Lorenzo D’Ambrosio

Three-dimensional dynamics of mesothelin-targeted CAR.CIK lymphocytes against ovarian cancer peritoneal carcinomatosis

Intraperitoneal cellular immunotherapy with CAR-redirected lymphocytes is an intriguing approach to target peritoneal carcinomatosis (PC) from ovarian cancer (OC), which is currently evaluated in clinical trials. PC displays a composite structure with floating tumor cells within ascites and solid-like masses invading the peritoneum. Therefore, a comprehensive experimental model is crucial to optimize CAR-cell therapies in such a peculiar environment. Here, we explored the activity of cytokine-induced killer lymphocytes (CIK), redirected by CAR against mesothelin (MSLN-CAR.CIK), within reductionistic 3D models resembling the structural complexity of both liquid and solid components of PC. MSLN-CAR.CIK effectively killed and were functionally efficient against OC targets. In a "floating-like" 3D context with floating OC spheroids, both tumor localization and killing by MSLN-CAR.CIK were significantly boosted by fluid flow. In a "solid-like" context, MSLN-CAR.CIK were recruited through the extracellular matrix on embedded tumor aggregates, with variable kinetics depending on the effector-target distance. Furthermore, MSLN-CAR.CIK penetrated the inner levels of OC spheroids exerting effective tumor killing. Our findings provide currently unknown therapeutically relevant information on intraperitoneal approaches with CAR.CIK, supporting further developments and improvements for clinical studies in the context of locoregional cell therapy approaches for patients with PC from OC.

Machine learning endometrial cancer risk prediction model: integrating guidelines of European Society for Medical Oncology with the tumor immune framework

Current prognostic factors for endometrial cancer are not sufficient to predict recurrence in early stages. Treatment choices are based on the prognostic factors included in the risk classes defined by the ESMO-ESGO-ESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European Society for Radiotherapy and Oncology) consensus conference with the new biomolecular classification based on POLE, TP53, and microsatellite instability status. However, a minority of early stage cases relapse regardless of their low risk profiles. Integration of the immune context status to existing molecular based models has not been fully evaluated. This study aims to investigate whether the integration of the immune landscape in the tumor microenvironment could improve clinical risk prediction models and allow better profiling of early stages. Leveraging the potential of in silico deconvolution tools, we estimated the relative abundances of immune populations in public data and then applied feature selection methods to generate a machine learning based model for disease free survival probability prediction. We included information on International Federation of Gynecology and Obstetrics (FIGO) stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon γ signature, and relative abundances of monocytes, natural killer cells, and CD4+T cells to build a relapse prediction model and obtained a balanced accuracy of 69%. We further identified two novel early stage profiles that undergo different pathways of recurrence. This study presents an extension of current prognostic factors for endometrial cancer by exploiting machine learning models and deconvolution techniques on available public biomolecular data. Prospective clinical trials are advisable to validate the early stage stratification.