The trajectory of cancer progression is shaped by evolutionary processes on a variety of time scales. Evolutionary selection forces drive populations towards more fit phenotypes over generations, yet individual cancer cells can escape these forces by modifying their intracellular signaling on short times scales. While these facts are becoming more and more accepted, little, if any, of this knowledge has been translated into clinically actionable information. And, more frustratingly, it has failed to change the pursuit of ‘silver bullets’: drugs that act on single targets awash in a sea of heterogeneity. We submit that even if such approaches yield new drugs, tumor heterogeneity assures these effects will be either partial, not durable, or both. Here I propose to use a multi-faceted approach which embraces our new evolutionary understanding of cancer, focusing on the evolutionary mechanisms themselves, rather than pursuing traditional mechanisms of resistance. I hypothesize that learning and perturbing evolutionary mechanisms of drug resistance will provide clinically meaningful gains without the need for new drug discovery. While the tenets of this strategy can be applied to any cancer, for both pragmatic and scientific reasons I focus my efforts on Ewing Sarcoma (ES), a cancer which I actively treat in adults and adolescent and young adult (AYA) patients alike. ES is a frustrating disease in that there is a well described translocation (EWS-FLI), but to date no successful targeted therapy (Uren 2005). Further, I have experience in my laboratory with experimental evolution using ES lines in vitro, in analyzing their resultant RNAseq and miRseq, and have begun applying mathematical models to chart the course of tumor progression. My published preliminary studies in other cancers and pilot data in ES support the premise that the evolutionary mechanisms to be studied play key roles in the emergence of drug resistance. To improve management for patients with ES, my investigation will progress through two orthogonal, synergistic, multi-disciplinary specific aims. Each aim utilizes a distinct, evolutionarily informed rationale and design. To approach these aims I will pursue the following objectives: (i) map evolutionary trajectories to convergent phenotypic states uncovered by experimental evolution (ii) identify classes of convergent phenotypes considering approved chemo- and radiotherapy and finally (iii) utilize seed-based and interactome-focused data-science methods to derive actionable molecular signatures and validate in vitro.
Jacob Scott, MD, Cleveland Clinic
Recipient of the: $50,000 Zach Cohen Memorial Research Award