Barlow, E. | bioRxiv Science

bioRxiv2024

Development and validation of a computational tool to predict treatment outcomes in cells from High-Grade Serous Ovarian Cancer patients

Treatment of High-Grade Serous Ovarian Cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient-derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patients response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behaviour and progression. They also provide a proof of concept for its use as a personalised medicine tool and support disease monitoring and treatment selection.

bioengineering↗

bioRxiv2023

Accurate identification of cancer cells in complex pre-clinical models using deep-learning: a transfection free approach.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment, while allowing control of the density and type of cells included in the analysis, as well as the experimental conditions in which they are maintained. More widespread application of these models is hampered however by the limited technologies available for their analysis. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, we present ORACLE, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that we have conducted includes multiple cell lines and patient derived cultures to characterise the effect of all the major potential confounding factors. High accuracy and reliability were maintained throughout the analysis demonstrating ORACLE effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognise both ovarian cancer cell lines and primary patient-derived cells. This feature sets ORACLE apart from currently available analysis methods and opens the possibility of analysing in vitro co-cultures comprised solely of patient-derived cells.

bioengineering↗