The primary objective of this Program Project is to generate a highly comprehensive gene expression profile of a critical early stage in human breast cancer evolution. We will specifically focus our studies to compare sets of lymph node negative (LNN) breast cancer that, although have identical morphological and histopathological characteristics, will follow significantly different clinical courses. It is of critical importance to identify prognostic factors that would assist in the decision of whether to subject LNN breast cancer patients to post-surgical systematic adjuvant treatment. Thus there is much interest in the identification and development of such prognostic tools that would allow us to better estimate the risk of disease recurrence. Our overall hypothesis is that clues to the prognosis of these lesions are reflected at the time of surgical removal in the pattern of gene expression in the primary tumor. Our ultimate goal is to identify specific """"""""gene expression signature profiles"""""""" that define subsets of tumors and that ultimately will allow to predict the clinical course of lymph node negative breast cancers. For this Program Project, we have assembled a unique team of oncologist, pathologist, molecular biologists, statisticians, computer programmers and we count with the unique patient and sample resources available at the M.D. Anderson Cancer Center. A major highlight of our studies is that two technologies-one comprehensive and one targeted-are integrated to give us a higher probability of finding key components of the molecular signatures. We felt that these state-of-the-art tools are unique suited to the task at hand: the discovery of molecular signatures. Different tools may be chosen ultimately for the analysis of gene expression signature profiles in a rapid fashion in a clinical pathology laboratory setting. Three important intermediate goals will also be achieved in the course of this Program. First, we will cross-validate two complementary gene expression technologies to obtain the most comprehensive, accurate and robust picture of the gene expression profile of lymph node negative breast cancer. Second, we will develop new, more useful statistical models, methods and software for the analysis and interpretation of global gene expression profiles. Third, we will develop an understanding of the contribution of different cell types to the overall expression profiles of breast tumors, in order to address problems of tumor heterogeneity.
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