The overwhelming majority of deaths from cancer are attributable to metastasis, rather than growth of the primary tumor. In breast cancer, metastatic recurrence can occur years to decades after apparently successful surgery. Current methods do not allow individualized assessment of metastatic recurrence risk nor do they offer effective therapies for metastatic breast cancer patients. Breast cancer presents a unique research opportunity because the long interval between surgery and recurrence offers the potential to improve patient outcomes if effective anti-metastatic therapies could be developed. However, few drug discovery efforts to date have focused on the metastatic process specifically. The challenges we address are developing and applying methods to identify the basic mechanisms of metastasis, then prioritizing and validating genes and proteins as potential therapeutic targets. Our approach combines advances in experimental (Ewald) and computational (Bader) methods that we have developed to interrogate the metastatic process and to systematically dissect the genetic basis of human disease. Experimentally, we will use a pipeline that relies on organoids from primary human breast cancer tissue to model several distinct steps of metastasis: invasion into the surrounding matrix, dissemination of cancer cell clusters, and outgrowth of these clusters molecular models of distant organs. Computationally, we have developed and applied powerful methods to connect quantitative traits to their genetic basis across multiple complex human disease. We will now apply these computational methods to dissect the molecular basis of breast cancer metastasis. The central insight of our proposal is that the known heterogeneity of breast tumors, while confounding to other methods, enables our quantitative trait loci approach. We will exploit this heterogeneity with computational methods that have the potential to identify the molecular differences between primary human breast tumor organoids that demonstrate metastatic vs. non-metastatic cell behaviors (Aim 1). We will use network analysis techniques to prioritize these as targets, and then use a combination of mammalian genetic engineering and small molecule perturbations to validate targets first in the organoid system and then in accepted mouse PDX models for metastatic growth (Aim 2). Finally, we will combine our novel target based approaches with chemical and genetic perturbagens from the CTD2 Network and broader drug discovery efforts (Aim 3). In this way, we can build on existing knowledge to accelerate our progress towards improved patient outcomes. Success of this program will provide clinically actionable targets for preventing metastatic recurrence or treating patients with established breast cancer metastases. Importantly, our approaches can provide a general platform for dissecting metastasis across epithelial cancers.
Our project will develop and apply methods to identify and validate new molecular targets for anti-metastatic breast cancer therapy. We will combine organoids developed from primary human breast tumors with real-time imaging to convert the process of cancer progression into quantitative phenotypes that can be dissected systematically by genomics technologies. Targets discovered by this method will be validated using genetic perturbations in organoid and mouse PDX models, and in select cases with existing small molecule perturbants. Our methods are generally applicable to other carcinomas.
| Sirka, Orit Katarina; Shamir, Eliah R; Ewald, Andrew J (2018) Myoepithelial cells are a dynamic barrier to epithelial dissemination. J Cell Biol 217:3368-3381 |
| Neumann, Neil M; Perrone, Matthew C; Veldhuis, Jim H et al. (2018) Coordination of Receptor Tyrosine Kinase Signaling and Interfacial Tension Dynamics Drives Radial Intercalation and Tube Elongation. Dev Cell 45:67-82.e6 |
| Ewald, Andrew J (2018) Metastasis inside-out: dissemination of cancer cell clusters with inverted polarity. EMBO J 37: |
