Personalised ovarian cancer treatment with digital twin technology
Cancer is an inherently complex disease. There are significant differences, not only among patients, but also within individual tumours. This variability often leads to different, unexpected responses to treatment, complicating the clinical management of the disease. Standardised therapies, which work well for homogeneous conditions, are frequently insufficient for cancers with this level of complexity. In this context, personalised medicine has emerged as the most promising approach, tailoring treatments to the unique biological makeup of each patient. However, despite its recognised potential, clinical application of personalised medicine remains limited due to the complexity and high costs compared to standard care.
Personalised medicine in action
With support of the Marie Skłodowska-Curie Actions (MSCA) programme, the ITHACA project has taken significant steps toward realising the potential of personalised medicine, focusing on improving treatment outcomes for ovarian cancer patients. High-grade serous ovarian cancer (HGSOC) is characterised by low survival rates, high recurrence, and frequent drug resistance. The project aimed at demonstrating how computational tools can bridge critical gaps in personalised medicine of HGSOC. “We wanted to develop a novel framework to assess the efficacy of various ovarian cancer therapies and identify optimal treatments for individual patients,” explains MSCA research fellow Marilisa Cortesi.
Digital twin
Researchers developed ALISON, a digital twin simulator that recreates the tissue environment of the abdominal cavity, the primary site of HGSOC metastasis. According to Cortesi: “ALISON is more than an anatomical model. It integrates biological behaviour of the different cell types of the tissue, considering how the concentration of relevant molecules (oxygen, glucose, waste products) changes over time.” Unlike traditional computational models which assume uniform cell behaviour, ALISON assigns distinct behavioural profiles to individual cells, reflecting real-life variability. It computes cell activities, such as division rates and interactions among different cell types. By simulating the evolution of the tissue, researchers can explore how cells respond to varying conditions including drug treatments. These simulations provide insights into treatment efficacy by tracking cancer cell behaviour and distribution over time. This innovation successfully replicated experimental findings linking HGSOC progression to increasing diversity among cancer cells. ITHACA calibrated the digital twin using standard clinical data. This technique generates virtual cancer cell populations that mimic a patient’s disease characteristics. While further validation with larger patient cohorts is necessary, this approach offers a promising avenue for guiding treatment selection, reducing reliance on trial-and-error methods.
A vision for the future of ALISON
“Digital twins, such as ALISON, hold transformative potential for cancer therapy. They promise to become essential clinical tools, predicting treatment responses and assessing side effects,” highlights Cortesi. This holistic approach could enhance quality of life, especially as cancer increasingly shifts toward being a manageable chronic condition. In research, digital twins can complement traditional experimental models. They offer cost-effective and detailed analyses, including scenarios that are difficult to replicate in vitro or in vivo. For example, ALISON can simulate rare patient characteristics and predict their impact on therapy outcomes. This capacity to explore unique conditions underscores the role of computational models in accelerating drug development and improving early clinical trial success rates. Furthermore, with ALISON’s code freely available on GitHub, the project sets the stage for broader scientific exploration beyond ovarian cancer.
Keywords
ITHACA, treatment, ovarian cancer, digital twin, simulation