Effect of sirolimus treatment on gene expression in renal transplant patients.

BACKGROUND: Sirolimus (rapamycin), a strong immunosuppressive agent, is administered to renal transplant patients to prevent rejection. The rapamycin signaling pathway [mammalian target of rapamycin (mTOR)] has been implicated in transcriptional regulation. METHODS: We used high-density oligonucleotide human microarrays to evaluate the effects of sirolimus treatment on gene expression in renal transplant patients. With this technique, we assessed selected genes in the rapamycin signaling, immunosuppression, insulin signaling, and triglyceride metabolism pathways. RESULTS: Filtered data from both treated and untreated patients showed variability within each group. Significant fold changes were observed in genes from the immunosuppression and insulin signaling pathways but not the rapamycin signaling pathway. The triglyceride metabolism pathway revealed a significant reduction of message levels in lipoprotein and triglyceride synthesis genes. CONCLUSIONS: These results show that using oligonucleotide microarrays to analyze the effects of sirolimus treatment in patients with renal transplant is an effective way to evaluate gene message levels in multiple pathways.

Reproducibility of gene expression signature-based predictions in replicate experiments.

PURPOSE: The goals of this analysis were to (a) determine concordance of gene expression results from replicate experiments, (b) examine prediction agreement of multigene predictors on replicate data, and (c) assess the robustness of prediction results in the face of noise. PATIENTS AND METHODS: Affymetrix U133A gene chips were used for gene expression profiling of 97 fine-needle aspiration biopsies from breast cancer. Thirty-five cases were profiled in replicates: 17 within the same laboratory, 11 in two different laboratories, and 15 to assess manual and robotic labeling. We used data from 62 cases to develop 111 distinct pharmacogenomic predictors of response to therapy. These were tested on cases profiled in duplicates to determine prediction agreement and accuracy. To evaluate the robustness of the pharmacogenomic predictors, we also introduced random noise into the informative genes in one half of the replicates. RESULTS: The average concordance correlation coefficient was 0.978 (range, 0.96-0.99) for intralaboratory replicates, 0.962 (range, 0.94-0.98) for between-laboratory replicates, and 0.971 (range, 0.93-0.99) for manual versus robotic labeling. The mean % prediction agreement on replicate data was 97% (95% CI, 0.96-0.98; SD, 0.006), 92% (95% CI, 0.90-0.93; SD, 0.009), and 94% (95% CI, 0.92-0.95; SD, 0.008) for support vector machines, diagonal linear discriminant analysis, and k-nearest neighbor prediction methods, respectively. Mean accuracy in the test set was 77% (95% CI, 0.74-0.79; SD, 0.014), 66% (95% CI, 0.63-0.73; SD, 0.015), and 64% (95% CI, 0.60-0.67; SD, 0.016), respectively. CONCLUSION: Gene expression results obtained with Affymetrix U133A chips are highly reproducible within and across two high-volume laboratories. Pharmacogenomic predictions yielded >90% agreement in replicate data.

Application of protein lysate microarrays to molecular marker verification and quantification.

This study presents the development and application of protein lysate microarray (LMA) technology for verification of presence and quantification of human tissue samples for protein biomarkers. Sub-picogram range sensitivity has been achieved on LMA using a non-enzymatic protein detection methodology. Results from a set of quality control experiments are presented and demonstrate the high sensitivity and reproducibility of the LMA methodology. The optimized LMA methodology has been applied for verification of the presence and quantification of disease markers for atherosclerosis. LMA were used to measure lipoprotein [a] and apolipoprotein B100 in 52 carotid endarterectomy samples. The data generated by LMA were validated by ELISA using the same protein lysates. The correlations of protein amounts estimated by LMA and ELISA were highly significant, with r2 > or = 0.98 (p < or = 0.001) for lipoprotein [a] and with r2 > or = 0.94 (p < or = 0.001) for apolipoprotein B100. This is the first report to compare data generated using proteins microarrays with ELISA, a standard technology for the verification of the presence of protein biomarkers. The sensitivity, reproducibility, and high-throughput quality of LMA technology make it a potentially powerful technology for profiling disease specific protein markers in clinical samples.

Analysis of dose-response effects on gene expression data with comparison of two microarray platforms.

MOTIVATION: The problems of analyzing dose effects on gene expression are gaining attention in biomedical research. A specific challenge is to detect genes with expression levels that change according to dose levels in a non-random manner, but nonetheless may be considered as potential biomarkers. METHOD: We are among the first to formally apply a tool that uses an isotonic (monotonic) regression approach to this area of study. We introduce a test statistic to select genes with significant dose-response expression in a monotonic fashion based on a permutation procedure. We then compare the results with those achieved from the application of a likelihood ratio-based test. RESULTS: We apply the isotonic regression approach to a study of gene expression in the RKO colon carcinoma cell line in response to varying dosage levels of the chemotherapeutic agent 5-fluorouracil. A feature of both Affymetrix and printed 75mer oligomer cDNA arrays produced from the same samples provides an opportunity to compare the two microarray platforms. AVAILABILITY: Statistical software S-plus Code to implement the method is available from the authors. CONTACT: kcoombes@mdanderson.org

Biological validation of differentially expressed genes in chronic lymphocytic leukemia identified by applying multiple statistical methods to oligonucleotide microarrays.

Oligonucleotide microarrays are a powerful tool for profiling the expression levels of thousands of genes. Different statistical methods for identifying differentially expressed genes can yield different results. To our knowledge, no experimental test has been performed to decide which method best identifies genes that are truly differentially expressed. We applied three statistical methods (dChip, t-test on log-transformed data, and Wilcoxon test) to identify differentially expressed genes in previously untreated patients with chronic lymphocytic leukemia (CLL). We used a training set of Affymetrix Hu133A microarray data from 11 patients with unmutated immunoglobulin (Ig) heavy chain variable region (VH) genes and 8 patients with mutated Ig VH genes. Differential expression was validated using semiquantitative real-time polymerase chain reaction assays and by validating models to predict the somatic mutation status of an independent test set of nine CLL samples. The methods identified 144 genes that were differentially expressed between cases of CLL with unmutated compared with mutated Ig VH genes. Eighty genes were identified by Wilcoxon test, 60 by t-test, and 65 by dChip, but only 11 were identified by all three methods. Greater agreement was found between the t-test and the Wilcoxon test. Differential expression was validated by semiquantitative real-time polymerase chain reaction assays for 83% of individual genes, regardless of the statistical method. However, the Wilcoxon test gave the most accurate predictions on new samples, and dChip, the least accurate. We found that all three methods were equally good for finding differentially expressed genes, but they found different genes. The genes selected by the nonparametric Wilcoxon test are the most robust for predicting the status of new cases. A comprehensive list of all differentially expressed genes can only be obtained by combining the results of multiple statistical tests.

A comparative analysis of data generated using two different target preparation methods for hybridization to high-density oligonucleotide microarrays.

BACKGROUND: To generate specific transcript profiles, one must isolate homogenous cell populations using techniques that often yield small amounts of RNA, requiring researchers to employ RNA amplification methods. The data generated by using these methods must be extensively evaluated to determine any technique dependent distortion of the expression profiles. RESULTS: High-density oligonucleotide microarrays were used to perform experiments for comparing data generated by using two protocols, an in vitro transcription (IVT) protocol that requires 5 microg of total RNA and a double in vitro transcription (dIVT) protocol that requires 200 ng of total RNA for target preparation from RNA samples extracted from a normal and a cancer cell line. In both cell lines, about 10% more genes were detected with IVT than with dIVT. Genes were filtered to exclude those that were undetected on all arrays. Hierarchical clustering using the 9,482 genes that passed the filter showed that the variation attributable to biological differences between samples was greater than that introduced by differences in the protocols. We analyzed the behavior of these genes separately for each protocol by using a statistical model to estimate the posterior probability of various levels of fold change. At each level, more differentially expressed genes were detected with IVT than with dIVT. When we checked for genes that had a posterior probability greater than 99% of fold change greater than 2, in data generated by IVT but not dIVT, more than 60% of these genes had posterior probabilities greater than 90% in data generated by dIVT. Both protocols identified the same functional gene categories to be differentially expressed. Differential expression of selected genes was confirmed using quantitative real-time PCR. CONCLUSION: Using nanogram quantities on total RNA, the usage of dIVT protocol identified differentially expressed genes and functional categories consistent with those detected by the IVT protocol. There was a loss in sensitivity of about 10% when detecting differentially expressed genes using the dIVT protocol. However, the lower amount of RNA required for this protocol, as compared to the IVT protocol, renders this methodology a highly desirable one for biological systems where sample amounts are limiting.