Can Gene Expression Pattern Analysis Predict Recurrence in Node-Negative Breast Cancer

摘要

Although a majority of women with node-negative breast cancers have a good prognosis, 30% experience recurrence and death from metastatic disease. As result, systemic therapies are routinely administered to nearly all of these node-negative patients. Markers that better predict recurrence risk would more effectively target adjuvant therapies to the patients most likely to benefit from them. Our goal is to identify the genetic markers that (1) are differentially expressed in good versus bad outcome node-negative primary breast cancers, (2) help dichotomize node-negative patients into low and high-risk categories so that adjuvant treatment could be more effectively utilized, (3) identify genetic pathways associated with the metastatic phenotype. cDNA micro- arrays were used to analyze 30 untreated primary node-negative breast tumors from patients who were either completely cured by surgery alone (good outcome) and those who experienced metastatic recurrences (Bad outcome). At the p =0.05 level of significance, 137 genes involved in cell cycle, apoptosis samples DNA repair, cell adhesion, cytoskeleton and signal transduction were found to be differentially expressed between the good versus bad outcome tumors by Wilcoxon tests. Tree-view analyses generated dendrograms showing that the two categories of tumors mostly, but not completely, formed outcome- related clusters. We are currently validating the array data using semi-quantitative RT-PCR. Preliminary assays showed that the tested genes are indeed differentially expressed in the tumor samples. More candidate genes are currently being assayed. We have just started immunohistochemistry analysis on tissue arrays of archival specimens to assess the prognostic significance of some of these interesting candidate metastasis markers for which antibodies are commercially available.