Understanding the Role of Matrix Microstructure in Metastasis
Abstract
In this proposal, we aim to assist patients stricken with invasive ductal carcinoma who are estrogen receptor positive and lymph node negative upon tumor resection. The consensus of the literature and our clinical collaborators (e.g., Drs. Ping Tang and Rachel Farkas) is that this patient population has a significant problem with overtreatment because a large fraction who are treated with adjuvant chemotherapy will not experience subsequent metastasis yet are still subjected to the toxic side effects of their treatment. Unfortunately, it is currently difficult to identify exactly who those patients are. In earlier work, funded by an Era of Hope Scholar Research Award, we discovered an innovative method to predict metastatic outcome of patients with estrogen receptor positive, lymph node negative invasive ductal carcinoma. This method uses the pattern of light scattering from collagen in the excised tumor. Intuitively our discovery makes sense: the light-scattering patterns are affected by subtle ways in which individual collagen fibers are assembled, their "microstructure," and that microstructure is likely to affect, or be affected by, the passage of a tumor cell along a fiber. That project is moving towards the clinic: we are now deriving and testing the optimal predictive formulas. Next is a clinical trial using samples saved from patients along with information as to whether they did, or did not, experience a metastasis over the 10 years following removal of their tumor. However, nowhere in that project do we ask why these particular light-scattering properties of the tumor collagen, and the microstructure that influences them, are predictive of metastasis. That is what we propose to do here. In particular, we will explore the mechanisms by which the cells in a tumor define the microstructure, and the cues that guide them to do so. Understanding the underlying mechanisms by which these light-scattering properties are predictive (the "why") may uncover additional measurable predictors of metastasis, which will improve our ability to predict who will, and who will not, experience metastases and hence who should, and who should not, be treated with adjuvant chemotherapy. New predictive formulas, with the new predictors, could be rapidly incorporated into the clinical trial, use the same samples and records, and hence be rapidly moved towards the clinic. Hence, this project will serve to conquer the problems of overdiagnosis and overtreatment. Furthermore, understanding the underlying mechanisms by which collagen microstructure is predictive of metastasis (the "why") may yield novel insights into the metastatic process, which will identify why some breast cancers become life-threatening metastases. As we are exploiting a fundamentally novel way to study the metastatic process (by exploring the role of collagen microstructure), we predict that the newly discovered mechanisms driving metastasis will themselves be novel. Hence, in addition to developing our predictive formulae, we will then be poised to explore druggable targets for future anti-metastatic therapies based upon these novel insights. However, this will take significantly longer to have clinical impact than our predictive formulae described above, as considerable subsequent work will then be required. The new anti-metastatic treatments, however, could greatly impact patient survival in the future. In summary, this work will have significant impact on ending mortality due to breast cancer, in both the short and longer terms. In the short term, less than 3 years out, we can improve our ability to predict metastatic outcome and hence reduce overtreatment. In the longer term, greater than 3 years out, we can hope to find druggable targets for anti-metastatic therapies.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 31, 2017
- Source ID
- W81XWH1710015
Entities
People
- Catherine Kuo
Organizations
- United States Army
- University of Rochester