Funded in Collaboration With Stand Up To Cancer (SU2C)
Decades of cancer research and therapeutic development have made it clear that achieving durable control of invasive solid tumors requires therapeutic combinations of a large number of drugs that target different elements within cancer cells. In aggressive cancers where cure is achievable (e.g., subtypes of leukemia and lymphoma), as many as 4-6+ drugs may be needed when administered as curative treatment to patients. This is because simpler drug combinations become ineffective due to the development of drug resistance by the tumor.
The guiding hypothesis of this project is that network-based models of cancer cell signaling together with evolutionary analyses and therapeutic data can identify a set of element within cancer cells that might eventually be exploited through therapeutic combinations to achieve a more durable control of cancer, even in the presence of tumor drug resistance. Specifically, we propose a theoretical framework that integrates so-called discrete dynamic network models and control theory with genomic evolutionary approaches. These models will be informed, tested, and iterated using experimental approaches applied to relevant cancer model systems. Based on its exemplary clinical need, we will focus on BRAF-mutant melanoma (skin cancer) and PIK3CA-mutant, estrogen receptor positive (ER+) breast cancer as initial tumor types in which to test and develop our approach. The final result will be a theoretical and experimentally validated approach that can in principle be generalized across many other therapeutic strategies.