Leveraging single-cell transcriptional data, we aim at classifying mammalian genes based on their pattern of transcriptional heterogeneity across cells of the same type, in both normal tissues and disease states (Figure 1), with the ultimate goal of identifying if these differences in variability are driven by distinct modes of regulation.

We previously identified a transcriptional signature that predicts the phenotype of those luminal breast cancer cells surviving short-term endocrine therapies (Hong et al. 2019). These cells, often called drug-tolerant persisters, will eventually lead to relapse. We aim at: 1) investigating the level of conservation of this signature across tumors of different origin and its importance in survival to different therapeutic strategies, and 2) characterizing its upstream regulators.

The genes of the so-called epigenetic regulators are often mutated or mis-regulated in cancer. We hypothesize that their aberrant activity is associated to transcriptional fluctuations that could be manipulated to the patient’s advantage (Figure 3). Based on this rationale, we are combining in vitro perturbations (directed at epigenetic regulators) with single-cell transcriptomics, to pinpoint novel treatment strategies to delay tumor progression.