External validation of Standardized KELIM and platinum-resistant recurrence scores in patients with advanced epithelial ovarian cancer.

BACKGROUND: Neoadjuvant chemotherapy followed by interval debulking surgery is currently a common treatment option for advanced epithelial ovarian cancer (EOC). The Standardized CA-125 ELIMination rate constant K (Std KELIM) and the Platinum Resistant Recurrence (PtRR) Score have been proposed as markers of tumor chemosensitivity. The aim of our study was to validate these tools for predicting platinum sensitivity in a real-world population of patients with advanced EOC treated with neoadjuvant chemotherapy. EXPERIMENTAL DESIGN: All patients with advanced EOC treated with neoadjuvant chemotherapy at the Institut Curie between 2000 and 2015 were included. The Std KELIM was calculated with the CA-125 concentrations during the first 100 days of chemotherapy. The predictive value of Std KELIM and PtRR scores for the risk of subsequent PtRR was assessed using receiver operating characteristic (ROC) curve analysis, logistic regression and calibration curve. Kaplan-Meier survival analysis was performed for the treatment-free interval from platinum (TFIp) therapy and overall survival (OS). RESULTS: Std KELIM data were available for 149 patients. The AUC was 0.67 for PtRR. A low Std KELIM was significantly associated with PtRR (OR = 0.19 (95% CI [0.06, 0.53], p = 0.002)) according to the univariate analysis. The calibration curve of the PtRR showed a slight but significant underestimation (p = 0.02) of the probability of platinum resistance. Favorable Std KELIM (≥ 1) alone and combined with the completeness of surgery were associated with significantly better survival in terms of TFIp and OS. CONCLUSIONS: Std KELIM is an early prognostic marker of chemosensitivity in a real-life setting complementary to surgical status. It could help the clinician in the early management of patients by identifying those with a worse prognosis.

Development of a model to predict the age at breast cancer diagnosis in a global population.

Knowing the mean age at diagnosis of breast cancer (BC) in a country is important for setting up an efficient BC screening program. The aim of this study was to develop and validate a model to predict the mean age at diagnosis of BC at the country level. To develop the model, we used the CI5plus database from the IARC, which contains incidence data for 122 selected populations for a minimum of 15 consecutive years from 1993 to 2012 and data from the World Bank. The standard model was fitted with a generalized linear model with the age of the population, growth domestic product per capita (GDPPC) and fertility rate as fixed effects and continent as a random effect. The model was validated in registries of the Cancer Incidence in Five Continents that are not included in the CI5plus database (1st validation set: 1950-2012) and in the most recently released volume (2nd validation set: 2013-2017). The intercept of the model was 30.9 (27.8-34.1), and the regression coefficients for population age, GDPPC and fertility rate were 0.55 (95% CI: 0.53-0.58, p < 0.001), 0.46 (95% CI: 0.26-0.67, p < 0.001) and 1.62 (95% CI: 1.42-1.88, p < 0.001), respectively. The marginal R2 and conditional R2 were 0.22 and 0.81, respectively, suggesting that 81% percent of the variance in the mean age at diagnosis of BC was explained by the variance in population age, GDPPC and fertility rate through linear relationships. The model was highly accurate, as the correlations between the predicted age from the model and the observed mean age at diagnosis of BC were 0.64 and 0.89, respectively, and the mean relative error percentage errors were 5.2 and 3.1% for the 1st and 2nd validation sets, respectively. We developed a robust model based on population age and continent to predict the mean age at diagnosis of BC in populations. This tool could be used to implement BC screening in countries without prevention programs.

The OVAREX study: Establishment of ex vivo ovarian cancer models to validate innovative therapies and to identify predictive biomarkers.

BACKGROUND: Ovarian cancer is the first cause of death from gynecological malignancies mainly due to development of chemoresistance. Despite the emergence of PARP inhibitors, which have revolutionized the therapeutic management of some of these ovarian cancers, the 5-year overall survival rate remains around 45%. Therefore, it is crucial to develop new therapeutic strategies, to identify predictive biomarkers and to predict the response to treatments. In this context, functional assays based on patient-derived tumor models could constitute helpful and relevant tools for identifying efficient therapies or to guide clinical decision making. METHOD: The OVAREX study is a single-center non-interventional study which aims at investigating the feasibility of establishing in vivo and ex vivo models and testing ex vivo models to predict clinical response of ovarian cancer patients. Patient-Derived Xenografts (PDX) will be established from tumor fragments engrafted subcutaneously into immunocompromised mice. Explants will be generated by slicing tumor tissues and Ascites-Derived Spheroids (ADS) will be isolated following filtration of ascites. Patient-derived tumor organoids (PDTO) will be established after dissociation of tumor tissues or ADS, cell embedding into extracellular matrix and culture in specific medium. Molecular and histological characterizations will be performed to compare tumor of origin and paired models. Response of ex vivo tumor-derived models to conventional chemotherapy and PARP inhibitors will be assessed and compared to results of companion diagnostic test and/or to the patient's response to evaluate their predictive value. DISCUSSION: This clinical study aims at generating PDX and ex vivo models (PDTO, ADS, and explants) from tumors or ascites of ovarian cancer patients who will undergo surgical procedure or paracentesis. We aim at demonstrating the predictive value of ex vivo models for their potential use in routine clinical practice as part of precision medicine, as well as establishing a collection of relevant ovarian cancer models that will be useful for the evaluation of future innovative therapies. TRIAL REGISTRATION: The clinical trial has been validated by local research ethic committee on January 25th 2019 and registered at ClinicalTrials.gov with the identifier NCT03831230 on January 28th 2019, last amendment v4 accepted on July 18, 2023.

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.