Microarray technology and its effect on breast cancer (re)classification and prediction of outcome

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Evaluation of MetriGenix custom 4D™ arrays applied for detection of breast cancer subtypes

BACKGROUND: Previously, a total of five breast cancer subtypes have been identified based on variation in gene expression patterns. These expression profiles were also shown to be associated with different prognostic value. In this study tumour samples from 27 breast cancer patients, previously subtyped by expression analysis using DNA microarrays, and four controls from normal breast tissue were included. A new MetriGenix 4D™ array proposed for diagnostic use was evaluated. METHODS: We applied MetriGenix custom 4D™ arrays for the detection of previously defined molecular subtypes of breast cancer. MetriGenix 4D™ arrays have special features including probe immobilization in microchannels with chemiluminescence detection that enable shorter hybridization time. RESULTS: The MetriGenix 4D™ array platform was evaluated with respect to both the accuracy in classifying the samples as well as the performance of the system itself. In a cross validation analysis using "Nearest Shrunken Centroid classifier" and the PAM software, 77% of the samples were classified correctly according to earlier classification results. CONCLUSION: The system shows potential for fast screening; however, improvements are needed

International Agency for Research on Cancer Workshop on 'Expression array analyses in breast cancer taxonomy'

In May 2006, a workshop on Expression array analyses in breast cancer taxonomy was held at the International Agency for Research on Cancer (IARC). The workshop covered an array of topics from the validity of the currently defined breast tumor subtypes and other expression profile-based signatures to the technical limitations of expression analysis and the types of platforms on which these omics results will eventually reach clinical practice. Overall, the workshop participants believed firmly that tumor taxonomy is likely to yield improved prognostic and predictive markers. Even so, further standardization and validation are required before clinical trials are set in motion

Early detection of breast cancer based on gene-expression patterns in peripheral blood cells

INTRODUCTION: Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells. METHODS: Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach. RESULTS: We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients. CONCLUSION: The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding

Recent translational research: microarray expression profiling of breast cancer – beyond classification and prognostic markers?

Genomic expression profiling has greatly improved our ability to subclassify human breast cancers according to shared molecular characteristics and clinical behavior. The logical next question is whether this technology will be similarly useful for identifying the dominant signaling pathways that drive tumor initiation and progression within each breast cancer subtype. A major challenge will be to integrate data generated from the experimental manipulation of model systems with expression profiles obtained from primary tumors. We highlight some recent progress and discuss several obstacles in the use of expression profiling to identify pathway signatures in human breast cancer