David Petersen

Multiple-laboratory comparison of microarray platforms

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC–Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Characterizing dye bias in microarray experiments

MOTIVATION Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. RESULTS We analyzed two large (each >27 arrays) tissue culture experiments with extensive dye swap arrays to better characterize dye bias. Indirect, amino-allyl labeling was used in both experiments. We found that post-normalization dye bias that is consistent across samples does appear to exist for many genes, and that controlling and correcting for this type of dye bias in design and analysis is advisable. The extent of this type of dye bias remained unchanged under a wide range of normalization methods (median-centering, various loess normalizations) and statistical analysis techniques (parametric, rank based, permutation based, etc.). We also found dye bias related to the individual samples for a much smaller subset of genes. But these sample-specific dye biases appeared to have minimal impact on estimated gene-expression differences between the cell lines.

Core Biopsies Can Be Used to Distinguish Differences in Expression Profiling by cDNA Microarrays

The primary focus of this work was to determine the feasibility of obtaining representative expression array profiles from clinical core biopsies. For this purpose we performed six 16-gauge needle core biopsies and an excision biopsy on each of two different human xenografts, one from an Ewings sarcoma cell line and the second from neuroblastoma cell line grown in Beige-Scid mice. Three of the six core biopsies were processed separately and the remaining three were pooled and processed together. As the initial RNA material isolated from the core biopsies was not sufficient for microarray analysis, an amplification procedure using a modified Eberwine protocol was performed, and the amplified products applied onto a 6000-feature human cDNA microarray. Comparisons of the array results from core biopsies (amplified RNA) and surgical specimens (non-amplified RNA) showed maintenance of the expression profile as assessed by hierarchical clustering. Gene expression profiles obtained from microarray analysis clearly differentiated the Ewings sarcoma from the neuroblastoma with both core and excisional biopsies as starting material. Pooling the core biopsies did not improve the concordance with excisional biopsies. In conclusion, our results suggest that core biopsies can be used as a suitable and reliable material for the determination of tumor genetic profiles.

Changes in gene expression during the development of mammary tumors in MMTV-Wnt-1 transgenic mice

BackgroundIn human breast cancer normal mammary cells typically develop into hyperplasia, ductal carcinoma in situ, invasive cancer, and metastasis. The changes in gene expression associated with this stepwise progression are unclear. Mice transgenic for mouse mammary tumor virus (MMTV)-Wnt-1 exhibit discrete steps of mammary tumorigenesis, including hyperplasia, invasive ductal carcinoma, and distant metastasis. These mice might therefore be useful models for discovering changes in gene expression during cancer development.ResultsWe used cDNA microarrays to determine the expression profiles of five normal mammary glands, seven hyperplastic mammary glands and 23 mammary tumors from MMTV-Wnt-1 transgenic mice, and 12 mammary tumors from MMTV-Neu transgenic mice. Adipose tissues were used to control for fat cells in the vicinity of the mammary glands. In these analyses, we found that the progression of normal virgin mammary glands to hyperplastic tissues and to mammary tumors is accompanied by differences in the expression of several hundred genes at each step. Some of these differences appear to be unique to the effects of Wnt signaling; others seem to be common to tumors induced by both Neu and Wnt-1 oncogenes.ConclusionWe described gene-expression patterns associated with breast-cancer development in mice, and identified genes that may be significant targets for oncogenic events. The expression data developed provide a resource for illuminating the molecular mechanisms involved in breast cancer development, especially through the identification of genes that are critical in cancer initiation and progression.

An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma Based on mRNA, microRNA, and DNA Methylation Biomarkers.

Introduction: Up to 30% stage I lung cancer patients suffer recurrence within 5 years of curative surgery. We sought to improve existing protein-coding gene and microRNA expression prognostic classifiers by incorporating epigenetic biomarkers. Methods: Genome-wide screening of DNA methylation and pyrosequencing analysis of HOXA9 promoter methylation were performed in two independently collected cohorts of stage I lung adenocarcinoma. The prognostic value of HOXA9 promoter methylation alone and in combination with mRNA and miRNA biomarkers was assessed by Cox regression and Kaplan–Meier survival analysis in both cohorts. Results: Promoters of genes marked by polycomb in embryonic stem cells were methylated de novo in tumors and identified patients with poor prognosis. The HOXA9 locus was methylated de novo in stage I tumors (p < 0.0005). High HOXA9 promoter methylation was associated with worse cancer-specific survival (hazard ratio [HR], 2.6; p = 0.02) and recurrence-free survival (HR, 3.0; p = 0.01), and identified high-risk patients in stratified analysis of stages IA and IB. Four protein-coding gene (XPO1, BRCA1, HIF1&agr;, and DLC1), miR-21 expression, and HOXA9 promoter methylation were each independently associated with outcome (HR, 2.8; p = 0.002; HR, 2.3; p = 0.01; and HR, 2.4; p = 0.005, respectively), and when combined, identified high-risk, therapy naive, stage I patients (HR, 10.2; p = 3 × 10−5). All associations were confirmed in two independently collected cohorts. Conclusion: A prognostic classifier comprising three types of genomic and epigenomic data may help guide the postoperative management of stage I lung cancer patients at high risk of recurrence.

Identification of new Rel/NF-kappaB regulatory networks by focused genome location analysis

NF-κB is an inducible transcription factor that controls kinetically complex patterns of gene expression. Several studies reveal multiple pathways linking NF-κB to the promotion and progression of various cancers. Despite extensive interest and characterization, many NF-κB controlled genes still remain to be identified. We used chromatin immunoprecipitation combined with microarray technology (ChIP/Chip) to investigate the dynamic interaction of NF-κB with the promoter regions of 100 genes known to be expressed in mitogen-induced T-cells. Six previously unrecognized NF-κB controlled genes (ATM, EP300, TGFβ, Selectin, MMP-1, and SFN) were identified. Each gene is induced in mitogen-stimulated T-cells, repressed by pharmacological NF-κB blockade, reduced in cells deficient in the p50 NF-κB subunit and dramatically repressed by RNAi specifically designed against cRel. A coregulatory role for Ets transcription factors in the expression of the NF-κB controlled genes was predicted by comparative promoter analysis and confirmed by ChIP and by functional disruption of Ets. NF-κB deficiency produces a deficit in ATM function and DNA repair indicating an active role for NF-κB in maintaining DNA integrity. These results define new potential targets and transcriptional networks governed by NF-κB and provide novel functional insights for the role of NF-κB in genomic stability, cell cycle control, cell-matrix and cell-cell interactions during tumor progression.

A search for candidate genes for lipodystrophy, obesity and diabetes via gene expression analysis of A-ZIP/F-1 mice

Genome scans for diabetes have identified many regions of the human genome that correlate with the disease state. To identify candidate genes for type 2 diabetes, we examined the transgenic A-ZIP/F-1 mouse. This mouse model has no white fat, resulting in abnormal levels of glucose, insulin, and leptin, making the A-ZIP/F-1 mice a good model for lipodystrophy and insulin resistance. We used cDNA-based microarrays to find differentially expressed genes in four tissues of these mice. We examined these results in the context of human linkage scans for lipodystrophy, obesity, and type 2 diabetes. We combined 199 known human orthologs of the misregulated mouse genes with 33 published human genome scans on a genome map. Integrating expression data with human linkage results permitted us to suggest and prioritize candidate genes for lipodystrophy and related disorders. These genes include a cluster of 3 S100A genes on chromosome 1 and SLPI1 on chromosome 20.

Aneuploidy, TP53 mutation, and amplification of MYC correlate with increased intratumor heterogeneity and poor prognosis of breast cancer patients

The clinical course of breast cancer varies from one patient to another. Currently, the choice of therapy relies on clinical parameters and histological and molecular tumor features. Alas, these markers are informative in only a subset of patients. Therefore, additional predictors of disease outcome would be valuable for treatment stratification. Extensive studies showed that the degree of variation of the nuclear DNA content, i.e., aneuploidy, determines prognosis. Our aim was to further elucidate the molecular basis of aneuploidy. We analyzed five diploid and six aneuploid tumors with more than 20 years of follow‐up. By performing FISH with a multiplexed panel of 10 probes to enumerate copy numbers in individual cells, and by sequencing 563 cancer‐related genes, we analyzed how aneuploidy is linked to intratumor heterogeneity. In our cohort, none of the patients with diploid tumors died of breast cancer during follow‐up in contrast to four of six patients with aneuploid tumors (mean survival 86.4 months). The FISH analysis showed markedly increased genomic instability and intratumor heterogeneity in aneuploid tumors. MYC gain was observed in only 20% of the diploid cancers, while all aneuploid cases showed a gain. The mutation burden was similar in diploid and aneuploid tumors, however, TP53 mutations were not observed in diploid tumors, but in all aneuploid tumors in our collective. We conclude that quantitative measurements of intratumor heterogeneity by multiplex FISH, detection of MYC amplification and TP53 mutation could augment prognostication in breast cancer patients.

Impact of telomere length on survival in classic and variant hairy cell leukemia

Telomeres, which protect the ends of chromosomes, are shortened in several hematologic malignancies, often with adverse prognostic implications, but their effect on prognosis of classic and variant hairy cell leukemia (HCL and HCLv) has not been reported. HCL/HCLv genomic DNA from 46 patients was studied by PCR to determine the ratio of telomere to single copy gene number (T/S). T/S was unrelated to diagnosis of HCL or HCLv (p=0.27), but shorter T/S was associated with unmutated immunoglobulin rearrangements (p=0.033) and age above the median at diagnosis (p=0.017). Low T/S was associated with shorter overall survival from diagnosis (OS), particularly T/S <0.655 (p=0.0064, adjusted p=0.019). Shorter OS was also associated with presence of unmutated (p<0.0001) or IGHV4-34+ (p<0.0001) rearrangements, or increasing age (p=0.0002). Multivariable analysis with Cox modeling showed that short T/S along with either unmutated or IGHV4-34+ rearrangements remained associated with reduced OS (p=0.0071, p=0.0024, respectively) after age adjustment. While T/S is relatively long in HCL and the disease usually indolent with excellent survival, shortened telomeres in HCL/HCLv are associated with decreased survival. Shortened T/S could represent a risk factor needing further investigation/intervention to determine if non-chemotherapy treatment options, in addition to or instead of chemotherapy, might be particularly useful.

Core biopsy versus surgical tumor specimens for microarray analysis of gene expression profiles

Core biopsy versus surgical tumor specimens for microarray analysis of gene expression profiles