Molecular detection of micrometastatic breast cancer in histopathology-negative axillary lymph nodes correlates with traditional predictors of prognosis: An interim analysis of a prospective multi-institutional cohort study

Objective: We sought to establish the clinical relevance of micrometastatic disease detected by reverse transcription polymerase chain reaction (RT-PCR) in axillary lymph nodes (ALN) of breast cancer patients. Background: The presence of ALN metastases remains one of the most valuable prognostic indicators in women with breast cancer. However, the clinical relevance of molecular detection of micrometastatic breast cancer in sentinel lymph nodes (SLN) and non-sentinel ALN has not been established. Methods: Four hundred eighty-nine patients with T1-T3 primary breast cancers were analyzed in a prospective, multi-institutional cohort study. ALN were analyzed by standard histopathology (H&E staining) and by multimarker, real-time RT-PCR analysis (mam, mamB, muc1, CEA, PSE, CK19, and PIP) designed to detect breast cancer micrometastases. Results: A positive marker signal was observed in 126 (87%) of 145 subjects with pathology-positive ALN, and in 112 (33%) of 344 subjects with pathology-negative ALN. In subjects with pathology-negative ALN, a positive marker signal was significantly associated with traditional indicators of prognosis, such as histologic grade (P = 0.0255) and St. Gallen risk category (P = 0.022). Mammaglobin was the most informative marker in the panel. Conclusion: This is the first report to show that overexpression of breast cancer-associated genes in breast cancer subjects with pathology-negative ALN correlates with traditional indicators of disease prognosis. These interim results provide strong evidence that molecular markers could serve as valid surrogates for the detection of occult micrometastases in ALN. Correlation of real-time RT-PCR analyses with disease-free survival in this patient cohort will help to define the clinical relevance of micrometastatic disease in this patient population.