Non-genetic plasticity as driver of melanoma intra-tumor heterogeneity and metastatic dissemination

Metastasis is responsible for 90% of cancer-related deaths. An incomplete view of the mechanisms that drive metastasis has been a major barrier to rational development of effective therapeutics and prognostic diagnostics for patients. There is increasing evidence that this multi-step process involves reversible non-genetic reprogramming events allowing cancer cells to acquire diverse phenotypic features needed to migrate, invade, intra/extra-vasate and actively adapt to the varying environment (stress) they encounter. Understanding metastasis therefore requires methodologies that capture the magnitude and dynamics of nongenetic reprogramming in 4D (space and time) at the single-cell resolution. The advent of reliable single-cell multi-Omics analytical tools allows the simultaneous profiling of single cell’s (epi)-genome and transcriptome. Integrating single-cell profiling with lineage tracing provides a robust framework for defining cell fate transitions, intermediate states and trajectory inference. Using such tools, I propose to perform a longitudinal and exhaustive analysis of the diversity and trajectories of melanoma cell states during metastatic dissemination using a clinically-relevant mouse model of melanoma, a disease with a very high metastatic propensity. The gene regulatory networks underlying the identified metastatic cell states will be deciphered to develop therapeutic modalities targeting drivers of state switching that contribute to metastasis.