Individualized treatment of cancer has improved treatment for some caser types, but is less implemented for endometrial cancer. Endometrial cancer is the most common gynecological cancer type and arise in the lining of the uterus, the endometrial glands. The incidence of EC is increasing and while prognosis is favorable for localized and early stage disease, patients with aggressive or advanced disease have poorer prognosis. To improve treatment for patients with poor prognosis, more knowledge on the mechanisms that causes aggressive disease as well as detailed description of the intratumor heterogeneity is needed. This project will bring together experts from different European countries, with expertise on cancer biology, biomarkers, genetics and artificial intelligence to develop better tools for diagnosing and treating endometrial cancer. The project will seek to develop models that mimics the molecular types of endometrial cancer. These models will be used to develop new treatment strategies, based on genetic alterations in the specific tumor, and contribute better treatment options and eventually better quality of life for patients who have been treated for endometrial cancer.
In the era of precision oncology, personalized management represents a challenge in Endometrial Cancer (EC). The incidence of this cancer has increased in the last years, and although patient prognosis is favorable in early stages, the prognosis for advanced disease are still poor and therapeutic options are few. This has a dramatic negative effect on the overall survival of EC patients with advanced disease. Thus, to improve the personalized treatment of poor prognosis EC patients, it is vital to gain knowledge on the cancer molecular biology and to also capture the innate intratumor heterogeneity (ITH) of these tumors. The objective of the present proposal is to reach personalized EC management by developing new tools that recapitulate the heterogeneous molecular composition of tumors and establish new and effective therapeutic regimens. In order to address these challenges, our consortium proposes to develop a new and robust algorithm, named ECLAI, to predict recurrence and therapy response in high-risk EC. Furthermore, we will aim to identify the best therapeutic options to advance personalized treatment of these patients. For this, the consortium will combine: a) the use of non-invasive biopsies, which capture ITH, to study the genomic landscape and to monitor the disease evolution of EC, b) the generation of valuable preclinical models to test alternative targeted therapies and c) the application of machine learning strategies to decipher a recurrence and therapeutic response rate algorithm with clinical application. This workflow will be applied with the advisory and support of patients’ associations and ENITEC, the European research network on EC. All together, this pioneering strategy has the final goal to improve the management and life quality of EC patients who currently have limited clinical opportunities.
BEHANDLING-God og treffsikker diagnostikk, behandling og rehabilitering