital of Helsinki, their K7-stained liver biopsy specimens were scored by a pathologist (human K7 score) and then digitally analyzed for K7-positive hepatocytes (K7%area). The digital analysis was by a K7-AI model created in an Aiforia Technologies cloud platform. For validation, values were human K7 score, stage of disease (Metavir and Nakunuma fibrosis score), and plasma liver enzymes indicating clinical cholestasis, all subjected to correlation analysis. Results The K7-AI model results (K7%area) correlated with the human K7 score (0.896; p < 2.2e(- 16)). In addition, K7%area correlated with stage of PSC (Metavir 0.446; p < 1.849e(- 10) and Nakanuma 0.424; p < 4.23e(- 10)) and with plasma alkaline phosphatase (P-ALP) levels (0.369, p < 5.749e(- 5)). Conclusions The accuracy of the AI-based analysis was comparable to that of the human K7 score. Automated quantitative image analysis correlated with stage of PSC and with P-ALP. Based on the results of the K7-AI model, we recommend K7 staining in the assessment of cholestasis by means of automated methods that provide fast (9.75 s/specimen) quantitative analysis.Peer reviewe

HER2 and GATA4 are new prognostic factors for early-stage ovarian granulosa cell tumor-a long-term follow-up study

Peer reviewe

Characteristics and outcome of recurrence in molecularly defined adult-type ovarian granulosa cell tumors

Objective. Adult-type ovarian granulosa cell tumors (AGCTs) have an unpredictable tendency to relapse. In a carefully validated patient cohort, we evaluated the prognostic factors related to AGCT recurrence. Methods. We identified all patients diagnosed with AGCT during 1956-2014 in Helsinki University Hospital, with a minimum follow-up of one year (n = 240). After a histological review supplemented with FOXL2 (402C G) mutation status analysis, we analyzed the clinical data for association with relapse. Results. The final cohort included 164 (68%) molecularly defined AGCTs (MD-AGCTs). The majority of the women were postmenopausal (63%), and 92% of tumors were stage I. The median follow-up time was 15.5 years. Fifty-two (32%) patients developed tumor recurrence, of whom 55% had successive recurrences. Multiple-site recurrences were common, and nearly half of the recurrences were asymptomatic. The median time to the first relapse was 7.4 years, and 75% of relapses occurred within ten years after primary diagnosis. The median disease-free survival was 11.3 years. Premenopausal status at initial diagnosis, FIGO stage Ic versus la, and tumor rupture associated with relapse. However, tumor rupture was the only independent predictive factor. Of the relapsed patients, 48% died of AGO' in a median time of 153 years. Conclusion. Tumor rupture is the strongest predictive factor for recurrence, and these patients might benefit from a more aggressive initial treatment approach. AGCT requires active follow up for 10 to 15 years after primary diagnosis, since recurrences may develop late, asymptomatically and in multiple anatomical locations. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

DNA-vaurioiden hyödyntäminen munasarjasyövän hoidossa

Vertaisarvioitu.Epiteliaalinen munasarjasyöpä jaetaan histologiansa ja molekulaarisen taustansa perusteella alaryhmiin, jotka eroavat myös kliiniseltä käyttäytymiseltään. Yleisimmän, huonosti erilaistuneen seroosin karsinooman syntymekanismit liittyvät DNA-vaurioita korjaavien homologisen rekombinaation (HR) geenien mutaatioihin. HR-puutoksesta syntyviä DNA-vaurioita voidaan hyödyntää seroosin karsinooman hoidossa käyttämällä polyadenosiinidifosfaattiriboosipolymeraasin (PARP) estäjiä yksinään tai yhdistettynä muihin kohdennettuihin hoitoihin. Mikäli endometrioidissa tai kirkassoluisessa alatyypissä on MMR (kahdentumisvirheiden korjaus, mismatch repair) -geenin mutaatio, ovat nämä syövät herkkiä immuunihoidolle. HR- ja MMR-puutoksista syntyviä DNA-vaurioita voidaan käyttää myös biomarkkereina tunnistamaan potilaita, jotka hyötyvät uusista, kohdennetuista hoidoista ja niiden yhdistelmistä.Peer reviewe

Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses

PURPOSE We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data. We demonstrate the utility of CLOBNET by predicting primary therapy outcomes of patients with high-grade serous ovarian cancer (HGSOC) on the basis of EHR data. MATERIALS AND METHODS CLOBNET is built using open-source software to make data preprocessing, analysis, and model training user friendly. The source code of CLOBNET is available in GitHub. The HGSOC data set was based on a prospective cohort of 208 patients with HGSOC who were treated at Turku University Hospital, Finland, from 2009 to 2019 for whom comprehensive clinical and EHR data were available. RESULTS We trained machine learning (ML) models using clinical data, including a herein developed dissemination score that quantifies the disease burden at the time of diagnosis, to identify patients with progressive disease (PD) or a complete response (CR) on the basis of RECIST (version 1.1). The best performance was achieved with a logistic regression model, which resulted in an area under receiver operating characteristic curve (AUROC) of 0.86, with a specificity of 73% and a sensitivity of 89%, when it classified between patients who experienced PD and CR. CONCLUSION We have developed an open-source computational infrastructure, CLOBNET, that enables effective and rapid analysis of EHR and other clinical data. Our results demonstrate that CLOBNET allows predictions to be made on the basis of EHR data to address clinically relevant questions.Peer reviewe