Extending personalised modelling towards clinical use in multiple malignancies

Cancer treatment remains a complex challenge due to significant variability in patient responses. Even patients diagnosed with the same type of cancer can react differently to identical treatments because their cancers are driven by diverse genetic mutations. The central challenge addressed by this project is the unpredictability of cancer treatment outcomes. This variability necessitates a more personalised approach to treatment, one that accounts for the unique genetic makeup of each patient’s cancer. During the initial phase of the Future Leaders Fellowship, we developed personalised computer simulations of lymphoma, based on individual patient mutations. These simulations successfully predicted patient responses to therapies, identifying subgroups of patients who do not benefit from standard treatments. Additional work demonstrated that simulations could overcome this variability and suggest effective drugs and drug combinations. Laboratory experiments validated these predictions, confirming the potential of computational models in personalised cancer treatment. In this follow-on project, we aim to: Extend our approach beyond lymphoma to include other malignancies such as leukaemia, myeloma, and breast cancer, using publicly available data to generate patient-specific simulations and predict treatment responses across these cancers. Integrate computational and experimental methods to create a robust personalized treatment pipeline using clinically available measurements combined with modelling. We will test the effectiveness of these predictions in the experimental lab. Create a web-based resource that enables the automatic creation of personalized models through an intuitive interface. This resource will maximize the impact of the fellowship by accelerating clinical adoption and providing a powerful tool for teaching systems biology. It will also serve as a hub for establishing multiple collaborations within the research community. The ultimate goal is to revolutionise cancer care through personalised treatment strategies. By leveraging genetic data available at diagnosis, we aim to: Stratify patients by risk using computational models. Identify high-risk patients and assign them alternative treatments. This project has the potential to transform how cancer is treated, ensuring that each patient receives the most effective therapy tailored to their specific genetic profile. Such advancements could significantly improve treatment outcomes, reduce unnecessary side effects, and pave the way for personalised medicine in oncology. The development of the web-based resource will further enhance the reach and impact of this work. It will provide clinicians and researchers with easy access to personalised modelling tools, fostering a collaborative environment that accelerates the integration of these advanced methods into clinical practice. Additionally, it will serve as an educational platform, helping to train the next generation of scientists and clinicians in systems biology and personalised medicine.