Rational Design of Anticancer Drug Combinations - Stand Up To Cancer

Convergence Teams

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SU2C–National Science Foundation Drug Combinations Convergence Research Team:
Rational Design of Anticancer Drug Combinations with Dynamic Multi-Dimensional Input

Grant Term: September 2015–August 2019

This SU2C–National Science Foundation (NSF) Drug Combinations Convergence Research Team is focused on understanding cancer mutations that can be key to developing therapeutic responses. Normal cells have mechanisms for determining whether they will divide or not and whether they will die or not, and these mechanisms depend on the interaction of many different proteins. In cancer cells, however, the communications pathways among some of these proteins are distorted. This research project engages five biological and computational laboratories to identify how cancer cells distort this communication and how we can use combinations of drugs to help restore proper function.


Decades of cancer research and therapeutic development have made it clear that achieving durable control of metastatic solid tumors will usually require complex therapeutic combinations. Unfortunately, there are far too many possible combinations to test in clinical trials. Instead, new conceptual frameworks and approaches are needed to design and deliver high-order therapeutic strategies.

To address this urgent need, a collaborative team has been assembled from a broad swath of disciplines. Researchers include theoretical physicists and clinical investigators who are integrating dynamic network modeling and evolutionary analyses with systematic cell death and therapeutic resistance data. The goal is to predict the impact of complex drug combinations and to determine safe and effective dosing including how the doses should be scheduled.

More specifically, the SU2C–NSF Drug Combinations Convergence Research Team is constructing dynamic models for estrogen-receptor positive breast cancer, testing the robustness of these models, and using molecular data obtained from breast cancer patients to characterize and interpret how sensitivity and resistance to treatment evolve over time.


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