Importance of the efficiency of double-stranded DNA formation in cDNA synthesis for the imprecision of microarray expression analysis.

BACKGROUND: The causes of imprecision in microarray expression analysis are poorly understood, limiting the use of this technology in molecular diagnostics. Two-dimensional strandness-dependent electrophoresis (2D-SDE) separates nucleic acid molecules on the basis of length and strandness, i.e., double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), and RNA·DNA hybrids. METHODS: We used 2D-SDE to measure the efficiency of cDNA synthesis and its importance for the imprecision of an in vitro transcription-based microarray expression analysis. RESULTS: The relative amount of double-stranded cDNA formed in replicate experiments that used the same RNA sample template was highly variable, ranging between 0% and 72% of the total DNA. Microarray experiments showed an inverse relationship between the difference between sample pairs in probe variance and the relative amount of dsDNA. Approximately 15% of probes showed between-sample variation (P < 0.05) when the dsDNA percentage was between 12% and 35%. In contrast, only 3% of probes showed between-sample variation when the dsDNA percentage was 69% and 72%. Replication experiments of the 35% dsDNA and 72% dsDNA samples were used to separate sample variation from probe replication variation. The estimated SD of the sample-to-sample variation and of the probe replicates was lower in 72% dsDNA samples than in 35% dsDNA samples. CONCLUSIONS: Variation in the relative amount of double-stranded cDNA synthesized can be an important component of the imprecision in T7 RNA polymerase-based microarray expression analysis.

Comparison of gene sets for expression profiling: prediction of metastasis from low-malignant breast cancer.

PURPOSE: In the low-risk group of breast cancer patients, a subgroup experiences metastatic recurrence of the disease. The aim of this study was to examine the performance of gene sets, developed mainly from high-risk tumors, in a group of low-malignant tumors. EXPERIMENTAL DESIGN: Twenty-six tumors from low-risk patients and 34 low-malignant T2 tumors from patients with slightly higher risk have been examined by genome-wide gene expression analysis. Nine prognostic gene sets were tested in this data set. RESULTS: A 32-gene profile (HUMAC32) that accurately predicts metastasis has previously been developed from this data set. In the present study, six of the eight other gene sets have prognostic power in the low-malignant patient group, whereas two have no prognostic value. Despite a relatively small overlap between gene sets, there is high concordance of classification of samples. This, together with analysis of functional gene groups, indicates that the same pathways may be represented by several of the gene sets. However, the results suggest that low-risk patients may be classified more accurately with gene signatures developed especially for this patient group. CONCLUSION: Several gene sets, mainly developed in high-risk cancers, predict metastasis from low-malignant cancer.

Prediction of metastasis from low-malignant breast cancer by gene expression profiling.

Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients were examined with gene expression profiling. An intermediate risk group of 34 low-malignant T2 tumors that fulfilled all other low-risk criteria than tumor size was included to increase statistical power. A 32-gene classifier, HUMAC32, was identified and it predicted metastases with 80% sensitivity and 77% specificity. The classifier was also validated in an independent group of high-risk tumors resulting in comparable performance of HUMAC32 and a 70-gene classifier developed for this group. Furthermore, the 70-gene signature was tested in our low- and intermediate-risk samples. The results demonstrated high cross-platform consistency of the classifiers. Higher performance of HUMAC32 was demonstrated among the low-malignant cancers compared with the 70-gene classifier. This suggests that although the metastatic potential to some extend is determined by the same genes in groups of tumors with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.

Spotting and validation of a genome wide oligonucleotide chip with duplicate measurement of each gene.

The quality of DNA microarray based gene expression data relies on the reproducibility of several steps in a microarray experiment. We have developed a spotted genome wide microarray chip with oligonucleotides printed in duplicate in order to minimise undesirable biases, thereby optimising detection of true differential expression. The validation study design consisted of an assessment of the microarray chip performance using the MessageAmp and FairPlay labelling kits. Intraclass correlation coefficient (ICC) was used to demonstrate that MessageAmp was significantly more reproducible than FairPlay. Further examinations with MessageAmp revealed the applicability of the system. The linear range of the chips was three orders of magnitude, the precision was high, as 95% of measurements deviated less than 1.24-fold from the expected value, and the coefficient of variation for relative expression was 13.6%. Relative quantitation was more reproducible than absolute quantitation and substantial reduction of variance was attained with duplicate spotting. An analysis of variance (ANOVA) demonstrated no significant day-to-day variation.