Label-free melanoma phenotype classification using AI-based morphological profiling
Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Within tumors, cells coexist in different mutable phenotypes that exhibit differential functional properties and drug responses. The definition of these phenotypic states has been challenging to rigorously define with conventional marker-based methods, and more high-parameter molecular methods are cell-destructive, labor-intensive, and can take days to weeks to obtain a readout. To overcome these technical and practical limitations, we utilized the Deepcell platform to perform real-time classification of unlabeled melanoma cells into Melanocytic and Mesenchymal phenotypes. We used 19 patient-derived cell lines with known Melanocytic or Mesenchymal transcription scores to develop the 'Melanoma Phenotype Classifier' to phenotype melanoma cells based on morphology alone. A Classifier accuracy of >88% was achieved, and morphology analysis of the images revealed distinct morphotypes for each phenotype, highlighting distinct morphological differences. To further link phenotypic state with multi-dimensional morphological profiles, we performed genetic and chemical perturbations known to shift the phenotypic state. The AI Classifier successfully predicted shifts in phenotype driven by the perturbations. These results further demonstrate how phenotype is linked to distinct morphological changes that are detectable by AI. Lastly, we applied the Melanoma Phenotype Classifier to dissociated biopsy samples, which revealed phenotypic heterogeneity that was confirmed by single cell RNASeq. This work establishes a link between morphology and Melanoma phenotype, and lays the groundwork for the use of morphology as a label-free method of phenotyping viable melanoma cells combined with additional analyses. Overall design: Cell line samples and dissociated biopsy samples were processed with the Deepcell device and analyzed using scRNA-sequencing