Periodic Reporting for period 1 - ITHACA (Digital Twin Technology to Predict Individual Response to Pharmacological Treatments in Ovarian Cancer)
Período documentado: 2021-09-01 hasta 2023-08-31
High-grade serous ovarian cancer (HGSOC) is characterised by low 5-year survival (43%), high rates of recurrence, and frequent insurgence of drug resistance. Beside the terrible human cost of this disease, HGSOC has a substantial societal burden with excess costs estimated at tens of thousands of euros per patient.
Treatment personalisation has been shown in multiple settings to improve outcomes, but its application in HGSOC is hampered by the limited number of established markers of treatment response.
ITHACA aims to bridge this gap by developing a computational framework that relies on patient-specific features to calibrate a digital twin and infer the response to different therapeutic options.
Treatment personalisation has been shown in multiple settings to improve outcomes, but its application in HGSOC is hampered by the limited number of established markers of treatment response.
ITHACA aims to bridge this gap by developing a computational framework that relies on patient-specific features to calibrate a digital twin and infer the response to different therapeutic options.
ALISON was created. It combines a finite element and an agent based model to describe the concentration of relevant molecules throughout the virtual tissue and the behaviour of individual cells. ALISON is fully programmable and integrates cell-cell variability.
Results obtained using ALISON have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford
A panel of HGSOC cell lines was used to characterise in vitro behaviour at different disease stages. A preliminary analysis, conducted only in PEO4 cells, has been recently published.
“ A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models” Cortesi M., Liu D., Yee C., Marsh D., and Ford C. (2023) Accepted for publication by Scientific Reports.
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford. Presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
The dataset used for the analysis is also freely available on Zenodo (doi: 10.5281/zenodo.7939591).
A cohort of HGSOC patients was recruited. HGSOC cells were isolated and dose response curves were measured. Relevant clinical data were also collected.
Results obtained from these primary samples have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford.
A digital twin calibration procedure was also developed. It relies on common clinical information to identify the parameters that better match each patient.
Results obtained using this procedure, or an earlier version, have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford.
Results obtained using ALISON have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford
A panel of HGSOC cell lines was used to characterise in vitro behaviour at different disease stages. A preliminary analysis, conducted only in PEO4 cells, has been recently published.
“ A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models” Cortesi M., Liu D., Yee C., Marsh D., and Ford C. (2023) Accepted for publication by Scientific Reports.
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford. Presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
The dataset used for the analysis is also freely available on Zenodo (doi: 10.5281/zenodo.7939591).
A cohort of HGSOC patients was recruited. HGSOC cells were isolated and dose response curves were measured. Relevant clinical data were also collected.
Results obtained from these primary samples have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford.
A digital twin calibration procedure was also developed. It relies on common clinical information to identify the parameters that better match each patient.
Results obtained using this procedure, or an earlier version, have been presented at the Annual Scientific Meeting of ANZGOG (March 2023, Brisbane, AU), the UNSW Cancer Symposium (June 2023 Sydney) and the Annual NSW cancer conference (September 2023 Sydney AU).
- “Computational modelling and simulations as tools for treatment personalisation in ovarian cancer” M.Cortesi D. Liu, E. Powell, K. Warton and C. Ford.
ALISON is the first digital twin calibrator and computational simulator able to recapitulate treatment response in vitro for both cell lines and patient-derived HGSOC primary cultures. It demonstrates that outcome inference from standard clinical information is feasible and while further development is required to improve accuracy and ensure reliability, it supports the usefulness of computational models in personalised medicine and their ability to recapitulate complex phenomena in a controlled way.
The development of this tool will continue during the final year of the project and it will focus on two main areas:
ALISON accuracy study. Aimed at evaluating the performance of this tool and testing its dependence on the available clinical information.
Expansion of the library of supported treatments.
The development of this tool will continue during the final year of the project and it will focus on two main areas:
ALISON accuracy study. Aimed at evaluating the performance of this tool and testing its dependence on the available clinical information.
Expansion of the library of supported treatments.