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

Abstract

ObjectiveCurrent 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.MethodsLeveraging 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.ResultsWe included information on International Federation of Gynecology and Obstetrics (FIGO) stage, tumor mutational burden, microsatellite instability, POLEmut status, interferon gamma 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.ConclusionThis 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