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

Abstract

Abstract3D co-cultures are key tools forin vitrobiomedical research as they recapitulate more closely thein vivoenvironment, 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 analysingin vitroco-cultures comprised solely of patient-derived cells.