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Algorithmically-driven Quantitative Combination Cancer Therapy Engineering

Algorithmically-driven Quantitative Combination Cancer Therapy Engineering


Dan A. Landau, MD, PhD
Assistant Professor of Medicine, Division of Hematology and Medical Oncology
Weill Cornell Medicine
New York, New York


Within a single tumor there are multiple, different cancer cell subtypes. This diversity means that the cancer cells can “try out” different ways to overcome the effects of anticancer drugs and to re-emerge as a more aggressive form of the disease. Thus, despite striking initial responses, the malignancy often evolves and adapts to the therapy, leading to recurrence. Combination therapies can block the ability of the cancer to evolve around any single therapy, but deciding on which drugs to combine, and how to combine them, is a major challenge. This project is focused on a new mathematical approach in designing combination therapy for chronic lymphocytic leukemia (CLL). In one approach, the investigators will genetically engineer CLL cells to recreate the diversity of cancer cell subtypes seen in tumors and will then determine which subpopulations are resistant to treatment with single agents or a combination of agents. In their second approach, the investigators will screen for genetic differences in CLL cells taken from patients before and during therapy to characterize how cells respond differently to drugs (administered to the patients or used in laboratory experiments). The measurements from these experiments will enable advanced mathematical models of leukemia growth, taking into account the fact that each population within the leukemia responds differently to different drugs. These data-driven models are expected to generate and optimize patient-specific combination treatment plans.

Updated: May 2016