In childhood, adolescence and young adults (CAYA), melanoma is under-studied and non-existing tailored clinical guidelines and standardized approaches lead to a very low diagnostic accuracy. The MELCAYA project aims to understand risk factors and determinants of melanoma to improve the prevention, diagnosis and prognosis of melanomas in CAYAs through a strong international consortium with experts from 10 countries in different disciplines (e.g. oncology, paediatrics, ethics, policy making), and sectors (e.g. academic centers, SMEs, hospitals, patient associations). MELCAYA will work on different approaches. 1) By integrating existing reference European cohorts and registries, studies of genetic and environmental risk factors and progression of melanoma in CAYA will be performed through different omic methods, and a novel taxonomy of CAYA melanoma will be generated. 2) MELCAYA will also develop image-based robust and trustworthy machine learning tools and a pan-European second-opinion platform for better diagnosis specifically designed for CAYA. 3) Moreover, the validation of minimally and non-invasive disruptive tools based on artificial intelligence and volatilomics detection from exhaled breath and skin will lead to earlier detection and more accurate prognosis of melanoma in CAYA. 4) Finally, through the evidence gathered, MELCAYA will design and implement public health strategies and will actively involve patients and the general population. The results of MELCAYA will maximize its impact by making its data and results accessible and re-usable through integration into UNCAN.eu. This action is part of the Cancer Mission cluster of projects on ‘‘Understanding".

The SCARLET (SCAling up early and late effects Research in Lithuanian childhood cancer survivors through Education and Twinning) proposal aims to increase scientific excellence and innovation capacity at Vilnius University Hospital Santaros Klinikos (VULSK) and its affiliated entity National Cancer Institute (NCI) by scaling up research activities in Lithuanian childhood cancer survivors (CCS). The area for twinning was defined based on the emerging importance of survivorship research due to the increasing number of CCS in Europe and identified gaps in Lithuania. The key challenges addressed in the proposal are 1) lack of data on the prevalence of CCS in Lithuania exploitable in research initiatives; 2) poor transition from paediatric to adult care depriving young CCS over the age of 18 of advantages of surveillance in a research context; 3) the need to implement innovative solutions for monitoring and prevention of early and late effects through joint research activities; 4) suboptimal competences in management and administration of large-scale research projects. Four research-intensive institutions from 4 European countries will bring to the consortium their specific expertise to address identified shortcomings. The proposal aims to build on and exploit current shared research initiatives that will be leveraged to achieve SCARLET’s goal and objectives. The twinning exercise will be implemented through secondments of Lithuanian staff to the partner premises and engagement in the research activities led by partner institutions, organisation of educational events, and participation in knowledge-of-transfer meetings. As a result of the twinning activities, VULSK and NCI will improve their research profile and reputation, international visibility, and attractiveness. The expanded networking channels will facilitate Lithuanian professionals to join international research groups focused on survivorship research ultimately improving the quality of survival in Lithuania.

Effective personalized medicine for paediatric cancers must address a multitude of challenges, including domain-specific challenges. To overcome these challenges, we propose a comprehensive computational effort to combine knowledge-base, machine-learning, and mechanistic models to predict optimal standard and experimental therapies for each child. Our approach is based on virtual patient models–in-silico avatars whose analysis can inform personalized diagnostics and recommend treatments. Our platform will also allow care givers to query models and infer benefits and drawbacks for specific treatment combinations for each child. To construct these models, we will combine state-of-the-art computational methods and data from molecular assays, and clinical and preclinical studies. We will test their predictions prospectively on data from clinical trials and test therapies in pre-clinical settings. We will focus on a select panel of paediatric tumours including both high-incidence and high-risk tumour types. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. We will produce, assemble, standardize, and harmonize accessible high-quality multi-disciplinary data and leverage the potential of Big Data and HPC for the personalized treatments of European citizens. We will make our models and data available through a cloud-based platform, whose exploitation will be maximised through a collaboration with the European Open Science Cloud initiative. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.

Neuroblastoma are pediatric tumors that respond poorly to chemotherapy and have a very poor prognosis. To improve treatment options, a global development towards precision medicine is ongoing. This strongly focusses on molecular genetic target identification in tumors and subsequent assigning patients to the best trials according to their molecular profile. Still it is difficult to predict which patients will benefit from these targeted compounds. In addition, if tumors do respond to single compound targeted therapy, they almost always relapse. These tumor evolution processes could be prevented by simultaneous intervention in different activated tumor pathways. We now want to study how we can select patients that will most likely respond to a targeted compound and what combinations of targeted compounds are most effective? This can’t be tested in a clinical setting since the number of neuroblastoma patients that can be included in Phase1/2 trials is very small. Recent research shows that tumor organoids mimic human tumors and can effectively be used as xenograft in nude mice as well. These in vitro and in vivo models could be used as an alternative selection system for optimal combination treatment in a personalized approach. The overall aim is now to test if combinations of targeted compounds can cause complete remission in neuroblastoma organoid model systems to select combination treatment options for personalized clinical trials For this purpose we will generate neuroblastoma organoids that properly represents the complexity and heterogeneity of individual tumors and build a repository that represents the various subtypes of neuroblastoma tumors. We will identify synergistic compound combinations that are effective in neuroblastoma tumors that are characterized by specific molecular genetic aberrations. Thereby we will build an efficient pipeline to generate personalized models that can be used in precision medicine programs to perform compound validation.

Extrachromosomal circular DNA (ecDNA) present in cancer cells stochastically amplifies oncogenes and drug resistance genes independent from the core genome and thereby contributes to tumor adaptability and heterogeneity. While ecDNA prevalence, diversity, and biological traits have been studied, we lack insights into their expression regulation. It is unclear how it differs from chromosomal genes and how it is linked to the non-random and dynamic nuclear localization patterns observed for many ecDNA. Moreover, we are unaware of the proteins that participate in interactions with nuclear compartments to control ecDNA transcription. Most studies using sequencing and FISH methods fall short in describing these dynamic processes. However, understanding their implications is vital for designing therapies that target ecDNA expression as a whole instead of targeting individual oncogenes. To advance our knowledge, we need tools to map ecDNA transcription at high temporal and spatial resolution and systematic methods to screen for associated proteins. The aim of this project is to identify crucial overarching mechanisms and players of ecDNA transcription through an interdisciplinary approach. The Medema group has devised protocols to induce extrachromosomal amplification of drug resistance genes in diverse cellular backgrounds and genetically tag them for imaging and perturbation. The present study will build on this work to for the first time reveal the spatiotemporal co-regulation of ecDNA transcription and localization using innovative imaging reporters for measurements in single live cells and use unbiased proteomic profiling to map factors that interact with ecDNA and regulate their expression. Besides addressing outstanding questions in the field, our study will create unique cell models and datasets as a basis for future studies. Our results will furthermore inform the design of more effective treatments for the many cancer patients affected in Europe and worldwide.