ABSTRACT
Targeted therapies for cancer are a promising class of drugs that inhibit the specific molecular alterations that underlie the uncontrolled proliferation seen in cancer. The primary shortcoming of targeted therapy is disease relapse, which is driven by a subpopulation of cells that are resistant to these drugs. This phenomenon is generally thought to be genetic in origin; however, our recent work on melanoma shows that non-genetic cellular plasticity may provide a mechanism of resistance to these therapies. Furthermore, we showed that through the addition of the drug itself, cells transition from this transient plasticity into a new, stably resistant cell state via cellular reprogramming, suggesting that the time an individual cell exists in a state is important for producing the divergent resistance phenotype. However, there are currently no methods available to quantify the timescale of these fluctuations for the whole transcriptome. Thus, broadly generalizing this concept of timescales for cellular plasticity, we developed a novel method for genome-wide quantification of the timescales of gene expression memory based on a modern version of the ingenious Luria-Delbrück fluctuation analysis. In melanoma, this method revealed the gene expression state of rare cells resistant to targeted therapy. In a completely new model, triple negative breast cancer, this method revealed a novel rare subpopulation of cells exhibiting resistance to chemotherapy. More generally, this method has the potential to reveal other new phenotypes associated with rare cell biology including metastasis and the early stages of stem cell differentiation. Taken together, our findings in melanoma and novel methods for studying cellular plasticity outline a framework for using single-cell technologies for applications in basic biology and clinical medicine.
Hosts:
Hanjoong Jo, Ph.D.
Kyle Allison, Ph.D.