NOVEL TOOLS TO PREDICT AND PREVENT THE EMERGENCE OF RESISTANCE
TO TARGETED DRUGS AND RADIATION THERAPY
Project Co-Leaders: William Pao, MD, PhD, Franziska Michor, PhD
Some cancers are exquisitely sensitive to anti-cancer treatment. For example, patients whose lung adenocarcinomas harbor specific mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain frequently experience clinical and radiographic responses to the selective EGFR tyrosine kinase inhibitors (TKIs) gefitinib (Iressa) and erlotinib (Tarceva). Medulloblastomas analogously are extremely sensitive to radiation treatment. However, in both instances, the disease returns. In half of such lung cancer patients, the Pao Lab has demonstrated that tumor cells harbor a second mutation in the EGFR kinase domain, which alters a ‘gatekeeper’ residue (T790M) in the ATP-binding pocket. Another 20% of patients develop tumors with amplification of the gene encoding another kinase, MET. In patients with medulloblastoma, data from the Holland Lab suggests that radiation-resistant cells in the perivascular niche undergo G1 arrest in response to treatment and then self-renew, giving rise to recurrence. Since acquired resistance to either targeted or radiation therapies represent severe limitations, and since existing treatment schedules were established empirically, we propose an interdisciplinary approach utilizing mathematical modeling and unique experimental systems to predict and prevent the emergence of resistance against targeted drugs and radiation therapy. We have already developed a mathematical framework for the general scenario of drug resistance emerging during therapy with targeted drugs, based upon simple growth and death rates. We now will: 1) broaden the mathematical framework to include more complex scenarios in cancer therapy, such as considerations of the effects of treatment on the cell cycle, the affect of treatments on synchronized versus non-synchronized cell populations, the dynamics of resistance emerging due to multiple mutations, the effects of therapy with several drugs administered sequentially or in combination, and the response of cell populations to radiation therapy; 2) apply the models to minimize the risk of resistance to EGFR TKIs in lung cancer, using quantitative measurements obtained from appropriate isogenic cell lines and in vivo transgenic lung tumor models; 3) apply the models to minimize the risk of resistance to radiation in medulloblastoma, using quantitative measurements obtained from a novel transgenic mouse model. Collectively, these studies are expected to lead to the rational design of clinical trials to prevent the emergence of resistance in patients treated with targeted drugs or radiation therapy. This project is cross-disciplinary (mathematical modeling (Michor) and cancer biology (Pao/Holland)), and will rely upon physical measurements obtained within biological labs.