Immunotherapy (IO) is the new standard of care for many patients with advanced Non-Small Cell Lung Cancer (aNSCLC), yet only around 30-50% of treated patients benefit from IO in the long term. Programmed Death-Ligand 1 (PD-L1) remains the only biomarker used to predict patient outcome to IO, though its efficacy is limited. Other potential biomarkers have been identified, yet not validated in prospective randomized clinical trials, providing only partial evidence. Due to the dynamic complexity of the immune system-tumour microenvironment, its interaction with the host and patient behaviour, it?s unlikely for a single biomarker to accurately predict patient outcome. Artificial Intelligence (AI) and machine learning (ML) frameworks, that synthetize and correlate information from multiple sources, are essential to develop powerful decision-making tools able to deal with this highly complex context and provide individualized predictions to improve patient outcomes reducing the economic burden of health care systems in NSCLC. The aim of the I3LUNG project is to develop such AI-based tools to assist in improving survival and quality of life, preventing undue toxicity, and reducing treatment costs. I3LUNG adopts a two-pronged approach: setting up a transnational platform of available data from 2000 patients in order to validate the AI models, and generating a multi-omics prospective data collection in 200 NSCLC patients integrating diverse -omic information then validate its usefulness in leading IO therapeutic decisions. A psychological study will help in defining the impact of AI-guided decisions on patients, eliciting their preference, and physicians comparing AI with Human Intuition. The final goal is the construction of a novel integrated AI-assisted Data Storage and Elaboration Platform backed up by Trustworthy Explainable AI methodology, ensuring its accessibility and ease of use by healthcare providers and patients alike.

Measurable residual disease (MRD) detected by multiparameter flow cytometry (MFC) has strong prognostic value in patients with the most frequent acute and chronic leukemias, acute myeloid (AML) and chronic lymphocytic leukemia (CLL), but it has not yet been confirmed as a treatment-guiding biomarker. The RESOLVE Consortium will leverage numerous existing expert networks and patient advocacy partnerships to establish the predictive value of MRD in AML/CLL patients, with the expectation that this affordable, minimally-invasive biomarker can be imminently used to guide the intensity of consolidation therapy, improve quality of life (QoL), and reduce costs. This will be achieved through 1) development of a real-world patient registry and data platform; 2) establishment of standardized, decentralized MRD analysis across Europe; and 3) a randomized, controlled multi-national pragmatic trial based on the hypothesis that treatment intensity can be safely reduced in MRD negative AML/CLL patients, to provide evidence for the clinical, personal and societal impact of MRD-guided therapy. These efforts will be supported by RESOLVE’s participatory research pipeline, which will incorporate input from patients, caregivers, and experts in social sciences and health economics. The real-world nature of the study ensures broadly applicable results for all patients regardless of location, socioeconomic status, gender, sex, disability or ethnicity. The findings will then be effectively communicated and disseminated following open science principles through the medical community for uptake in routine clinical practice. The laboratory, clinical, and patient advocacy infrastructures already in place will support rapid adoption of MFC-based MRD assessment to aid in clinical decision-making. The Consortium’s widespread member organizations will work with policymakers and authorities across the EU to provide access to the test in the national health care systems for all AML and CLL patients. This action is part of the Cancer Mission cluster of projects "Diagnostics and Treatment (diagnostics)".

The incidence of paediatric onset Inflammatory Bowel Diseases (PIBD) has risen dramatically in recent decades. Compared to adult forms, PIBD reflects a more severe disease, more often requiring aggressive treatment with immunomodulators, and thereby exposing children to a life-long risk of serious disease and treatment-related adverse events, such as infections and malignancies. Therefore, there is an urgent need to develop strategies which balance, on an individual basis, therapeutic effectiveness with risks of treatment. The overall goal of this proposal is to develop and validate a treatment algorithm for PIBD based on high or low risk predictors for early complicated or relapsing disease. This will improve effectiveness, while reducing treatment related risks and life-long complications due to uncontrolled disease progression. To attain this goal 3 specific aims are proposed under the umbrella of an international network, the "PIBD-net": 1) Development of an accessible and feasible risk-stratified treatment algorithm for new onset paediatric IBD on an existing inception cohort and validation in an independent cohort 2) Generation of a prospective large longterm real world inception cohort in a registry designed to analyze effectiveness and safety signals and correlate them to individual risk factors 3) Design and performance of a risk algorithm-based prospective large-scale multicenter randomized clinical trial (RCT) (stratification into high or low risk groups based on specific aim#1) in order to provide optimal personalized therapy : low risk azathioprine vx. methotrexate, high risk : methotrexate vx. adalimumab This project will translate into the first risk-stratified PIBD treatment algorithms allowing optimization of medical therapy while minimizing treatment-related risk (personalized medicine).