The standard of care for intermediate stage (Stage II and III) non-small cell lung (NSCLC) cancer is treatment via resection of the affected portion of the lung, followed by chemotherapy with taxane/platinum derivatives to prevent relapse. Unfortunately, despite these treatments, the cancer recurs in a large percentage of these patients, presumably due to resistance to chemotherapy. In order to avoid the toxic exposure associated with needless treatment, it would be beneficial to know a priori which patients are expected to benefit from chemotherapy post treatment. In addition, this knowledge would allow non-responding patients to be treated at the outset with second-line therapies, thus gaining valuable time to pursue a cure. It is non-trivial, however, to make this determination for the tumor as a system, as the factors involved include a variety of characteristics (e.g., cell proliferation, angiogenic and metabolic activity) that span a wide range of physical (nm to cm) and temporal (second to month) scales. We propose to distill tumor characteristics that are measurable from patients into parameter values of a mathematical model that provides a quantitative perspective needed for system analysis. We apply our experience in metabolomics analysis from live tissue and biopsies, imaging evaluation of tissue hypoxia and glucose uptake, in vitro techniques for 3D cell culture, and advanced mathematical modeling integrated with experimental data to predict the patient response to chemotherapy. Our hypothesis is that the combination of patient tumor analysis and mathematical modeling provides a system framework to predict response to chemotherapy post lung tumor resection. A computational model of tumor growth will be applied to describe cell proliferation, apoptosis, and necrosis of lung lesions, and to include glucose, oxygen, growth factor, and drug transport and uptake. We plan to extract cells with biopsy-proven NSCLC, obtained from surgical lobectomies as part of standard oncologic treatment protocols, into a 3D cell culture system. FDG-PET analysis before surgery will help establish a baseline for glucose uptake specific to these cells. This measurement along with tissue metabolomic information in vivo will be correlated to the metabolic activity and drug response observed in the cell culture. Samples will be subjected to physiologically- relevant dosages of cisplatin and paclitaxel chemotherapy or to molecular targeted therapy via erlotinib in case of epidermal growth factor receptor (EGFR) over-expression. These data will be combined with the mathematical model to project tumor response to chemotherapy, and the projections will be compared to actual patient outcomes. This project will thus establish a first step towards an integrated patient data/computational framework to predict response to chemotherapy post lung tumor resection.
Patients with intermediate stage non-small cell lung (NSCLC) cancer are treated by removing the affected portion of the lung, followed by chemotherapy. Unfortunately, the cancer recurs for many of these patients, presumably due to resistance to chemotherapy; it would be beneficial to know beforehand which patients would benefit from chemotherapy and which would not, so that everybody is treated faster and with the most suitable medicines. We propose to combine detailed analysis of patient tumors with advanced mathematical modeling to predict the response to chemotherapy - the combination of experimental data and computational simulation will provide a system for more efficient patient evaluation.
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