The most important predictor of prognosis in breast cancer is lymph node status, yet little is known about molecular changes associated with lymph node metastasis. Here, gene expression analysis was performed on primary breast (PBT) and corresponding metastatic lymph node (MLN) tumors to identify molecular signatures associated with nodal metastasis. RNA was isolated after laser microdissection from frozen PBT and MLN from 20 patients with positive lymph nodes and hybridized to the microarray chips. Differential expression was determined using Mann-Whitney testing; Bonferroni corrected P values of 0.05 and 0.001 were calculated. Results were validated using TaqMan assays. Fifty-one genes were differentially expressed (P < 1 x 10(-5), less than twofold differences) between the PBT and paired MLN; 13 with significantly higher expression in the MLN and 38 in the PBT. qRT-PCR validated the differential expression of 40/51 genes. Of the 40 validated genes, NTS and PAX5 were found to have >100-fold higher expression in MLT while COL11A1, KRT14, MMP13, TAC1 and WNT2 had >100-fold higher expression in PBT. Gene expression differences between PBT and MLN suggests that expression of a unique set of genes is required for successful lymph node colonization. Genes expressed at higher levels in PBT are involved in degradation of the extracellular matrix, enabling cells with metastatic potential to disseminate, while genes expressed at higher levels in metastases are involved in transcription, signal transduction and immune response, providing cells with proliferation and survival advantages. These data improve our understanding of the biological processes involved in successful metastatis and provide new targets to arrest tumor cell dissemination and metastatic colonization.

BACKGROUND: Breast tumors from African American women have less favorable pathological characteristics and higher mortality rates than those of Caucasian women. Although socioeconomic status may influence prognosis, biological factors are also likely to contribute to tumor behavior. METHODS: Patients with invasive breast cancer were matched by age, grade, and estrogen receptor status; patients with benign disease were matched by age and diagnosis type. RNA from laser microdissected tumors and whole-sectioned nonmalignant breast tissues was hybridized to HG U133A 2.0 microarrays. Data were analyzed using Partek Genomics Suite using a cutoff of P < .001, >1.5-fold change, and results were validated by quantitative real-time polymerase chain reaction. RESULTS: Clinicopathological factors did not differ significantly between groups for age at diagnosis, tumor size or stage, lymph node or human epidermal growth receptor 2 status, intrinsic subtype, or mortality. Two-way analysis of the tumor specimens revealed 25 probes representing 23 genes differentially expressed between populations; hierarchical clustering classified 24 of 26 African American women and 25 of 26 Caucasian women correctly. In the nonmalignant specimens, 15 probes representing 13 genes were differentially expressed, including 5 genes that also differed in the tumor specimens; these genes were able to correctly classify nonmalignant breast specimens from 20 of 22 of African American women and all of the Caucasian women. CONCLUSIONS: Despite matching of tumors by pathological characteristics, molecular profiles differed between African American women and Caucasian women in both invasive tumors and benign breast tissues. These differentially expressed genes, including CRYBB2, PSPHL, and SOS1, are involved in cellular growth and differentiation, invasion, metastasis, and immune response and thus may contribute to the poor outcome in African American women. Cancer 2012;118:1334-

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Lymph node status remains one of the most useful prognostic indicators in breast cancer; however, current methods to assess nodal status disrupt the lymphatic system and may lead to secondary complications. Identification of molecular signatures discriminating lymph node-positive from lymph node-negative primary tumors would allow for stratification of patients requiring surgical assesment of lymph nodes. Primary breast tumors from women with negative (n = 41) and positive (n = 35) lymph node status matched for possible confounding factors were subjected to laser microdissection and gene expression data generated. Although ANOVA analysis (P < .001, fold-change >1.5) revealed 13 differentially expressed genes, hierarchical clustering classified 90% of node-negative but only 66% of node-positive tumors correctly. The inability to derive molecular profiles of metastasis in primary tumors may reflect tumor heterogeneity, paucity of cells within the primary tumor with metastatic potential, influence of the microenvironment, or inherited host susceptibility to metastasis.

Microarray-based gene expression profiling is revolutionizing biomedical research by allowing expression profiles of thousands of genes to be interrogated in a single experiment. In cancer research, the use of laser microdissection (LM) to isolate RNA from tissues provides the ability to accurately identify molecular profiles from different cell types that comprise the tumor and its surrounding microenvironment. Because RNA is an unstable molecule, the quality of RNA extracted from tissues can be affected by sample preparation and processing. Thus, special protocols have been developed to isolate research-quality RNA after LM. This chapter provides detailed descriptions of protocols used to generate micro-array data from high-quality frozen breast tissue specimens, as well as challenges associated with formalin-fixed paraffin-embedded specimens.