Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer

Abstract

Background: Drug resistance in breast cancer is a major obstacle to successful chemotherapy. In this study we used cDNA microarray technology to examine gene expression profiles obtained from fine needle aspiration (FNA) of primary breast tumors before and after systemic chemotherapy. Our goal was to determine the feasibility of obtaining representative expression array profiles from limited amounts of tissue and to identify those expression profiles that correlate with treatment response.

Methods: Repeat presurgical FNA samples were taken from six patients who were to undergo primary surgical treatment. Additionally, a group of 10 patients who were to receive neoadjuvant chemotherapy underwent two FNAs before chemotherapy (adriamycin 60 mg/m2 and cyclophosphamide 600 mg/m2) followed by another FNA on day 21 after the first cycle. Total RNA was amplified with T7 Eberwine's procedure and labeled cDNA was hybridized onto a 7600-feature glass cDNA microarray.

Results: We identified candidate gene expression profiles that might distinguish tumors with complete response to chemotherapy from tumors that do not respond, and found that the number of genes that change after one cycle of chemotherapy was 10 times greater in the responding group than in the non-responding group.

Conclusion: This study supports the suitability of FNA-derived cDNA microarray expression profiling of breast cancers as a comprehensive genomic approach for studying the mechanisms of drug resistance. Our findings also demonstrate the potential of monitoring post-chemotherapy changes in expression profiles as a measure of pharmacodynamic effect and suggests that these approaches might yield useful results when validated by larger studies.

Figure 1

FNAs are representative of the entire tumor. (a)Dendrogram representing similarities in the gene expression profiles between different FNAs and surgical specimens. FNA-surgical specimen and FNA-FNA pairs from the same patient clustered together more than with samples from any other patient. ^*Surgical specimen. (b)Representative scatter plots indicating levels of similarity between the cDNA microarray results comparing different specimens. (panels a and b) Comparison of log expression ratios derived from two pretreatment FNAs from the same patient. (panels c and d) Comparison of log expression ratios derived from surgical specimens and the corresponding FNAs from two different patients. (panels e and f) Comparison of log expression ratios derived from two pretreatment FNAs and from a pretreatment FNA and a surgical specimen from different patients, respectively. The correlation coefficient of each comparison is shown at the top left of each panel.

Figure 2

Gene expression profiles distinguishing good from poor responders. (a) Hierarchical clustering of 37 genes that defined the class predictor and whose expression best differentiated good from poor responders before chemotherapy. Each row represents a single gene and each column represents the average of two available independent FNAs. Green and red squares indicate, respectively, overexpressed and underexpressed genes in a breast tumor compared with the MCF10A breast cancer cell line (color intensity is proportional to the magnitude of the expression level ratio). Black squares indicate genes with approximately equivalent expression levels and gray squares indicate missing or filter-excluded data. Branches representing good responders are shown in blue and those representing poor responders in yellow. (b) Hierarchical clustering of 16 genes whose change in expression best differentiated good from poor responders after one cycle of chemotherapy.

Figure 3

Real-time quantitative RT-PCR analysis of gene expression confirms the cDNA micoarray data. Expressions of selected genes were examined using RT-PCR in all FNA breast tumors. The expression level of each gene in the tumor samples was compared to the reference MCF10A cell line. All RT-PCR data have been normalized to β-actin. White and black columns represent good and poor responders, respectively.