Search

There is increasing evidence that molecular detection of micrometastatic breast cancer in the axillary lymph nodes (ALN) of breast cancer patients can improve staging. Molecular analyses of samples obtained from the Minimally Invasive Molecular Staging of Breast Cancer Trial (n = 489 patients) indicate that whereas the majority of molecular markers are informative for the detection of metastatic breast cancer (significant disease burden), only a few are sensitive for the detection of micrometastatic disease (limited disease burden). Frequency distribution and linear regression analyses reveal that relative levels of gene expression are highly correlated with apparent sensitivity for the detection of micrometastic breast cancer (P < 0.05). These data provides statistical validation of the concept that the most informative markers for detection of micrometastatic disease are those that are most highly expressed in metastatic disease. To test this hypothesis, we developed an innovative microarray strategy. RNA from a metastatic breast cancer ALN was diluted into RNA from a normal lymph node and analyzed using Affymetrix microarrays. Expression analysis indicated that only two genes [mammaglobin (mam) and trefoil factor 1 (TFF1)] were significantly overexpressed at a dilution of 1:50. Real-time reverse transcription-PCR analysis of pathology-negative ALN (n = 72) confirm that of all the markers tested, mam and TFF1 have the highest apparent sensitivity for detection of micrometastatic breast cancer. We conclude that a dilutional microarray approach is a simple and reliable method for the identification of informative molecular markers for the detection of micrometastatic cancer.

Esophageal adenocarcinoma (EA) is increasing faster than any other cancer in the U.S. In this report, we first show that EA can be distinguished from normal esophagus (NE) and esophageal squamous cell carcinoma by plotting expression values for EpCam, TFF1, and SBEM in three-dimensional Euclidean space. For monitoring progression of Barrett's esophagus (BE) to EA, we developed a highly sensitive assay for limited quantities of tissue whereby 50 ng of RNA are first converted to cDNA using 16 gene-specific primers. Using a set of training tissues, we developed a novel quantitative three-tiered algorithm that allows for accurate (overall accuracy = 61/63, 97%) discrimination of BE versus EA tissues using only three genes. The gene used in the first tier of the algorithm is TSPAN: samples not diagnosed as BE or EA by TSPAN in the first tier are then subjected to a second-tier analysis using ECGF1, followed by a third-tier analysis using SPARC. Addition of TFF1 and SBEM to the first tier (i.e., a five-gene marker panel) increases the overall accuracy of the assay to 98% (62/63) and results in mean molecular diagnostic scores (± SD) that are significantly different between EA and BE samples (3.19 ± 1.07 versus −2.74 ± 1.73, respectively). Our results suggest that relatively few genes can be used to monitor progression of BE to EA.