Disrupting Resistance to Targeted Therapies in NSCLC with Evolutionary Informed Therapies
Abstract
Due to their high highly efficiency and relatively low toxicity, targeted therapies are used as front-line treatments for the patients with “druggable” oncogenic mutations, such as those affecting EGFR and ALK. Unfortunately, these therapies are rarely curative in advanced metastatic cancers. The reason for the eventual failure is that, unless we manage to quickly eliminate all of the tumor cells (which almost never happens in these advanced cancers because of intra-tumor heterogeneity), the surviving tumor cells can evolve under the selective pressures imposed by these treatments, eventually developing genetic and non-genetic adaptations that make them resistant to these drugs, which translates into clinical relapse. Even though we know that tumors are plastic and evolve, they are treated as static entities, with adjustments made only after cancers stop responding to our best drugs. Can we treat cancers proactively rather than reactively, anticipating and blocking the evolution of resistance? The recent success of an adaptive therapy clinical trial in prostate cancer suggests that the answer is yes. While evolutionary informed therapies do not fit into a “one size fits all” template, we believe that it is possible to develop and apply conceptually similar approaches in targetable lung cancers. But in order to achieve this, we first need to understand how resistance evolves and capture this understanding in mathematical models that could not only recapitulate experimental and clinical observations, but also to inform therapeutic approaches. Current therapies are optimized to achieve maximal short term tumor cell kill. Whereas quick elimination of maximal numbers of tumor cells might be highly beneficial in the short-term, continuous treatment with the highest tolerated doses of the drugs (current clinical approach) is likely to be sub-optimal in the long run, as it ensures that the eventual selection of resistant sub-populations. Mathematically captured understanding of how resistance evolves might enable us to predict therapeutic strategies, optimized to achieve long-term decease control, optimizing long-term progression free and overall survival. Our proposal is based on true integration of experimental and mathematical studies, where experiments with preclinical mouse models are designed to maximize our ability to extract information that is crucial for the development of accurate mathematical models. Then, in turn, the experiments will be used to test accuracy and validity of the models, with changes made in the assumptions/parameters until satisfactory accuracy is achieved. The result is a framework that will allow for the optimization of the schedules of targeted treatments for NSCLC patients. We believe that this approach could qualitatively transform our understanding of the underlying evolutionary dynamics, allowing us to predict therapeutic outcomes and therefore perform optimization toward achieving better long-term responses. Whereas our experimental work is limited to interrogating the resistance to the front-line tyrosine kinase inhibitor therapies, its inferences, captured in mathematical models, will be applicable to tackling the problem of resistance to back up therapies as well. While we are not pursing an unrealistic goal of finding a “silver bullet” to quickly cure, we are also not aiming to achieve an incremental advance that could briefly benefit a small subset of patients. Instead, should our studies succeed, it will open doors toward significant improvement of clinical outcomes with the pharmacological tools already approved for clinical use, as those as those that are currently under development.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010451
Entities
People
- Andriy Marusyk
Organizations
- H. Lee Moffitt Cancer Center & Research Institute
- United States Army