Cancer in children is rare but when it happens, clinically prescribed treatment options are not always as efficient as one would hope. Of those that are cured, a substantial proportion suffer long-term serious health consequences from the intensive treatments that are currently required. A major reason for cancer being so difficult to treat effectively is that cancer cells undergo many random changes, which means that each cancer has an essentially unique combination of molecular characteristics. To address this problem, it is important to develop ways of specifically tailoring treatment combinations for the molecular profile of each individual cancer, to maximize cures and to minimize short- and long-term treatment side-effects.
The project team will focus on identifying effective personalized medicine for paediatric cancers and will address a multitude of challenges. To meet these challenges, a comprehensive computational effort to combine knowledge base, machine-learning, and mechanistic models to predict optimal standard and experimental therapies for each child will be proposed. 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. While the ever-present complexities of cancer continue to challenge our scientific community, it is reassuring that European projects like iPC are using the latest technology and brightest minds to find solutions which, in turn, usher in better patient care.