Ventricular tachycardia (VT) is an unpredictable and potentially deadly condition and should be promptly treated with catheter ablation and medication, before irreversible and potentially fatal organ damage follows. Unfortunately, this combination of treatments does not prevent VT reoccurrence in 30-50% of VT patients and while they can undergo multiple invasive ablations, technical difficulties or refusal of the patient can lead to a lack of effective treatment options. A promising novel, non-invasive treatment option for VT is stereotactic arrhythmia radioablation (STAR). Besides being non-invasive, STAR can also be used to reach locations that are inaccessible for catheter ablation, which may potentially improve effectiveness of overall VT treatment. Small scale first in men/early phase trials have been performed for STAR, providing proof-of-concept for clinical safety and efficacy. However, patients with recurrent VT are not a homogenous group and each center deals with different inclusion criteria, imaging and/or target definition. Many questions remain and the available studies lack the power to clinically validate the approach and prepare for late stage phase III trials. The STOPSTORM consortium sets out to consolidate all current and future European efforts to clinically validate STAR treatment by merging all data in a validation cohort study, standardising pre-treatment and follow-up, in order to collect the data sets and statistical power needed to unanimously establish clinical safety, efficacy and benefit for STAR. The STOPSTORM consortium also sets out to refine protocols and guidelines, determine volumes of interest, define and model the optimal target region and target dose, also in relation to surrounding healthy tissues (i.e. organs at risk) and determine which patient population and underlying cardiopathies respond best to STAR. By doing so the STOPSTORM consortium paves the way to consensus and future late stage clinical trials for STAR.

Background: EU countries face large health challenges to combat chronic diseases. Recently, systems medicine has emerged as a promising discipline to accelerate the translation of basic research into applications for improved diagnostics and personalized treatment. Its power arises from the integration of laboratory and computational approaches crossing research disciplines and sectors to solve clinical questions. COSMIC delivers the next generation of leading, entrepreneurial, and innovative systems medicine professionals having expertise, skills, and experience to successfully combat complex human disorders. These professional will find excellent career opportunities. COSMIC focuses on B-cell neoplasia and rheumatoid arthritis, prototypical diseases originating from abnormal functioning of immune cells, often resulting in similar antigen specificities. COSMIC enables Early Stage Researchers to play a leading role in this exciting field. Approach: COSMIC develops and integrate experimental and computational approaches and establish a unique cross-fertilization between oncology and auto-immunity. In addition to transferable skills, the training program focuses on establishing a double expertise in laboratory and computational to address clinical questions. It involves a wide-range of stakeholders: (pre)clinical departments, companies, patient groups, students, and the general public. COSMIC will establish a link with the leading European EASyM and ISBE initiatives, and aims to harmonize systems medicine training throughout Europe by connecting to other EU (Marie Curie systems medicine) training initiatives. Impact: COSMIC (i) significantly improves ESR career perspectives (ii) leads to new public-private collaborations increasing competitiveness for companies; (iii) contributes to future oncology and immunology medical care; (iv) contributes to the EU systems medicine best practices.

Serious mental illness – depression, bipolar, and psychotic disorder – is among the leading causes of disability worldwide, disproportionately affecting people of non-European ancestry. On top of the burden posed by its symptoms, mental illness is also associated with comorbid health problems. The two most important ‘comorbidities’ of mental illness, given their driving role in decreasing quality and duration of life, are substance (mis)use and cardiovascular disease. Whether these comorbidities arise due to causal relationships is surprisingly unclear. The causal direction is also uncertain: does mental illness lead to comorbidities, and / or do comorbidities increase the risk of (more severe) mental illness? Through several prestigious fellowships, I have established myself as an expert in epidemiological and genetic causal inference methods. In this ambitious project, I will bring together innovative approaches to unravel the relationships of mental illness with substance (mis)use and cardiovascular disease. My aims are to: 1) assess bidirectional relationships between mental illness and its comorbidities by conducting longitudinal analyses in (multi-ancestry) prospective cohort studies, 2) distinguish bidirectional relationships from shared genetic liability by jointly modelling the complete genetic architecture of mental illness and its comorbidities (genomic structural equation modelling), 3) establish causality by using only highly significant genetic variants as instruments for one variable and testing causal effects on another (Mendelian randomization), 4) fully unravel the nature of relationships between mental illness and its comorbidities by ‘triangulating’ evidence from aims 1 to 3, and 5) assess how informing medical doctors about the outcomes of aim 4 influences their clinical decisions in a randomized online experiment. This interdisciplinary project sets the stage for more effective prevention and treatment of mental illness, across ancestry groups.

After the primary intervention, most of cancer patients are managed at home, facing long-term treatments or sequelae, making the disease comparable to a chronic condition. Despite their benefit, strong therapeutic regimens often cause toxicity, severely impairing quality of life. This may decrease adherence to treatment, thus compromising therapeutic efficacy. Also due to age-related multimorbidity, patients and their caregivers develop emotional, educational and social needs. CAPABLE will develop a cancer patient coaching system with the objective of facing these needs/issues. The time is right to fully exploit Artificial Intelligence (AI) and Big Data potentialities for cancer care and bring them to patients’ home. CAPABLE will rely on predictive models based on both retrospective and prospective data (clinical data, data from unobtrusive environmental and wearable sensors, data from social media and questionnaires). Models will be integrated with existing clinical practice guidelines and made available to oncologists. Thanks to the mobile coaching system for patients, CAPABLE will allow identifying unexpected needs, and providing patient-specific decision support. This feature, together with the chance of discovering unknown adverse effects of new treatments, makes CAPABLE more than a personalised tool for improving life quality, an advance for the whole research community. Our team includes complementary partners with experience in data- and knowledge-driven AI, data integration, telemedicine, decision support. In addition, the involved patients’ association gives a unique opportunity to access thousands of questionnaires on patients’ needs, which will inform the system design. The project addresses EU priorities such as shifting care from hospitals to home to face scarcity of healthcare resources, facilitating patients’ re-integration in the society and in the labour market, and ensuring all EU citizens to benefit from an effective, novel cancer care model.

GEMINI aims to deliver validated multi-organ and multi-scale computational models for treatment decision support and improved fundamental understanding of acute strokes, both ischaemic and haemorrhagic. We will demonstrate the added benefit of these computational models in personalised disease management. Specifically, GEMINI will deliver validated, integrated multi-scale, multi-organ Digital Twin in Healthcare (DTH) models for cerebral blood and cerebrospinal fluid flow, brain perfusion and metabolism, and blood flow and thrombosis along the heart-brain axis by integrating available and newly developed dynamic, interoperable, and modular computational models. Building on these models, GEMINI will deliver validated population-based DTHs of ischemic and haemorrhagic stroke aetiology and onset, treatment, and disease progression. Utilising these population-based DTHs, GEMINI will validate five personalised subject-specific DTHs, (1) stroke treatment, and (2) disease progression DTHs for acute ischaemic stroke and (3) aneurysm treatment, (4) subarachnoid haemorrhage progression, and (5) unruptured intracranial aneurysm risk assessment DTHs for haemorrhagic stroke to guide patient care and long-term management. We will bring proof of value of digital twins by the evaluation of the ischaemic stroke treatment selection DTH in a multi-centre clinical trial, in which treatment and patient outcomes are compared in situations with and without the availability of a DTH. GEMINI will implement a project-wide structured approach for data harmonisation, curation, model validation, verification, and model certification of the DTHs. Several outcomes of GEMINI have a high value for clinical practice, medical device industry, and in enhancing research in the fields of (bio)medical and computer sciences, warranting an extensive valorisation strategy with adequate IP protection and versatile exploitation actions to enhance a wide adaptation of the results of GEMINI.