What is the Problem?

Epithelial ovarian cancer is the most lethal gynaecological malignancy, with a survival rate of about 40%. Many factors contribute to this poor prognosis, including frequent late stage diagnosis, high prevalence of aggressive histotypes, low treatment efficacy and high recurrence rates.

What is our solution?

ITHACA employs a multidisciplinary approach that combines innovative in-vitro techniques, such as organotypic models, and  an advanced computational simulator for the efficient screening of ovarian cancer therapies and the optimisation of the treatment for each patient. This personalized medicine approach has been shown to be more effective than traditional “one size fits all” methods, and ITHACA aims at exploring its full potential through the development of a pipeline for patient-specific treatment optimization.

This workflow will mainly rely on a computational simulator, developed within the project, which will be programmed with standard clinicopathological data to mirror the behaviour of each patient’s cancer. This “digital twin” will be used to test different treatment options and predict the one which is more likely to be effective. An extensive experimental validation using state of the art in-vitro technologies will enable the effective development of the simulator and improve its accuracy. While additional experimentation will be necessary, prior to the clinical application of this technology, this project will provide the scientific community with invaluable tools for the study of cancer progression an treatment, and of how they are influenced by patient-specific features.

What have we learned so far?

ITHACA is set to conclude in August 2024 so its results are still preliminary but the preliminary results are encouraging and suggest that the computational model can be adjusted  to fit the characteristics of different patients and determine response to different treatments. This is the first time this approach has been applied to ovarian cancer.

Where to from here?

The testing of the model is ongoing. It aims at verifying its accuracy and ability to capture treatment response. A wider range of treatments and a larger patient cohort will also be included in the near future to maximise the utility of this framework.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 883172.