Safety, Feasibility, and Merits of Longitudinal Molecular Testing of Multiple Metastatic Sites to Inform mTNBC Patient Treatment in the Intensive Trial of Omics in Cancer.

PURPOSE: Patients with metastatic triple-negative breast cancer (mTNBC) have poor outcomes. The Intensive Trial of Omics in Cancer (ITOMIC) sought to determine the feasibility and potential efficacy of informing treatment decisions through multiple biopsies of mTNBC deposits longitudinally over time, accompanied by analysis using a distributed network of experts. METHODS: Thirty-one subjects were enrolled and 432 postenrollment biopsies performed (clinical and study-directed) of which 332 were study-directed. Molecular profiling included whole-genome sequencing or whole-exome sequencing, cancer-associated gene panel sequencing, RNA-sequencing, and immunohistochemistry. To afford time for analysis, subjects were initially treated with cisplatin (19 subjects), or another treatment they had not received previously. The results were discussed at a multi-institutional ITOMIC Tumor Board, and a report transmitted to the subject's oncologist who arrived at the final treatment decision in conjunction with the subject. Assistance was provided to access treatments that were predicted to be effective. RESULTS: Multiple biopsies in single settings and over time were safe, and comprehensive analysis was feasible. Two subjects were found to have lung cancer, one had carcinoma of unknown primary site, tumor samples from three subjects were estrogen receptor-positive and from two others, human epidermal growth factor receptor 2-positive. Two subjects withdrew. Thirty-four of 112 recommended treatments were accessed using approved drugs, clinical trials, and single-patient investigational new drugs. After excluding the three subjects with nonbreast cancers and the two subjects who withdrew, 22 of 26 subjects (84.6%) received at least one ITOMIC Tumor Board-recommended treatment. CONCLUSION: Further exploration of this approach in patients with mTNBC is merited.

Pharmacological Silencing of MicroRNA-152 Prevents Pressure Overload-Induced Heart Failure.

BACKGROUND: MicroRNAs are small, noncoding RNAs that play a key role in gene expression. Accumulating evidence suggests that aberrant microRNA expression contributes to the heart failure (HF) phenotype; however, the underlying molecular mechanisms are not well understood. A better understanding of the mechanisms of action of microRNAs could potentially lead to targeted therapies that could halt the progression or even reverse HF. METHODS AND RESULTS: We found that microRNA-152 (miR-152) expression was upregulated in the failing human heart and experimental animal models of HF. Transgenic mice with cardiomyocyte-specific miR-152 overexpression developed systolic dysfunction (mean difference, -38.74% [95% CI, -45.73% to -31.74%]; P<0.001) and dilated cardiomyopathy. At the cellular level, miR-152 overexpression perturbed mitochondrial ultrastructure and dysregulated key genes involved in cardiomyocyte metabolism and inflammation. Mechanistically, we identified Glrx5 (glutaredoxin 5), a critical regulator of mitochondrial iron homeostasis and iron-sulfur cluster synthesis, as a direct miR-152 target. Finally, a proof-of-concept of the therapeutic efficacy of targeting miR-152 in vivo was obtained by utilizing a locked nucleic acid-based inhibitor of miR-152 (LNA 152) in a murine model of HF subjected to transverse aortic constriction. We demonstrated that animals treated with LNA-152 (n=10) showed preservation of systolic function when compared with locked nucleic acid-control treated animals (n=9; mean difference, 18.25% [95% CI, 25.10% to 11.39%]; P<0.001). CONCLUSIONS: The upregulation of miR-152 expression in the failing myocardium contributes to HF pathophysiology. Preclinical evidence suggests that miR-152 inhibition preserves cardiac function in a model of pressure overload-induced HF. These findings offer new insights into the pathophysiology of HF and point to miR-152-Glrx5 axis as a potential novel therapeutic target.

A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer.

Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

Transcriptional analysis of human cranial compartments with different embryonic origins.

OBJECTIVE: Previous investigations suggest that the embryonic origins of the calvarial tissues (neural crest or mesoderm) may account for the molecular mechanisms underlying sutural development. The aim of this study was to evaluate the differences in the gene expression of human cranial tissues and assess the presence of an expression signature reflecting their embryonic origins. METHODS: Using microarray technology, we investigated global gene expression of cells from the frontal and parietal bones and the metopic and sagittal intrasutural mesenchyme (ISM) of four human foetal calvaria. qRT-PCR of a selected group of genes was done to validate the microarray analysis. Paired comparison and correlation analyses were performed on microarray results. RESULTS: Of six paired comparisons, frontal and parietal compartments (distinct tissue types of calvaria, either bone or intrasutural mesenchyme) had the most different gene expression profiles despite being composed of the same tissue type (bone). Correlation analysis revealed two distinct gene expression profiles that separate frontal and metopic compartments from parietal and sagittal compartments. TFAP2A, TFAP2B, ICAM1, SULF1, TNC and FOXF2 were among differentially expressed genes. CONCLUSION: Transcriptional profiles of two groups of tissues, frontal and metopic compartments vs. parietal and sagittal compartments, suggest differences in proliferation, differentiation and extracellular matrix production. Our data suggest that in the second trimester of human foetal development, a gene expression signature of neural crest origin still exists in frontal and metopic compartments while gene expression of parietal and sagittal compartments is more similar to mesoderm.

Osteoblast differentiation profiles define sex specific gene expression patterns in craniosynostosis.

Single suture craniosynostosis (SSC) is the premature fusion of one calvarial suture and occurs in 1-1700-2500 live births. Congenital fusion of either the sagittal, metopic, or coronal sutures represents 95% of all cases of SSC. Sagittal and metopic synostosis have a male preponderance (3:1) while premature fusion of the coronal suture has a female preponderance (2:1). Although environmental and genetic factors contribute to SSC, the etiology of the majority of SSC cases remains unclear. In this study, 227 primary calvarial osteoblast cell lines from patients with coronal, metopic, or sagittal synostosis and unaffected controls were established and assayed for ALP activity and BrdU incorporation (n = 226) as respective measures of early stage osteoblast differentiation and proliferation. Primary osteoblast cell lines from individuals with sagittal synostosis demonstrated higher levels of ALP activity and reduced proliferation when compared to control lines. In order to address the sex differences in SSC types, the data was further stratified by sex. Osteoblasts from males and females with sagittal synostosis as well as males with metopic synostosis demonstrated higher levels of ALP activity when compared to sex matched controls, and males with sagittal or metopic synostosis demonstrated reduced levels of proliferation. In order to elucidate genes and pathways involved in these observed phenotypes, correlation analyses comparing ALP activity and proliferation to global gene expression was performed. Transcripts related to osteoblast differentiation were identified both differentially up and downregulated, correlated with ALP activity when compared to controls, and demonstrated a striking sex specific gene expression pattern. These data support that the dysregulation of osteoblast differentiation plays a role in the development of SSC and that genetic factors contribute to the observed sex related differences.

A statin-dependent QTL for GATM expression is associated with statin-induced myopathy.

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Improving breast cancer survival analysis through competition-based multidimensional modeling.

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

Supervised normalization of microarrays.

MOTIVATION: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is 'normalization', the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date. RESULTS: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods. AVAILABILITY: An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org). CONTACT: jstorey@princeton.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Histone deacetylase inhibition elicits an evolutionarily conserved self-renewal program in embryonic stem cells.

Recent evidence indicates that mouse and human embryonic stem cells (ESCs) are fixed at different developmental stages, with the former positioned earlier. We show that a narrow concentration of the naturally occurring short-chain fatty acid, sodium butyrate, supports the extensive self-renewal of mouse and human ESCs, while promoting their convergence toward an intermediate stem cell state. In response to butyrate, human ESCs regress to an earlier developmental stage characterized by a gene expression profile resembling that of mouse ESCs, preventing precocious Xist expression while retaining the ability to form complex teratomas in vivo. Other histone deacetylase inhibitors (HDACi) also support human ESC self-renewal. Our results indicate that HDACi can promote ESC self-renewal across species, and demonstrate that ESCs can toggle between alternative states in response to environmental factors.

Expression profiles of the mouse lung identify a molecular signature of time-to-birth.

A greater understanding of the regulatory processes contributing to lung development could help ameliorate morbidity and mortality in premature infants and identify individuals at risk for congenital and/or chronic lung diseases. Genomics technologies have provided rich gene expression datasets for the developing lung that enable systems biology approaches for identifying large-scale molecular signatures within this complex phenomenon. Here, we applied unsupervised principal component analysis on two developing lung datasets and identified common dominant transcriptomic signatures. Of particular interest, we identify an overlying biological program we term "time-to-birth," which describes the distance in age from the day of birth. We identify groups of genes contributing to the time-to-birth molecular signature. Statistically overrepresented are genes involved in oxygen and gas transport activity, as expected for a transition to air breathing, as well as host defense function. In addition, we identify genes with expression patterns associated with the initiation of alveolar formation. Finally, we present validation of gene expression patterns across the two datasets, and independent validation of select genes by qPCR and immunohistochemistry. These data contribute to our understanding of genetic components contributing to large-scale biological processes and may be useful, particularly in animal models of abnormal lung development, to predict the state of organ development or preparation for birth.

Individual matrix metalloproteinases control distinct transcriptional responses in airway epithelial cells infected with Pseudomonas aeruginosa.

Airway epithelium is the initial point of host-pathogen interaction in Pseudomonas aeruginosa infection, an important pathogen in cystic fibrosis and nosocomial pneumonia. We used global gene expression analysis to determine airway epithelial transcriptional responses dependent on matrilysin (matrix metalloproteinase 7 [MMP-7]) and stromelysin-2 (MMP-10), two MMPs induced by acute P. aeruginosa pulmonary infection. Extraction of differential gene expression (EDGE) analysis of gene expression changes in P. aeruginosa-infected organotypic tracheal epithelial cell cultures from wild-type, Mmp7-/-, and Mmp10-/- mice identified 2,091 matrilysin-dependent and 1,628 stromelysin-2-dependent genes that were differentially expressed. Key node network analysis showed that these MMPs controlled distinct gene expression programs involved in proliferation, cell death, immune responses, and signal transduction, among other host defense processes. Our results demonstrate discrete roles for these MMPs in regulating epithelial responses to Pseudomonas infection and show that a global genomics strategy can be used to assess MMP function.

Genetic background influences murine prostate gene expression: implications for cancer phenotypes.

BACKGROUND: Cancer of the prostate is influenced by both genetic predisposition and environmental factors. The identification of genes capable of modulating cancer development has the potential to unravel disease heterogeneity and aid diagnostic and prevention strategies. To this end, mouse models have been developed to isolate the influences of individual genetic lesions in the context of consistent genotypes and environmental exposures. However, the normal prostatic phenotypic variability dictated by a genetic background that is potentially capable of influencing the process of carcinogenesis has not been established. RESULTS: In this study we used microarray analysis to quantify transcript levels in the prostates of five commonly studied inbred mouse strains. We applied a multiclass response t-test and determined that approximately 13% (932 genes) exhibited differential expression (range 1.3-190-fold) in any one strain relative to other strains (false discovery rate < or = 10%). Expression differences were confirmed by quantitative RT-PCR, or immunohistochemistry for several genes previously shown to influence cancer progression, such as Psca, Mmp7, and Clusterin. Analyses of human prostate transcripts orthologous to variable murine prostate genes identified differences in gene expression in benign epithelium that correlated with the differentiation state of adjacent tumors. For example, the gene encoding apolipoprotein D, which is known to enhance resistance to cell stress, was expressed at significantly greater levels in benign epithelium associated with high-grade versus low-grade cancers. CONCLUSION: These studies support the concept that the cellular, tissue, and organismal context contribute to oncogenesis and suggest that a predisposition to a sequence of events leading to pathology may exist prior to cancer initiation.

Induction of the plasminogen activator system by mechanical stimulation of human bronchial epithelial cells.

Mechanical stimulation of the airway epithelium, as would occur during bronchoconstriction, is a potent stimulus and can activate profibrotic pathways. We used DNA microarray technology to examine gene expression in compressed normal human bronchial epithelial cells (NHBE). Compressive stress applied continuously over an 8-h period to NHBE cells led to the upregulation of several families of genes, including a family of plasminogen-related genes that were previously not known to be regulated in this system. Real-time PCR demonstrated a peak increase in gene expression of 8.0-fold for urokinase plasminogen activator (uPA), 16.2-fold for urokinase plasminogen activator receptor (uPAR), 4.2-fold for plasminogen activator inhibitor-1 (PAI-1), and 3.9-fold for tissue plasminogen activator (tPA). Compressive stress also increased uPA protein levels in the cell lysates (112.0 versus 82.0 ng/ml, P = 0.0004), and increased uPA (4.7 versus 3.3 ng/ml, P = 0.02), uPAR (1.3 versus 0.86 ng/ml, P = 0.007), and PAI-1 (50 versus 36 ng/ml, P = 0.006) protein levels in cell culture media. Functional studies demonstrated increased urokinase-dependent plasmin generation in compression-stimulated cells (0.0090 versus 0.0033 OD/min, P = 0.03). In addition, compression led to increased activation of matrix metalloproteinase (MMP)-9 and MMP-2 in a urokinase-dependent manner. In postmortem human lung tissue, we observed an increase in epithelial uPA and uPAR immunostaining in the airways of two patients who died in status asthmaticus compared with minimal immunoreactivity noted in airways from seven lung donors without asthma. Together these observations suggest an integrated response of airway epithelial cells to mechanical stimulation, acting through the plasminogen-activating system to modify the airway microenvironment.

The SERPINE2 gene is associated with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a complex human disease likely influenced by multiple genes, cigarette smoking, and gene-by-smoking interactions, but only severe alpha 1-antitrypsin deficiency is a proven genetic risk factor for COPD. Prior linkage analyses in the Boston Early-Onset COPD Study have demonstrated significant linkage to a key intermediate phenotype of COPD on chromosome 2q. We integrated results from murine lung development and human COPD gene-expression microarray studies with human COPD linkage results on chromosome 2q to prioritize candidate-gene selection, thus identifying SERPINE2 as a positional candidate susceptibility gene for COPD. Immunohistochemistry demonstrated expression of serpine2 protein in mouse and human adult lung tissue. In family-based association testing of 127 severe, early-onset COPD pedigrees from the Boston Early-Onset COPD Study, we observed significant association with COPD phenotypes and 18 single-nucleotide polymorphisms (SNPs) in the SERPINE2 gene. Association of five of these SNPs with COPD was replicated in a case-control analysis, with cases from the National Emphysema Treatment Trial and controls from the Normative Aging Study. Family-based and case-control haplotype analyses supported similar regions of association within the SERPINE2 gene. When significantly associated SNPs in these haplotypic regions were included as covariates in linkage models, LOD score attenuation was observed most markedly in a smokers-only linkage model (LOD 4.41, attenuated to 1.74). After the integration of murine and human microarray data to inform candidate-gene selection, we observed significant family-based association and independent replication of association in a case-control study, suggesting that SERPINE2 is a COPD-susceptibility gene and is likely influenced by gene-by-smoking interaction.

Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.

BACKGROUND: Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples. RESULTS: The redefined probe sets displayed a substantially higher level of cross-platform consistency at the level of gene correlation, cell line correlation and unsupervised hierarchical clustering. The same strategy allowed an almost complete correspondence of breast cancer subtype classification between Affymetrix gene chip and cDNA microarray derived gene expression data, and gave an increased level of similarity between normal lung derived gene expression profiles using the two technologies. In total, two Affymetrix gene-chip platforms were remapped to three cDNA platforms in the various cross-platform analyses, resulting in improved concordance in each case. CONCLUSION: We have shown that probes which target overlapping transcript sequence regions on cDNA microarrays and Affymetrix gene-chips exhibit a greater level of concordance than the corresponding Unigene or sequence matched features. This method will be useful for the integrated analysis of gene expression data generated by multiple disparate measurement platforms.

Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements.

Cancer derived microarray data sets are routinely produced by various platforms that are either commercially available or manufactured by academic groups. The fundamental difference in their probe selection strategies holds the promise that identical observations produced by more than one platform prove to be more robust when validated by biology. However, cross-platform comparison requires matching corresponding probe sets. We are introducing here sequence-based matching of probes instead of gene identifier-based matching. We analyzed breast cancer cell line derived RNA aliquots using Agilent cDNA and Affymetrix oligonucleotide microarray platforms to assess the advantage of this method. We show, that at different levels of the analysis, including gene expression ratios and difference calls, cross-platform consistency is significantly improved by sequence- based matching. We also present evidence that sequence-based probe matching produces more consistent results when comparing similar biological data sets obtained by different microarray platforms. This strategy allowed a more efficient transfer of classification of breast cancer samples between data sets produced by cDNA microarray and Affymetrix gene-chip platforms.

Increased measurement accuracy for sequence-verified microarray probes.

Microarrays have been extensively used to investigate genome-wide expression patterns. Although this technology has been tremendously successful, it has suffered from suboptimal individual measurement precision. Significant improvements in this respect have been recently made. In an effort to further explore the underlying variability, we have attempted to globally assess the accuracy of individual probe sequences used to query gene expression. For mammalian Affymetrix microarrays, we identify an unexpectedly large number of probes (greater than 19% of the probes on each platform) that do not correspond to their appropriate mRNA reference sequence (RefSeq). Compared with data derived from inaccurate probes, we find that data derived from sequence-verified probes show 1) increased precision in technical replicates, 2) increased accuracy translating data from one generation microarray to another, 3) increased accuracy translating data from oligonucleotide to cDNA microarrays, and 4) improved capture of biological information in human clinical specimens. The logical conclusion of this work is that probes containing the most reliable sequence information provide the most accurate results. Our data reveal that the identification and removal of inaccurate probes can significantly improve this technology.

A variable fold change threshold determines significance for expression microarrays.

The use of expression microarrays to determine bona fide changes in gene expression between experimental paradigms is confounded by noise due to variability in measurement. To assess the variability associated with transcript hybridization to commercial oligonucleotide-based microarrays, we generated a data set consisting of five replicate hybridizations of a single labeled cRNA target from three distinct experimental paradigms, using the Affymetrix human U95 GeneChip set. We found that the variability of expression level in our data set is intensity-specific. We quantified the observed variability in our data set in order to determine significant changes in gene expression. LOESS fitting to a plot of the standard deviation of replicates assigned a variability associated with a specific intensity. This allowed for the calculation of a "variable fold-change" threshold for any absolute intensity at any level of statistical confidence. Testing of this method indicates that it removes intensity-specific bias and results in a 5- to 10-fold reduction in the number of false-positive changes. We suggest that this approach can be widely used to improve prediction of significant changes in gene expression for oligonucleotide-based microarray experiments and reduce false leads, even in the absence of replicates.