# Development and validation of a computational tool to predict treatment outcomes in cells from high‐grade serous ovarian cancer patients

## TL;DR

This paper introduces a computational tool to predict treatment outcomes for high-grade serous ovarian cancer patients, aiming to improve personalized treatment selection.

## Contribution

The novel contribution is a calibrated computational simulator that acts as a digital twin for individual patients to predict treatment responses.

## Key findings

- The model was validated on cell lines and patient-derived laboratory models.
- The tool provides insights into HGSOC behavior and supports personalized medicine approaches.
- It demonstrates potential for preclinical research and treatment monitoring.

## Abstract

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 patient's response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behavior and progression. They also provide a proof of concept for its use as a personalized medicine tool and support disease monitoring and treatment selection.

In this work we have developed a computational model of High Grade Serous Ovarian Cancer that can be used to enhance in vitro experimentation and yield useful information on single cell behaviour. This tool can also be calibrated as a digital twin for individual patients and provide information on their response to specific treatments.