Modeling cancer in vivo

In collaboration with the lab of Monte Winslow we co-developed a tumor initiation and barcoding system (Tuba-seq) that allows us to generate and follow thousands of tumors with precisely engineered driver mutations in the mouse lung. We have used this approach to quantify effects of multiple driver mutations (here and here), model their epistatic effects, and study how the fitness of drivers shifts in response to tumor therapies (here and here). We are improving the Tuba-seq technology and extending our studies to high throughput studies of the fitness landscapes of tumor suppression in the face of environmental shifts due to 1) aging, 2) immune system modulation, and 3) proteotoxic stress. We are also extending to other oncogenic drivers, to understanding the frequency of overdominance in tumor suppressive function, and to genome-wide screens for tumor suppressors and potential drug targets.

Genomic studies of cancer as an evolutionary process

Cancer develops from an accumulation of somatic mutations over time. While a small subset of these mutations drive tumor progression, the vast majority of remaining mutations, known as passengers, don’t help and might even hinder cancer growth. The number of passengers in a tumor can vary by over four orders of magnitude, even within the same cancer type, from just a few to tens of thousands of point mutations.

The role that passengers play in tumor progression has traditionally received little attention despite their abundance and diversity. Some have argued that passengers are functionally unimportant to tumors given that most non-synonymous mutations are not removed by negative selection in somatic tissues and thus might be thought to be selectively neutral (Bakhoum and Landau 2017; Martincorena et al. 2018; and many others). However, the notion that non-synonymous mutations are selectively neutral in somatic tissues is surprising given that non-synonymous mutations are known to be damaging due to their effects on protein folding and stability, and these damaging effects ought to be shared between somatic and germline evolution.

Indeed, recently we provided strong evidence that passenger mutations generate strong protein misfolding stress, especially in the late stage high mutational burden tumors, and that viability of high mutational load tumors is strongly dependent on the up-regulation of complexes that degrade and refold proteins (here and here). We are continuing this work with a specific focus on discovering phenotypic and genetic changes that are necessary for tumors to thrive in face of the large protein misfolding stress and looking for ways that we can exacerbate the stress further and exploit what we believe is a virtually universal vulnerability of cancer.