This Phase 1 SBIR application proposes to establish whether or not metastasis-associated proteins are upregulated in primary breast cancers that have shown the ability to become mobile and invade non-breast tissue and whether or not this phenomenon can be exploited to give indication of the aggressiveness and/or metastatic potential of earlier stage breast cancers. Predicting the clinical aggressiveness of a tumor through molecular pathology indications could have far reaching ramifications for personalized patient management. Expression Pathology Inc. (EPI) has preliminary mass spectrometry-based protein expression data strongly suggesting that specific proteins known to be involved in tumor cell attachment, invasion, and locomotion are upregulated in epithelium microdissected from primary tumors that had already shown the metastatic phenotype vs. epithelium microdissected from primary tumors that had not metastasized at time of collection. In collaboration with the Clinical Proteomics Facility-University of Pittsburgh Hillman Cancer Center, EPI expects to apply the emerging technical capabilities of global quantitative protein expression analysis with specific emphasis on these proteins in order to firmly establish that increased expression of multiple proteins from entire families of proteins that mediate the metastatic phenotype correlate directly with the metastatic status of a primary breast tumor. This will be accomplished by utilizing Liquid Tissue(r) reagents to prepare soluble tissue lysates of microdissected epithelium directly from 36 formalin fixed breast cancer tissue samples. The first set of 12 tissue samples will be from primary breast cancers in which each patient had demonstrated metastatic disease at time of presentation (Stage IV). A second separate collection of 12 primary breast cancer tissue samples will be interrogated that showed no spread to the surrounding lymph nodes at time of presentation and who, after long term follow-up, remain cancer free (Stage I). A third collection of 12 samples will be from patients whose primary tumors presented with cancer that had spread to surrounding lymph nodes at time of presentation (Stage II). Each of the 36 protein lysates will be analyzed by mass spectrometry- based global proteomic profiling and quantitative protein expression data developed using spectral count methods. Differential protein expression by mass spectrometry will be validated by immunohistochemical methods for the proteins that correlate most closely with metastatic disease. The capability to quantify multiple metastasis-associated proteins simultaneously directly in formalin fixed breast tissue is important to this application because it allows for interrogation of tissue collections with extensive clinical data, which cannot be duplicated with frozen tissue collections. This fact becomes even more prominent during a Phase 2 proposal where much larger tissue collections with extensive clinical data and follow-up need to be interrogated. Technological capabilities co-developed and already demonstrated by collaboration between these groups in the past will form the foundation for success of this proposal. Successful achievement of the stated milestones will provide for a subsequent Phase 2 proposal to explore and develop large scale quantitative protein expression applications of these metastasis-associated proteins to provide for clinical diagnostic and prognostic assays of formalin fixed pathologic tissue. The goal would be to develop the ability to predict if a primary breast tumor had already metastasized or was likely to develop metastatic capabilities. This will be achieved by mass spec quantitative proteomic technologies such as AQUA and Multiple Reaction Monitoring through an expanded collaborative between EPI, the Clinical Proteomics Facility-University of Pittsburgh Hillman Cancer Center, and the Department of Pathology-University of Pittsburgh Hillman Cancer Center involving large collections of breast cancer tissue. [unreadable] [unreadable] [unreadable]
NIH ExPorter
NIH ExPorter