NSF Award
1545793
This award is part of the NSF effort to promote significant advances in the fundamental understanding of cancer biology made possible through multidisciplinary research that involves experts in theoretical physics, applied mathematics, and computer science.<br/><br/>The last two decades have seen the development of increasingly effective cancer therapies that target different facets of transformed cells, including aberrant proliferation/survival, immune evasion, hyper-activated signaling pathways and dysregulated transcriptional programs. In a subset of cancers, including acute myeloid leukemia (AML) and non-small cell lung cancer with specific mutations, these therapies lead to dramatic clinical responses in a significant proportion of patients. However, in the majority of AML and subset of lung cancer patients who respond to anti-cancer therapies, therapeutic relapse subsequently ensues, although often after a considerable interval, such that these responses do not lead to long-term cures. In this project the PIs will use theoretical physics and mathematical modeling approaches to investigate the process of response to treatment, the basis for persistence of a subset of cells during clinical response, and the mechanisms driving subsequent therapeutic relapse in these two tumor types. This integrative approach will involve genomic, transcriptional, and phenotypic assays of tumors at the various stages of therapeutic response and resistance. As such, this project is expected to lead to a more fundamental understanding of the evolution of drug resistance and inform the development of novel therapeutic strategies aimed to prevent the emergence of clinical resistance. Although the efforts in this project will focus on lung cancer and AML, the results and approaches described herein will have broader relevance to oncology and are aimed to uncover general principles and models, which are relevant to the spectrum of human cancers. <br/><br/>The integrative approach in this project will lead to major advances in the understanding of the genetic and epigenetic evolution of cancer. The studies into the genetic and mechanistic basis for therapeutic relapse, and the detailed genetic, epigenetic, and functional studies of EGFR mutant lung cancer and AML patient samples before therapy, at the time of maximal clinical response, and at disease relapse will allow the PIs to obtain detailed datasets from DNA sequencing and gene expression profiling to probe the dynamics of the genetic and epigenetic diversity at different phases of disease. In turn, this will guide the development of quantitative models of the evolutionary processes that can then be tested in the laboratory. The interplay between the modeling and data will guide strategies for future data collection and in vitro experiments to functionally test hypotheses that emanate from the modeling studies.
