Breast cancer is the most commonly diagnosed malignancy and second leading cause of cancer death among American women. Mammography has contributed to early detection, and thus new treatments, of breast tumors, but has also generated controversies. Many patients now diagnosed at very early, symptom free stages of the disease may be likely candidates for breast-conserving treatment (BCT), but some will experience a morbid disease course and die if not promptly treated with radical intervention (mastectomy, chemo- and radiation therapies). Morphology-based histopathology has to date served effectively in assessing breast cancer patients, particularly in the age dominated by mastectomy, but it is insensitive in guiding treatment plans with BCT. In addition to morphological evaluation, a method that directly evaluates the biological behavior of individual tumors and predicts potential aggressiveness is now needed for optimal breast cancer management. To achieve this, we will test the efficacy of an ex vivo spectroscopic method of identifying cellular metabolic signatures. By providing a new paradigm for the biochemical diagnosis of breast cancer, this method can assist in the diagnosis and prognostication of breast tumors in the era of BCT. We will quantify cellular metabolic changes in the development and progression of breast cancer with high-resolution magic angle spinning (HRMAS) proton magnetic resonance spectroscopy (1HMRS). HRMAS 1HMRS can measure cellular metabolites in intact human tissue specimens, while preserving histopathological structures. We will test the ability of HRMAS 1HMRS to quantify breast cancer metabolites, correlate metabolic concentrations obtained with histopathological features measured in the same intact tissue samples, define and evaluate metabolic signatures for breast cancer according to type, grade and histopathologic stage, perform molecular biology analyses of tumor signatures at the cellular level with laser capture microdissection (LCM), reverse transcription polymerase chain reaction (RT-PCR), and establish biochemical databases that help predict tumor pathologies and patient outcome, independently of pathology. Initially, we will quantify tissue metabolites of new surgical specimens, as well as stored frozen tissues, with HRMAS 1HMRS. Observed metabolites that correlate with histopathology will then be used to create a database of breast cancer metabolite signatures. These signatures should be useful in clinical care of women with breast cancer by identifying less aggressive tumors as candidates for breast-conserving treatment.
