Advances in personalized medicine have allowed for improved outcomes in cancer. However, the development of drug resistance and relapse with refractory disease remains a challenge in clinical settings. To address this problem, this multi-disciplinary project proposed by Dr. Tadele is aimed at combining computational and experimental approaches for understanding the evolutionary dynamics in heterogeneous tumors. This understanding will provide a basis for informed therapeutic interventions.
The main idea behind this proposed project is to harness the potential of collateral sensitivity tradeoffs by better understanding the dynamics of drug resistance. To this end, we will create a complete collection of cell lines containing all combinations of four clinically relevant driver mutations in small cell lung cancer. Utilizing a time lapse system, pairwise and dose-dependent fitness landscapes will be measured for this complete genotype space. Furthermore, we will utilize single cell RNA sequencing and molecular profiling to obtain a finer grained understanding of the evolutionary trajectories.
This work will be part of a long-term international collaboration between the labs of Professors Scott and Rayner, who are at the Cleveland Clinic (USA) and University of Oslo (Norway), respectively. While both labs have a well-established computational team, Scott’s lab is focused on understanding cancer evolution in the context of drug resistance, and Rayner’s lab is interested in deciphering the role of non-coding RNAs in genome evolution. This partnership therefore encompasses a non-overlapping set of expertise with shared vision allowing powerful synergy.
Dr. Tadele is optimistic that the international mobility grant will enable him to gain significant technical and theoretical insight in the field of cancer evolution. Furthermore, he strongly hopes to establish a unique niche in the Norwegian research community, creating collaboration nationally and internationally.
The evolution of drug resistance is the ultimate cause of most of patient’s deaths in oncology. To address this problem, there has been an increased interest in the research community utilizing mathematical approaches, called fitness landscapes, together with computational models, to predict cancer evolution. More recently, a novel method has been derived to control evolution: that is, drive populations of cells at arbitrary speed through specific evolutionary trajectories using an extension of these landscapes called fitness seascapes. Fitness seascapes map from genotype and drug dose to fitness, often exhibiting trade-offs in maximum fitness genotypes at different doses of the same drug. To enable this evolutionary control method, in this multi-disciplinary project we hypothesize that a panel of fully parameterized fitness seascapes can be used, in conjunction with evolutionary algorithm, to control cancer evolution.
To date, these seascapes have been made through combinatorially complete genetic engineering of specific loci in simpler organisms together with fitness measurements. To move this project into reality, it is proposed for a construction (genetic engineering) and discovery (through experimental evolution) of cancer genotype spaces together with exhaustive measurement and correlation of phenotype (across drug dose): the creation of fitness seascapes in cancer. Once measured, we will utilize novel method to prescribe a temporally-varying dosing scheme to control the evolutionary path the cancers take to resistance, and track the progress using feedback from high temporal-resolution genomics in a bioreactor. Prediction and control of evolution is a high-risk endeavor, if successful, would be paradigm changing in cancer therapy. The project manager strongly believes this proposed multi-disciplinary project will provide new understanding that benefit the cancer research community, as well as provide novel opportunities for therapeutic interventions.