Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
OPTIMAL METHODS TO CHARACTERIZE ADC RESISTANCE IN SOLID TUMORS AND IDENTIFY CLINICALLY USEFUL BIOMARKERS (OASIS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Unlocking the full potential of new tools, technologies and digital solutions for a healthy society
Action type: Research and Innovation actions
Countries involved: France,Belgium,Greece,Sweden
Call: Innovative non-animal human-based tools and strategies for biomedical research (HORIZON-HLTH-2024-TOOL-05-two-stage, 2024)
Start date: 2025-01-01
End date: 2029-12-31
Duration (months): 60.0
Total project cost (EUR): 10389734.82
EU funding (EUR): 9941527.86
Participants: 13
Project status: SIGNED

Over the past 5 years, antibody drug conjugates (ADCs) have shown impressive improvements in survival outcomes of solid tumors and hematological malignancies. With 14 ADCs already approved across different countries and more than 140 that entered the clinical development, they are intended to replace standard chemotherapies across multiple tumor types over the next decade. Although ADCs show great clinical efficacy, resistance eventually occurs, and it becomes critical to understand resistance mechanisms to guide the choice of the following lines of therapy for patients who progress on a given ADC. Given the complex nature of ADCs, immunocompromised mouse models (nude mice, NOD-SCID or NOG mice) and currently used clinical assays (standard radiology, IHC, WES etc) are not the optimal preclinical and clinical tools, to identify the multiple causes of resistance. The OASIS project aims to generate a biobank of different patient-derived organoids (PDOs), which better recapitulate ADCs resistance and integrate different assays, spanning from whole-body molecular imaging (Ab-radiolabeled PET scan or immunoPET), circulating tumor cells (CTC), plasma proteomics, to multiplex immunofluorescence (MIF) and machine-learning enhanced digital pathology (AI-digital pathology) to capture most of the parallel mechanisms of resistance to ADCs. Such tools will enable to define biomarkers of ADC resistance that can inform further therapeutic decisions.

Keywords: antidoby drug conjugates patient derived organoids ab radiolabeled imaging circulating tumor cells ai digital pathology proteomics
Exploiting Therapy-Induced Neo-Epitopes to Overcome Drug Resistance in Triple-Negative Breast Cancer (TI-uORFe)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Germany
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2025-POC, 2025)
Start date: 2025-12-01
End date: 2027-05-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

Therapy-induced neo-antigens represent a novel class of tumor-specific antigens that arise from therapeutic interventions such as chemotherapy and targeted therapies. These neo-antigens, resulting from genomic mutations, aberrant post-translational modifications, and dysregulated RNA splicing, are recognized by the immune system as foreign, prompting an immune response that bypasses central tolerance mechanisms. Despite advancements in next-generation sequencing and bioinformatics that facilitate the identification of these neo-antigens, distinguishing drug-induced neo-antigens from inherent tumor antigens remains a significant challenge. This proof-of-concept proposal aims to leverage therapy-induced neo-antigens to develop personalized cancer vaccines and immunotherapies, particularly targeting triple-negative breast cancer (TNBC), where drug resistance is prevalent. By utilizing ribosome profiling, we have identified non-canonical peptides translated from therapy-induced changes in cancer cells, which are presented by MHC-I molecules and recognized by T lymphocytes, leading to specific tumor cell killing. Our approach focuses on generating mRNA vaccines based on these neo-epitopes, with the goal of enhancing chemotherapy responses and overcoming drug resistance. The proposed research will address critical bottlenecks in neo-epitope therapy, including accurate identification of immunogenic neo-epitopes and validation of immunogenicity in preclinical models. Ultimately, this innovative strategy aims to improve therapeutic efficacy and patient outcomes in TNBC, offering a transformative approach to combat drug resistance and enhance survival rates.
WISH-OI, Hop on OCEAN ICE: Water - Ice- Snow -Hop-on to Ocean Ice (WISH-OI)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Destination: Improved access to Excellence
Action type: Research and Innovation actions
Countries involved: Denmark,Ukraine
Call: Hop on Facility (HORIZON-WIDERA-2023-ACCESS-06, 2023)
Duration (months): 31.0
Total project cost (EUR): 600000.0
EU funding (EUR): 600000.0
Participants: 2
Project status: SIGNED

WISH-OI enhances the existing OCEAN:ICE (O:I) project in Horizon Europe by the addition of the National Antarctic Science Centre (NASC) of Ukraine to the consortium. NASC expertise will add new observations and analysis as well as innovative numerical modelling to ocean and atmospheric processes and regions not presently represented in the project. NASC logistical support will expand existing O:I observations to areas not covered by O:I around Antarctica, allowing us to close the gap and become a truly circum-polar project. WISH-OI focuses on developing collaborations between scientists in Ukraine and internationally as well as leveraging expertise in O:I to develop existing NASC capabilities and new observational and analytic skillsets. The focus on physical oceanography and meteorology builds on existing research programmes in Ukraine and extends them to applications with the new Ukrainian research vessel Noosphera. WISH-OI will also maintain and develop key Antarctic science objectives and ensure a new generation of scientists will remain in research given the difficult environment now faced by Ukraine.

Keywords: ocean observations,numerical modelling,ocean processes,atmospheric processes,antarctica,widening participation,physical oceanography,meteorology,antarctic
Reversibly operating fuels cells for clean energy and long-term storage (Reverion)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Challenges
Action type: EIC actions
Countries involved: Germany
Call: EIC Accelerator Challenge: Technologies for ‘Fit for 55’ (HORIZON-EIC-2022-ACCELERATOR-01, 2022)
Start date: 2023-02-01
End date: 2025-04-30
Duration (months): 27.0
Total project cost (EUR): 3568126.25
EU funding (EUR): 2497688.0
Participants: 1
Project status: CLOSED

The main innovation of Reverion is a patented (2 patents already granted, 4 submitted and other 6 planned), disruptive new system architecture / design for high temperature fuel cells (or Solid Oxide Cells – SOCs) – we are not producing the SOCs but purchasing those from established SOC manufacturers. The individual subcomponents of a Reverion system are commercially available, however, the system design, meaning the arrangement and use of the components, and the control algorithms of the closed cycle are brand new and highly innovative. It leverages synergies no other product can achieve, such as the unprecedented efficiencies. By combining reversible solid oxide fuel cells with a two-stage offgas recovery, we can achieve a complete fuel utilisation and record-breaking efficiencies: 80% electrical efficiency for power generation and an even higher efficiency in electrolysis mode (energy storage, Power-to-Gas - production of hydrogen or biomethane as desired).

Keywords: energy economics,renewable energy sources,environmental protection,design innovation,environmental and green technologies,bioenergy,engineering and technology,multimodality,energy efficiency,low zero carbon communities,business model innovation,energy systems production,distribution,application
Narrative Archetypes for Artificial Intelligence (AI STORIES)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Texts and Concepts
Panel description: Literary studies , literature,  philosophy
Countries involved: Norway
Call: ERC ADVANCED GRANTS (ERC-2023-ADG, 2023)
Start date: 2024-08-01
End date: 2029-07-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

AI STORIES is premised on the hypothesis that narrative archetypes fundamentally structure the output of contemporary artificial intelligence (AI). Large language models (LLMs) like GTP-4 are trained on vast quantities of text and images and generate new texts that are statistically similar to the training data. The scientific consensus acknowledges that LLMs replicate and sometimes exacerbate historical biases in their training data. AI STORIES proposes that LLMs are also affected by a deeper bias: that of the narrative structures in the social media posts, news stories, marketing blurbs and novels the models are trained on. If this is the case it will deeply impact how we use and apply AI, and how we think about bias and cultural diversity in AI models. Currently available LLMs are largely trained on English-language texts, with a heavy weighting towards the United States. When they generate texts in non-English languages they may succeed in producing grammatically correct texts, but if my hypothesis is correct, their deeper content will be fundamentally structured by the stories that dominate in the training data. This is a threat to cultural diversity that goes well beyond the purely linguistic. AI STORIES applies the humanities’ deep knowledge of narrative to AI research by developing and testing this hypothesis. We will apply narratology to understand the narrative structures of LLM’s training data. We test the hypothesis by training LLMs on specific kinds of narratives, then using prompt engineering and both qualitative and computational narratological analysis to reverse engineer the structures of AI-generated output. Three comparative case studies will look specifically at Scandinavian, Australian and either Indian or Nigerian stories. The overall objective is to develop a narratology of AI, and to leverage the findings to ensure that policymakers, developers, educators and other stakeholders can use our research to direct the future of AI.

Keywords: artificial intelligence,narrative,narratology,digital humanities,datasets,digital culture,literary studies,internet studies,critical dataset studies,ai bias,large language models,llms,machine learning,computational narratology,chatgpt,gpt,cultural heritage
Exploring the Impacts of Global Change on the Risk of Aedes and Dengue Expansion in Tanzania (TRACED)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Ireland,Belgium
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-09-01
End date: 2027-08-31
Duration (months): 24.0
Total project cost (EUR): 268568.64
EU funding (EUR): 268568.64
Participants: 2
Project status: SIGNED

Dengue, a vector-borne disease transmitted by Aedes mosquitoes, has emerged as a significant global health challenge, particularly in regions like East Africa, where its impact is increasingly felt. Tanzania, with recurrent outbreaks and an expanding range of mosquito vectors, offers a critical case study for understanding dengue's broader epidemiological trends in these regions. The spread of Aedes aegypti, driven by global changes such as rising temperatures, urbanisation, and increased transportation networks, has intensified the threat of dengue in Tanzania. Despite this, substantial knowledge gaps remain, particularly in predicting future hotspots for transmission and understanding the extent of the population at risk, now and in the future. The TRACED project aims to address these gaps by integrating innovative methodologies to assess ecological niches, human population dynamics, and dengue transmission risk. The project's outcomes are expected to support global efforts to achieve Sustainable Development Goal (SDG) 3.3, which focuses on ensuring healthy lives and promoting well-being for all, by raising awareness among policymakers, which is crucial for effective intervention strategies. Given the limited research and funding in this area, particularly in Africa, this project will provide critical insights and methodological frameworks that can be applied across resource-limited regions, contributing to better preparedness and response to future dengue outbreaks. Researchers from Technological University Dublin and the Institute of Tropical Medicine are collaborating with Tanzania's Ministry of Health's Neglected Tropical Disease programme and local experts from the Sokoine University of Agriculture (SUA), the Nelson Mandela African Institution of Science and Technology (NM-AIST), and the Ifakara Health Institute (IHI).

Keywords: vector borne diseases,aedes aegypti,climate change,global change processes,infectious diseases,outbreak preparedness
Generalised Time-Separated Stochastic Mechanics (Gen-TSM)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Products and Processes Engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-06-01
End date: 2029-05-31
Duration (months): 60.0
Total project cost (EUR): 1996920.0
EU funding (EUR): 1996920.0
Participants: 1
Project status: SIGNED

As a physical fact, randomness is an inherent and ineliminable aspect in all physical measurements and engineering production. Hence, all material parameters are subject to stochastic fluctuations. However, highly accurate predictions are key to achieving the goals of resource-efficiency and sustainability in engineering and construction. In this context, randomness in material parameters constitutes a severe problem: if we can experimentally quantify the magnitude of fluctuations of material parameters, how can we numerically include this information to predict fluctuations in the reactions of construction parts or processes? All existing strategies for the inclusion of stochasticity in engineering design share the drawback of large numerical costs, leading to inefficiencies, e.g., an increase in simulation time by orders of magnitude. This prevents their industrial application and, consequently, stochastic fluctuations are not factored into design. This is compensated for by adding buffers, e.g., overestimated safety factors, oversized wall thickness, or higher maintenance frequencies, all leading to increased economic and environmental costs. Thus, a step change in engineering design is required to allow the inclusion of stochasticity into material modelling and engineering simulation, while minimising the numerical extra cost. Gen-TSM aims to meet this challenge by developing a novel strategy for efficient stochastic modelling of highly complex problems and creating a computational framework for the automated formulation of stochastic versions of existing material models. My vision is to unlock the currently untapped potential of material modelling and enable computing stochastic behaviour to become the new standard in engineering design. This would revolutionise current state of the art, allowing access to the full information field by including stochasticity into simulations at low computational costs.

Keywords: continuum mechanics computational mechanics stochastic material modelling finite element analysis
Innovating Thyroid Health with Genomics and Predictive Algorithms through Collaborative Excellence (InnoThyroGen)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Destination: Improved access to Excellence
Action type: Coordination and support actions
Panel: Life Sciences
Countries involved: Croatia,Serbia
Call: Excellence Hubs (HORIZON-WIDERA-2023-ACCESS-07, 2023)
Start date: 2025-01-01
End date: 2028-12-31
Duration (months): 48.0
Total project cost (EUR): 4937275.0
EU funding (EUR): 4937275.0
Participants: 13
Project status: SIGNED

Global research indicates that millions of people suffer from thyroid disorders, such as hypothyroidism, hyperthyroidism, autoimmune diseases, and tumors, with nearly 60% of them unaware of their condition. Autoimmune thyroid diseases, notably Hashimoto's thyroiditis and Graves' disease, are the most prevalent, affecting about 5% of the population. Thyroid cancers are also on the rise, with significant increases in detection over recent decades. Despite various treatment and diagnostic options, many face misdiagnosis, ineffective treatments, and diminished quality of life. Levothyroxine, a synthetic thyroid hormone, is widely prescribed in Europe and the USA, yet 20-50% of patients experience improper dosing, necessitating frequent adjustments. Antithyroid medications can cause severe side effects, underscoring the need for careful management of thyroid diseases to avoid increased healthcare costs and lost productivity. Current treatment approaches, often not personalized, overlook genetic predispositions and environmental factors. Advances in genomics, such as next-generation sequencing (NGS) and the polygenic risk score (PRS), offer new opportunities for personalized medicine. These tools, combined with electronic health records (EHR) and lifestyle data, can improve disease prediction and prevention strategies. Pharmacogenomics further enables tailored drug treatment based on individual genetic profiles, promising more effective and personalized care. The design of thyroid disease gene panels and the development of innovative and digital solutions based on artificial intelligence stand as pivotal advancements. These could markedly accelerate the integration of more personalized and cost-effective approaches into clinical practice, heralding a new era of precision medicine for thyroid disorders.

Keywords: thyroid cancers,autoimmune thyroid diseases,polygenic risk score,machine learning,pharmacogenomics
Investigating aspects of metabolism pertinent to atherosclerosis utilizing computational techniques. (IMPACT)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Ireland,United States
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-09-01
End date: 2025-08-31
Duration (months): 24.0
Total project cost (EUR): 199694.4
EU funding (EUR): 199694.4
Participants: 3
Project status: CLOSED

Cardiovascular disease (CVD) claims 17.9 million lives and costs the European economy €210 billion annually. New, innovative strategies are needed to address this health crisis. IMPACT takes a systems medicine approach to extend our understanding of the factors that contribute to cholesterol metabolism and to develop innovative therapeutic approaches for atherosclerotic cardiovascular disease (ACVD). IMPACT has three objectives: 1) Develop an LP algorithm that analyzes models of human and microbiome metabolism to identify optimal microbes and nutrients that may influence metabolic pathways of interest. 2) Develop a detailed model of cholesterol metabolism, that is trained and validated using human data, by synergistically merging FBA models with an ordinary differential equation (ODE) model of cholesterol transport. Simulations and statistical analysis on the in-silico results will be conducted to identify metabolic pathways related to ACVD and to investigate how variables such as microbiome composition, sex, obesity, and diet influence cholesterol metabolism and ACVD risk. 3) Metabolic pathways related to ACVD risk will be targeted by the LP algorithm to identify optimal microbial and nutritional therapies that can treat and/or diminish the progression of ACVD. Promising bacterial strains and nutrients will be tested with in-vivo gut simulators in an intersectoral secondment with International Flavors & Fragrances Inc. Results of the proposed research will be effectively disseminated and exploited to maximize IMPACT. While the action serves to extend the state-of-the-art and address a health crisis, IMPACT will simultaneously expand my technical knowledge and transferable skills, bolster my credentials and enhance my career competitiveness through intersectoral exposure.

Keywords: flux balance analysis,ordinary differential equations,gastrointestinal microbiome,probiotics,cholesterol transport