Cancer Modeling and New Drug Target Identification

Understanding cancer growth using scalable 3D spheroid models: A major area of interest in the lab is to identify key drivers of cancer growth which can be potential therapeutic targets, individually or in combination. We have worked with a number of labs to establish tumor growth screens in cell lines, organoids, and mice, each of which has distinct advantages in either scalability or accuracy in recapitulating key features of tumors. We recently found that relatively simple modifications to cell culture, in which cancer cells are grown as 3D spheres, result in dramatic improvements in our ability to accurately model cancer. Phenotypes in 3D spheroids much more closely mirror those observed in actual tumors in mice or in patients, and yet retain the scalability needed to probe the enormous space of potential therapeutic target combinations. These models are also very easy to manipulate, and we are interested in understanding how varying nutrient conditions, extracellular matrix materials, and immune composition of simple in vitro models can be tuned to improve model accuracy.

Dissecting mechanisms of drug action: While we are deeply interested in identifying new drug targets, many existing drugs work by unknown or poorly understood mechanisms. Therefore we have pursued a number of strategies to determine the targets and mechanisms of actions for diverse types of drugs. We have found that this process illuminates fundamental biology, and also provides a more rational path to improved therapeutic development. For example, we used shRNA and CRISPR libraries to identify the target and mechanism of action for a host-targeting antiviral drug (with no known MOA). In other work, we studied how antibody-drug conjugates (billion-dollar anti-cancer therapeutics) gain entry into cells and are metabolized in the lysosome, revealing candidate targets to enhance efficacy. We’ve also used targeted mutagenesis of a candidate target to understand mechanisms of drug resistance (by identifying mutations that confer drug resistance).