Circulating tumour microenvironment components as Urothelial Cancer Immunotherapy Response Predictors
Background and rationale: Urothelial carcinoma (UC) is the 9th most common cancer worldwide. There are not reliable biomarkers to predict prognosis, therapy response or metastasis. Although liquid biopsy has emerged as a reliable tool for tumour surveillance, nothing has been done with the circulating components of the tumour microenvironment. In this context, UCIPredict will develop an innovative and reliable urine and blood-based biomarker test for response prediction to immunotherapy (IT) and tumour recurrence, using non-invasive techniques measuring circulating biomarkers from tumour and tumour microenvironment. Hypothesis and aims: Implementation of liquid biopsy biomarkers will improve diagnosis, prognosis and prediction of IT response in UC patients. The main objective of this project is to identify molecular and cellular signatures from urine and blood samples to develop a robust and reproducible laboratory tool for personalized therapy and IT response prediction in UC patients. We will 1) Identify potential molecular targets to guide IT treatments in UC, 2) Detect circulating tumour cells (CTCs) and tumour hybrid cells (THCs) in IT treated and metastatic patients and 3) Evaluate immunomodulation for IT outcome. Methods: We will develop a multinational platform with a transversal study design using urine and blood samples from UC patients. We will employ high standing, high throughput technology, guided by standard operational procedures (SOPs) that will be validated by multinational laboratories. Expected results and potential impact: UCIPredict will provide a non-invasive innovative tool for patient prognosis and IT response in UC. Its implementation on the clinical practice for IT response prediction would increase the rate of patient receiving the best clinical benefit. UCIPredict use will prevent treating non responding patients, which would not only avoid unnecessary suffering for many patients, but also a significant saving for healthcare systems.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.