The goal of disease research is to develop therapeutics. While many diseases have been successfully addressed, genetics-based diseases have proven more difficult to develop therapies that bring the disease into remission without significant side effects. Doing so requires addressing both disease complexity and also the complex interactions within the whole body network that affect disease as well as response to therapies. The central theme of this Proposal is to leverage the advantages of the fruit fly Drosophila to build a `functional network platform' designed to identify therapeutics that address disease complexity. We then use stem cell approaches to explore the most promising leads in a human cell context. At each step we make a concerted effort to embrace complexity both in our models and our lead therapeutics. The overall objective of this Proposal is to further develop a platform and pipeline that can be adapted to a broad range of diseases. In the attached Projects, we describe our emerging technologies designed to weave together complementary disease models into a seamless platform designed to build drugs and personalized therapeutics rapidly and at a reasonable cost. We select two diseases-colorectal cancer and RASopathy- to develop and demonstrate the strength of our platform to address both a rare Mendelian disease and one of the most common cancers. The former example tests whether our platform can provide a rapid and cost-effective approach to orphan diseases that require sophisticated therapeutics suitable for long-term treatment. The latter provides an example of how it can embrace disease complexity to build new generation lead therapeutics for a disease that remains a key unmet need in our community. Once developed, we will offer a readily accessible standard operating procedure that will most efficiently bring our platform to the applied science community.
Genetics-based diseases have proven difficult to treat due to the complexity of the disease in the context of the whole body. We propose to provide to the community a discovery platform designed to develop drugs and identify truly personalized therapeutics in a rapid, rational, and cost effective manner.
| Ung, Peter Man-Un; Rahman, Rayees; Schlessinger, Avner (2018) Redefining the Protein Kinase Conformational Space with Machine Learning. Cell Chem Biol 25:916-924.e2 |
| Sonoshita, Masahiro; Scopton, Alex P; Ung, Peter M U et al. (2018) A whole-animal platform to advance a clinical kinase inhibitor into new disease space. Nat Chem Biol 14:291-298 |
| Das, Tirtha K; Cagan, Ross L (2018) Non-mammalian models of multiple endocrine neoplasia type 2. Endocr Relat Cancer 25:T91-T104 |
| Cagan, Ross; Meyer, Pablo (2017) Rethinking cancer: current challenges and opportunities in cancer research. Dis Model Mech 10:349-352 |
| Han, Dan; Rodriguez-Bravo, Veronica; Charytonowicz, Elizabeth et al. (2017) Targeting sarcoma tumor-initiating cells through differentiation therapy. Stem Cell Res 21:117-123 |
| Schlessinger, Avner; Abagyan, Ruben; Carlson, Heather A et al. (2017) Multi-targeting Drug Community Challenge. Cell Chem Biol 24:1434-1435 |
| Pandya, Chetanya; Uzilov, Andrew V; Bellizzi, Justin et al. (2017) Genomic profiling reveals mutational landscape in parathyroid carcinomas. JCI Insight 2:e92061 |
| Das, Tirtha Kamal; Cagan, Ross Leigh (2017) KIF5B-RET Oncoprotein Signals through a Multi-kinase Signaling Hub. Cell Rep 20:2368-2383 |
| Shukla-Dave, Amita; Castillo-Martin, Mireia; Chen, Ming et al. (2016) Ornithine Decarboxylase Is Sufficient for Prostate Tumorigenesis via Androgen Receptor Signaling. Am J Pathol 186:3131-3145 |
| Rykunov, Dmitry; Beckmann, Noam D; Li, Hui et al. (2016) A new molecular signature method for prediction of driver cancer pathways from transcriptional data. Nucleic Acids Res 44:e110 |
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