Project description
Better monitoring of cancer immunotherapy patients
Cancer immunotherapy has helped to significantly advance cancer treatment. However, there remain two main challenges that hinder the path to better health and quality of life for cancer patients after starting immunotherapy. There is a need for predictive markers for immunotherapy-related adverse events as well as information on such patients beyond randomised controlled trials. To address these problems, the EU-funded QUALITOP project aims to develop a European immunotherapy-specific open smart digital platform. The platform will help identify the determinants of patients’ health status, define patient profiles in a real-world context, and provide real-time recommendations. The project will thus enable better monitoring of the health and quality of life of cancer patients who are undergoing immunotherapy.
Objective
"Cancer immunotherapy brought about significant progress in cancer treatment. It resulted in high efficacy in some cancers; e.g. up to 60% objective response rate in melanoma and 80% complete response rate in acute lymphoblastic leukaemia. Nevertheless, two main challenges still impede improving cancer patients health status and quality of life (QoL) after immunotherapy initiation: 1) a crucial need for predictive markers of occurrence of immunotherapy-related adverse events (IR-AEs) to predict and improve patients health status and promote their QoL; and, 2) the lack of knowledge on patients after start of immunotherapy outside randomised controlled trials. To reach these goals, significantly more diversified sources of data are required.
Project QUALITOP aims at developing a European immunotherapy-specific open Smart Digital Platform and using big data analysis, artificial intelligence, and simulation modelling approaches. This will enable collecting and aggregating efficiently real-world data to monitor health status and QoL of cancer patients given immunotherapy. Through causal inference analyses, QUALITOP will identify the determinants of health status regarding IR-AEs and define patient profiles in a real-world context. For this, heterogeneous data sources (big data), both retrospective and prospective --collected for QUALITOP from clinical centres in four EU countrieswill integrate lifestyle, genetic, and psychosocial determinants of QoL. Using machine learning approaches, QUALITOP will provide ""real-time"" recommendations stemming from patient profiles and feedbacks via the Smart Digital Platform. Furthermore, an increased visibility on patients behaviour, a better IR-AEs prediction, and an improvement of care coordination will help analysing through simulation modelling approaches the gain in cost-effectiveness. Guidelines will be issued over the short and long-term.
"
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health sciencesclinical medicineoncologyskin cancermelanoma
- medical and health sciencesbasic medicineimmunologyimmunotherapy
- medical and health sciencesclinical medicineoncologyleukemia
You need to log in or register to use this function
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
69002 Lyon
France