Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

/pdf/the-microarray-quality-control-maqc-ii-study-of-common-4u0riugubk.pdf

The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.

/pdf/a-comparison-of-batch-effect-removal-methods-for-enhancement-4tml49ck6p.pdf

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

Computational Toxicology—A State of the Science Mini Review

Application of Transcriptional Benchmark Dose Values in Quantitative Cancer and Noncancer Risk Assessment

The results of predictive toxicogenomics investigations over the past 6 years reviewed in this report have shed new light on the potential of molecular expression analysis to more properly classify both genotoxic and nongenotoxic carcinogens and to predict the carcinogenicity of untested chemicals. Predictive toxicogenomics uses global molecular expression data resulting from genomic perturbation (e.g., transcription or gene expression profiles) to predict a toxicological outcome, such as carcinogenicity. The classification of carcinogens has become an essential and highly debatable component of cancer risk assessment largely because of the default assumptions that drive regulatory decision-making regarding the presumed linearity of the dose-response curve for genotoxic carcinogens. Nongenotoxic mechanisms of carcinogenesis complicate the well-established relationship between genotoxicity and carcinogenicity and challenge the interpretation of the results of rodent carcinogenicity studies in terms of their relevance to humans. Although the number of presumed nongenotoxic rodent carcinogens has dramatically increased over the past two decades, the fact remains that more than 90% of the known human carcinogens are detected in conventional short-term tests for genotoxicity and induce tumors at multiple sites in rodents. In toxicogenomics studies, a strong DNA damage response at the gene expression level suggests direct DNA modification whereas increased expression of genes involved in cell cycle progression is more characteristic of the indirect-acting agents such as those that induce oxidative stress. Metabolism genes are prominently represented among gene expression profiles that discriminate nongenotoxic modes of action (e.g., cytotoxicity and regenerative proliferation, xenobiotic receptor agonists, peroxisome proliferator-activated receptors, or hormonal-mediated processes). The evidence accumulated to date suggests that gene expression profiles reflect underlying modes or mechanisms of action, such that they will be useful in the prediction of chemical carcinogenicity, especially in conjunction with conventional short-term tests for gene mutation, chromosomal aberration and aneuploidy.

Characterizing and predicting carcinogenicity and mode of action using conventional and toxicogenomics methods.

Application of Genomic Biomarkers to Predict Increased Lung Tumor Incidence in 2-Year Rodent Cancer Bioassays

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Application of Genomic Biomarkers to Predict Increased Lung Tumor Incidence in 2-Year Rodent Cancer Bioassays