The ColoMARK network integrates 17 teams with multidisciplinary expertise (omics, epidemiology, microbiome, circulating tumour DNA, bioinformatics, assay development, circulating RNAs, circulating tumour cells, tumour profiling, clinics) that will take advantage of the assets conferred by novel liquid biopsy strategies to provide complementary and comprehensive know-how on the features required to successfully fulfil the objectives of ColoMARK: 1) to foster a next generation of scientists that can address the challenges of colorectal cancer biomarker development. The network is enforced by pre-existing collaborations from COST Action TransColonCan, and enhanced by the participation of non-academic entities, who will play an essential role in the cross-sectoral and transversal training of the researchers. It is our purpose to provide a personalised training programme that builds on the knowledge offered by the participants to produce future cross-sectoral leadership and a sustainable initiative of excellent research that enhances the competitiveness of European researchers; 2) to achieve maximum impact by appropriate dissemination, patient and public engagement, and results exploitation, activities that will be embedded in the training of the researchers. Given the promising perspectives of liquid biopsy approaches for the future of cancer prevention and management, ColoMARK will constitute an opportunity to reach outstanding scientific, technological and societal impact in a field highly relevant to Horizon Europe´s priorities in cancer, therefore enhancing European competitiveness in R&I; 3) to advance in colorectal cancer understanding by producing outstanding, synergistic research. As the main scientific aim of ColoMARK, ten tailored ground-breaking research projects, only possible in the context of ColoMARK, will be undertaken for the identification and validation of better risk, prognostic and monitoring colorectal cancer biomarkers.

Nowadays, cerebrovascular diseases are devastating conditions representing a large cause of mortality worldwide. It is estimated that 1.1 million people suffer a stroke every year in Europe. Due to the lack of a reliable treatment for stroke, it is important to keep looking for new treatments that stop the progression of the insult by providing neuroprotection, and even promote neuronal recovery for those neurons already affected by the condition. The goal of NeuProHRI is to study the potential of targeting the HRI kinase cellular cascade as a new therapeutic aim for stroke. To develop this, I will use a combination of cutting-edge molecular techniques to define the cellular pathways underlying the activation of the HRI kinase, and their effect on promoting neuroprotection in in vitro and in vivo models of stroke. First, I will decipher the molecular cascade following the activation of HRI in primary cultured neurons by pharmacological and genetic approaches. Then, in vitro models of stroke will be used to measure the impact of these treatments in cells with stroke-like phenotype. Finally, mouse models of stroke will allow me to tackle the impact of pharmaceutical and genetic treatments on in vivo brain tissue. Moreover, Ribo-seq analysis will be performed to study accurately protein synthesis and its relation to neuron-intrinsic pathways involved in stroke. There is a clear benefit of the mobility for both the applicant and the host, ensuring high quality results and dissemination. In this regard, the host will ensure the acquisition of new technical, management, tutorial and transferable skills. The applicant will also benefit from a multidisciplinary environment enhancing international collaboration that will surely contribute to diversify his career. Finally, via a specialized Career Development Plan, the host will provide the ideal training and validation environment, through which the applicant will reach unprecedented levels of professional maturity.

Our overall objectives are to accelerate the diagnosis, and enable personalised management, of inherited metabolic diseases (IMDs). Established academic technology for statistical genomic analysis, deep learning-based prediction of protein structure, and whole-body metabolic network modelling shall be applied to generate personalised computational models, given patient-derived genomic, transcriptomic, proteomic and metabolomic data. To train diagnostic models, a comprehensive clinical team will recruit 1,945 diagnosed patients with a wide variety of IMDs, then validate the clinical utility of personalised computational models on a set of 685 undiagnosed patients. An enhanced human metabolic network reconstruction, especially for lipid metabolism, reaction kinetics and inherited metabolic disease pathways, will increase the predictive capacity of cellular and whole-body metabolic network models. As an exemplar for other IMDs, personalised computational modelling will be used to identify compensatory and aggravating mechanisms that associate with clinical severity in Gaucher disease. The predictive capacity of personalised models will be validated by comparison with additional empirical investigations of protein structure and function as well as metabolomics, tracer-based metabolomics and proteomics of patient-derived in vitro disease models. To maximise the potential for impact, personalised modelling software will be developed to be generally applicable to a broad variety of IMDs, and implemented in a way that is both accessible to clinicians and admissible to regulatory authorities. Sustainability will be promoted by development of a roadmap for a European foundation to aid personalised diagnosis and management of IMDs, informed by broad stakeholder consultation. This is a unique opportunity to realise the potential of personalised computational modelling for a broad set of rare diseases, which is a field where European collaboration is an essential for progress.

In radiation therapy (RT), which is used in more than 50% of cancer treatments, the dose delivered to the tumour/normal tissue determines tumour control and toxicity. Targeted radionuclide therapy (TRNT) is an effective growing type of RT in which radiative compounds with high tumour affinity are administered to patients. While in external RT there are well-defined methodologies to accurately determine the dose, this is not the case in TRNT. Traditionally, TRNT dosimetry is obtained from simple biokinetic models, developed many years ago, and non-individualized dosimetric factors calculated in phantoms. This dosimetry is inaccurate and not patient individualized. The recent European council directive 2013/59/Euratom, transposed on Feb 2018, has clearly highlighted the need to accurately report all doses from radiopharmaceutical procedures, hardening the criteria of previous regulations. The objective of this project is to develop methodologies for the accurate, individualized dosimetry and radiobiological assessment of TRNT, shifting from the current paradigm of empirical treatment to the era of personalized treatment. This will be achieved by the following actions: a) adaptation of Monte Carlo codes for patient’s internal dose calculation, considering novel biokinetic models of the drug biodistribution; b) development and implementation of radiobiological algorithms to evaluate tumour control/toxicity; and c) implementation of a platform useful for the clinical practice, incorporating these models and allowing easy handling and user interactivity. To successfully address this, the researcher experience will be combined with the host organisation (FIDIS) capabilities, a leading biomedical research institute where the supervisor is already involved in TRNT research. FIDIS will also provide clinical data for the models development/validation, an interdisciplinary environment and a training program greatly contributing to re-enforce the researcher professional maturity