High-content imaging of primary chronic lymphocytic leukemia cells predicts patient cohorts with distinct cellular drug responses.

Cancer precision medicine benefits from identifying biomarkers that can predict therapy response. However, within a population of chronic lymphocytic leukemia (CLL) patients, there is heterogeneity that is inherent to the disease and also between patients. This heterogeneity, usually explained at the level of genetic and epigenetic abnormalities, obscures conventional potential biomarkers. As an alternative, confocal microscopy of live primary CLL patient samples in a microenvironment model that mimics proliferation centers was used to identify morphological features that define cellular phenotypes that can be used as alternative biomarkers. Applying machine learning to micrographs of 133 patient samples revealed five stable patient clusters, not discernible by standard clinical methods. Within clusters, CLL patient samples responded similarly to drugs, suggesting that live cell imaging could be used to stratify patients and predict drug responses for rational treatment design.