The lumi Bioconductor package, www.bioconductor.org

BackgroundHigh-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.ResultsWe demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences.ConclusionsThe Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.

The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.

Pathway-targeted cancer drugs can produce dramatic responses that are invariably limited by the emergence of drug-resistant cells. We found that many drug-treated "oncogene-addicted" cancer cells engage a positive feedback loop leading to Stat3 activation, consequently promoting cell survival and limiting overall drug response. This was observed in cancer cells driven by diverse activated kinases, including EGFR, HER2, ALK, and MET, as well as mutant KRAS. Specifically, MEK inhibition led to autocrine activation of Stat3 via the FGF receptor and JAK kinases, and pharmacological inhibition of MEK together with JAK and FGFR enhanced tumor regression. These findings suggest that inhibition of a Stat3 feedback loop may augment the response to a broad spectrum of drugs that target pathways of oncogene addiction.

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.