Anti-cancer therapies can often kill the vast majority of tumor cells but a few rare cells remain and grow despite treatment. Non-genetic variability has emerged as a potential contributor to this behavior. However, it remains unclear what drives this variability, and what the ultimate phenotypic consequences are. We have developed a set of new single-cell barcoding technologies (Rewind and FateMap) that have enabled us to show how different types of variability can translate into different drug-resistant outcomes upon treatment with drug. We found that even a genetically and epigenetically clonal population harbors latent variability that can produce an entire ecosystem of different resistant cell types.
Arjun went to UC Berkeley, where he majored in math and physics, earned his Ph.D. in math from the Courant Institute at NYU, and did his postdoctoral training at MIT before joining the faculty at Penn Bioengineering in 2010. He is currently a professor of Bioengineering. His research focus is on the developed experimental techniques for making highly quantitative measurements in single cells and models for linking those measurements to cellular function. His ultimate goal is to achieve a quantitative understanding of the molecular underpinnings of cellular behavior.
