In this project, a multinational consortium of high-tech SMEs, academic research institutes, biotech and pharma partners, affiliated patient-centred organisations and professional societies will achieve a multi-faceted diagnostic and prognostic platform and a precision medicine approach. This consortium will together realize a patient empowerment centred decision support system that will enable multiple stakeholders to participate in improved and more rapid diagnosis and prognosis, as well as the potential of precision medicine for accelerated development of new therapies. Citizens and patients will be empowered to contribute to the efficient planning and usage of resources. The project will begin by rapidly delivering a nomogram. Data from the pandemic will be used to validate and further optimise a scalable multifactorial diagnosis/prognosis solution. Existing and new data and sample collection efforts will be used to perform molecular profiling, which - using advanced AI techniques will be shaped into a precision medicine approach. These initial outputs will undergo further enhancement and assessment to evaluate the value they add to the development of a decision support system. The entire effort will be supported by the deployment of a federated machine learning system that will allow for the GDPR compliant use of multinational data resources. The various iterations of the decision support system and the federated machine learning system will be made available to other coronavirus initiatives with the intent to develop a stakeholder community that forms the basis for a highly efficient innovation ecosystem. Our proposed study will be one of the first to develop innovative machine learning, and clinical procedure improvement that will potentially make a huge socio-economic impact for the coronavirus outbreak.

The high degree of tumour (genomic and phenotypic) heterogeneity influences patient’s response to therapy and hampers wide deployment of personalised medicine for cancer treatment. Thus, there is an imperative need for new technologies that can accurately detect tumour heterogeneity, allow for patient stratification and assist clinicians in providing the right diagnosis and treatment for the right patient. PREDICT’s mission is to address this huge unmet need. Radiomics, a newly emerging field that uses high-throughput extraction of large amounts of features from radiographic images, can boost the field of personalised medicine. The analysis of medical images taken as standard-of-care allows Radiomics to capture tumour heterogeneity and to generate ‘tumour-specific’ signatures in a non-invasive way, without the need of assessing the patient’s genetic profile. Thus, Radiomics, if linked to Big- data and decision support systems (DSS), can be used as diagnostic tool for patient stratification, for prediction of treatment response and for guidance, involving the patient, of clinical decisions in oncology. However, researchers that understand cancer biology, advanced imaging and big data analytics are virtually absent. Even more challenging is to translate the outcomes into actual clinical tools involving the patient. PREDICT will train 15 highly promising researchers in the emerging field of Radiomics and Big data. These ESRs will be trained to implement the automatic exploitation of large amounts of imaging data to drive decision-making algorithms that will guide diagnosis and treatment of different types of cancer and to develop ‘tumour-specific’ signatures integrated in multifactorial DSS. The ESRs will become experts and innovators in Radiomics, Big Data and DSS, which will allow them to bring unique solutions towards the clinic. PREDICT builds upon a strong consortium with 8 academic and 10 non-academic partners that are all pioneers in their respective field.

The goal of EuCanImage is to build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of artificial intelligence (AI) in oncology. Firstly, the EuCanImage platform will be populated with a completely new data resource totaling over 25,000 single subjects, which will allow to investigate unmet clinical needs like never before, such as for the detection of small liver lesions and metastases of colorectal cancer, or for estimating molecular subtypes of breast tumours and pathological complete response. Secondly, the cancer imaging platform, built by leveraging the well-established Euro-Bioimaging infrastructure, will be cross-linked to biological and health repositories through the European Genome-phenome Archive, allowing to develop multi-scale AI solutions that integrate organ-level, molecular and other clinical predictors into dense patient-specific cancer fingerprints. To deliver this platform, the consortium will build upon several key European initiatives in data sharing for personalised medicine research, including EUCANCAn (cancer genomics and health data sharing), euCanSHare (cardiac imaging and omics data sharing) and EUCAN-Connect (federated data analytics). Furthermore, to foster international cooperation and leverage existing success stories, the consortium comprises the coordinators of The Cancer Imaging Archive (TCIA), the US cancer imaging repository funded by the National Cancer Institute. This will allow EuCanImage to leverage a unique 10-year long experience in cancer imaging storage, anonymisation, curation and management. Finally, a close collaboration between world renown clinical, radiomics, AI and legal experts within the consortium and beyond will establish well-needed guidelines for AI development and validation named FUTURE, for delivering Fair, Universal, Traceable, Usable, Robust and Explainable decision support systems for future cancer care.

TIGER delivers proof of principle (PoP) in humans for a novel best-in-class therapeutic mRNA cancer vaccine platform optimized for intravenous (IV) administration, with the aim to show clinical benefit. The antigens used for the PoP consists of mRNAs encoding the proteins E6 and E7 of Human Papilloma Virus strain 16 (HPV16), and TriMix mRNAs that act as adjuvant to stimulate dendritic cells to start strong T cell responses. The mRNAs will be formulated in a novel patented lipid nanoparticle shielding the mRNA, and delivering it to immunoactive antigen presenting cells, vastly enhancing T-cell response. Safety and potent efficacy of our IV mRNA product have been demonstrated in rodent experiments. Furthermore, preclinical to clinical translation has been shown for our TriMix based vaccines using different delivery strategies. Based on the preclinical and prior clinical data, our platform has the potential to cure cancer patients. The PoP study will be in patients with recurrent HPV16 positive cancer, which is categorised as a non-communicable disease by the WHO, without and with a PD-1 checkpoint inhibitor. Safety, immunogenicity and clinical benefit will be key endpoints of the study. Biomarker and PROM research will allow future informed therapeutic and care decisions by both patient and care team. Recruitment and stratification plans will be in place. Interactions with regulatory, reimbursement and ethical authorities together with patients and carers will help laying out the route to the patient not only for our product but also for all other mRNA cancer vaccines. The project encompasses essential elements for preparing therapy validation in later stage clinical studies, while addressing patient needs, values and choices. Upscaling of GMP-production for IV mRNA vaccines will enable further clinical studies. Once validated, our platform will be easily translatable to a wide range of cancers using other tumour antigens, be they TSA, TAA or neoantigens.