A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drug therapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacological treatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledge and translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective, personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translate scientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA to bridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. To that end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across the major psychiatric disorders of schizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basis of TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedented collection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies of individuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts will enable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we will enable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision support and patient-oriented decision-making mental health boards.

The Instituto de Medicina Molecular João Lobo Antunes (iMM) is a leading European institute in basic biomedical research, now establishing a pioneering Centre of Excellence in human-centred clinical and translational research in Portugal. Research at iMM increasingly requires analysing large volumes of molecular, phenotypic and clinical data but its development is constrained by the national scarcity of experts in biomedical data science. BIOMICS is therefore set on iMM’s strong data-driven research and innovation (R&I) model and investment on the digital transformation of biomedical and clinical research. BIOMICS aims at: 1) leveraging iMM’s excellence in biomedical data science by implementing joint research projects with the partner institutions, strengthening existing interactions and promoting new ones, through staff exchanges, expert visits and joint lab retreats; 2) training a new generation of critically thinking and ethically aware researchers who are able to test scientific hypotheses on biomedical data and soundly interpret their results, through integration in international mentoring networks, facilitating conference and thematic course attendance, and organising on-site training; 3) enhancing international awareness and attract talent to iMM in data science, through mobility of researchers, targeted dissemination and communication activities, and on-site organisation of workshops and an international conference; 4) strengthening iMM’s entrepreneurial and innovation capacity, adapting it to the digital transformation of biomedical and clinical research, through professional technology transfer activities and synergies with the information technology industry. BIOMICS will sustainably leverage iMM’s timely R&I model by supporting the associated digital transformation, making iMM internationally competitive in biomedical data science. Moreover, BIOMICS will provide the partners with a platform for exploring the translational potential of their research.

Type 2 diabetes (T2D) is a major public health problem, affecting 55 million European citizens. T2D ensues in individuals who develop a progressive pancreatic beta cell failure. T2D probably comprises a heterogeneous group of diseases. A new molecular taxonomy of T2D is essential for the development of medical care that is predictive, preventive and personalized. Currently available T2D therapies are not disease modifying: they treat hyperglycaemia without addressing its underlying cause, i.e. beta cell failure. In this proposal we seek to identify pathogenic molecular events that operate in the diseased tissue, i.e. the failing human beta cell, at their true level of complexity. T2DSystems will accomplish this ambitious goal by integrating human islet genetic and epigenetic data with disease-relevant environmental perturbations, metabolomics and functional studies, and use this knowledge to identify distinct human islet phenotypes in subgroups of patients. In closely interacting work packages, we will achieve the following goals: • Compile and expand existing European bio-banks and datasets to create the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), a T2D systems biomedicine resource of unprecedented scale; • Develop large-scale data analysis tools and both data driven and mechanistic probabilistic modelling frameworks to exploit TIGER towards system level biological insight; • Translate these findings to identify stratified beta cell phenotypes in human cohorts. This will provide understanding of beta cell pathophysiology in vivo and enable stratified prevention and therapeutics. T2DSystems will enable the development of personalized diagnostic tests, taking into account individual environmental and genetic risk factors. The newly identified molecular disease mechanisms will provide the basis for development of novel therapies and for patient stratification to test individualized therapies.

Diabetes Mellitus affects nearly 600 million people worldwide. It is a major cause of premature death, blindness, end stage kidney disease, and limb amputation. However, it is not a single disease. Most patients are catalogued as type 2 diabetes, which is itself highly heterogeneous, while others have autoimmune type 1 diabetes. Genetic testing is currently able to define the precise cause of diabetes in a small group of young patients, although the categorization of diabetes subtypes is in most cases largely based on clinical judgement, rather than on specific tests. It is known, however, that the classification of diabetes subtypes has major implications for treatment. The emergence of whole genome sequencing in clinical practice provides new opportunities for classification of diabetes subtypes, but also entails major challenges such as the interpretation of non protein-coding variants. The recently funded ERC project DecodeDiabetes analyzed sequences from nearly 1500 young patients who had typical features of genetic forms of diabetes, but negative genetic tests in specialized genetic diagnostic laboratories. This study revealed different groups of genetic variants that cause diabetes in young patients with diabetes. It also developed approaches that leverage regulatory genomic knowledge to define noncoding genetic defects underlying human disease. The current proposal aims to compile new findings with existing knowledge, and to build a genetic interpretation solution to subcategorize young patients with diabetes. It will then validate this tool in patient cohorts. This proposal can thus translate fundamental knowledge derived from the ERC-funded Decode Diabetes project into applications whose valorization can bridge the gap to market and provide added value for society.