The SU2C-NSF Cancer Convergence Drug Combination Dream Team Progress Update
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The SU2C-National Science Foundation Cancer Convergence Dream Team Progress Report
“Rational Design of Anticancer Drug Combinations with Dynamic Multi-Dimensional Input”
Grant Funded: January, 2016
Convergence Team Leader:
• Anthony Letai – Dana Farber Cancer Institute
• (former leader) Levi Garraway – Dana Farber Cancer Institute
• Jose Baselga – Memorial Sloan Kettering Cancer Center
• Maurizio Scaltriri – Memorial Sloan Kettering Cancer Center
• Reka Albert – Penn State University
• Raul Rabadan – Columbia University
Through collaboration of research scholars in distinct disciplines, convergence grants offer a novel model research meant to spur innovation in new ways of combating cancer. By taking advantage of advances in information technology, nanotechnology, new material research, imaging, optics, quantum physics, and other physical sciences, often considered outside the realm of traditional biomedical research, the subsequent convergence grants may provide critical outcomes to advance the fight against cancer.
Decades of experience with cancer treatment and therapeutic development have made it clear that, in most cases, effective treatment requires combinations of treatments. These combinations are becoming increasingly complex as we use treatments that specifically target certain genes, cellular processes, or work to activate the immune system. As the number of treatments in a combinatorial approach increase, it becomes difficult or impossible to thoroughly test variables such as the relative dosing of drugs or the order in which they are given. To add to the complexity, a tumor will evolve in response to treatment, often making the original combination less effective over time.
This Convergence Team is creating a method for predicting how best to treat an individual patient with combination therapies over the entire course of their disease. To do this, the Team is building a computer model of the cancer cell that can predict when to administer each treatment, at what amount and for how long. It is expected that these predictions will create treatment regimens that are far more complex that currently used, to hit the cancer’s weak spots as they arise head off evolution towards an untreatable tumor. This new understanding could prove to be an incredibly powerful way of using combinations of new and existing treatments to more effectively attack cancer cells and improve patient’s quality of life.
In order to create the model, the Team will focus on melanoma and breast cancer. The Team has three specific aims:
1. Create dynamic models for BRAF-mutant melanoma and PIK3-mutant, ER positive breast cancer. The Team will make a computer model of these cancer cells that is able to adjust and compensate to perturbations, such as inhibiting a gene function, just as real cells do. The Team will use this to identify key “nodes” in the molecular pathways that drive cancer in order to understand how it may try to evade a treatment by relying on a different pathway or node.
2. Test and iteratively improve the models. The Team will conduct experiments in cell cultures to test the robustness of the models and identify a new mechanism that the cancer cells use to escape the effects of different therapeutic combinations. This will be an iterative process, where the results from experiments will refine the model, which will then predict new hypotheses to test in cell culture.
3. Dissect the evolutionary trajectories of acquired drug resistance. The Team will analyze biopsies from melanoma and breast cancer patients taken before and after treatment in order to understand how the tumors change in response to different combinations of therapies. The genetic data collected will be combined with the computational models to create a detailed picture of how cancer cells adapt and evolve during the course of treatment.
The Team is initially focusing on a key pathway involved in cancer development. They have modeled the pathway with feedback loops and interacting gene products (Aim 1). They have begun to test early ideas and iteratively improve the models (Aim 2). In addition, the Team has begun to characterize tumor samples from melanoma and breast cancer patients, analyzing DNA mutations in order to construct preliminary evolutionary trees to describe how the tumors evolve during the course of treatment (Aim 3).
This combined work has identified several potential nodes that may confer resistance to treatments currently used in the clinic to treat melanoma and breast cancer patients. These discoveries will help to better understand the signaling circuitry that drives these cancers and, in turn, guide the design of better combination therapies to prevent relapse after treatment.
The two original leaders of this team were Levi Garraway (Dana Farber) and Jose Baselga (MSKCC). Within the first six months of the funding of the team Levi Garraway accepted a new position at Eli Lilly. After discussion with the team it was decided that one of the senior members of this team, Anthony Letai of Danna Farber Cancer Center, would assume leadership in place of Levi Garraway. This also kept the primary funding contract and reporting responsibilities at Dana Farber Cancer Center. At this meeting were both Tony Letai and Levi Garraway. The passing of leadership was smooth and Tony Letai did an excellent job keeping the team on track and productive. This transition has gone well. In addition one of Levi Garraway’s postdoctoral fellows has been appointed an assistant professor at Dana Farber Cancer Center, Nikhil Wagle. He spoke at this meeting to review his future plans and he was accepted as a young investigator in the team to continue some of the research proposed originally by Levi Garraway. There will be some internal budget rearrangements (no increase in net costs) to accommodate these changes.