Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

Profiles of Global Gene Expression in Ionizing-Radiation–Damaged Human Diploid Fibroblasts Reveal Synchronization behind the G(1) Checkpoint in a G(0)-like State of Quiescence

Cell cycle arrest and stereotypic transcriptional responses to DNA damage induced by ionizing radiation (IR) were quantified in telomerase-expressing human diploid fibroblasts. Analysis of cytotoxicity demonstrated that 1.5 Gy IR inactivated colony formation by 40–45% in three fibroblast lines; this dose was used in all subsequent analyses. Fibroblasts exhibited > 90% arrest of progression from G(2) to M at 2 hr post-IR and a similarly severe arrest of progression from G(1) to S at 6 and 12 hr post-IR. Normal rates of DNA synthesis and mitosis 6 and 12 hr post-IR caused the S and M compartments to empty by > 70% at 24 hr. Global gene expression was analyzed in IR-treated cells. A microarray analysis algorithm, EPIG, identified nine IR-responsive patterns of gene expression that were common to the three fibroblast lines, including a dominant p53-dependent G(1) checkpoint response. Many p53 target genes, such as CDKN1A, GADD45, BTG2, and PLK3, were significantly up-regulated at 2 hr post-IR. Many genes whose expression is regulated by E2F family transcription factors, including CDK2, CCNE1, CDC6, CDC2, MCM2, were significantly down-regulated at 24 hr post-IR. Numerous genes that participate in DNA metabolism were also markedly repressed in arrested fibroblasts apparently as a result of cell synchronization behind the G(1) checkpoint. However, cluster and principal component analyses of gene expression revealed a profile 24 hr post-IR with similarity to that of G(0) growth quiescence. The results reveal a highly stereotypic pattern of response to IR in human diploid fibroblasts that reflects primarily synchronization behind the G(1) checkpoint but with prominent induction of additional markers of G(0) quiescence such as GAS1

Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype

Histopathology, clinical chemistry, hematology and gene expression data were collected from the rat liver and blood after treatment with eight known hepatotoxins

Capturing genomic signatures of DNA sequence variation using a standard anonymous microarray platform

Comparative genomics, using the model organism approach, has provided powerful insights into the structure and evolution of whole genomes. Unfortunately, only a small fraction of Earth's biodiversity will have its genome sequenced in the foreseeable future. Most wild organisms have radically different life histories and evolutionary genomics than current model systems. A novel technique is needed to expand comparative genomics to a wider range of organisms. Here, we describe a novel approach using an anonymous DNA microarray platform that gathers genomic samples of sequence variation from any organism. Oligonucleotide probe sequences placed on a custom 44 K array were 25 bp long and designed using a simple set of criteria to maximize their complexity and dispersion in sequence probability space. Using whole genomic samples from three known genomes (mouse, rat and human) and one unknown (Gonystylus bancanus), we demonstrate and validate its power, reliability, transitivity and sensitivity. Using two separate statistical analyses, a large numbers of genomic ‘indicator’ probes were discovered. The construction of a genomic signature database based upon this technique would allow virtual comparisons and simple queries could generate optimal subsets of markers to be used in large-scale assays, using simple downstream techniques. Biologists from a wide range of fields, studying almost any organism, could efficiently perform genomic comparisons, at potentially any phylogenetic level after performing a small number of standardized DNA microarray hybridizations. Possibilities for refining and expanding the approach are discussed

Identification of Primary Transcriptional Regulation of Cell Cycle-Regulated Genes upon DNA Damage

The changes in global gene expression in response to DNA damage may derive from either direct induction or repression by transcriptional regulation or indirectly by synchronization of cells to specific cell cycle phases, such as G1 or G2. We developed a model that successfully estimated the expression levels of >400 cell cycle-regulated genes in normal human fibroblasts based on the proportions of cells in each phase of the cell cycle. By isolating effects on the gene expression associated with the cell cycle phase redistribution after genotoxin treatment, the direct transcriptional target genes were distinguished from genes for which expression changed secondary to cell synchronization. Application of this model to ionizing radiation (IR)-treated normal human fibroblasts identified 150 of 406 cycle-regulated genes as putative direct transcriptional targets of IR-induced DNA damage. Changes in expression of these genes after IR treatment derived from both direct transcriptional regulation and cell cycle synchronization

Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology

Identification of Functional Networks of Estrogen- and c-Myc-Responsive Genes and Their Relationship to Response to Tamoxifen Therapy in Breast Cancer

BACKGROUND: Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance. METHODOLOGY/PRINCIPAL FINDINGS: With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes) in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The 'cell cycle', 'cell growth' and 'cell death' gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature was predictive independent of the cell cycle and cell growth signatures. CONCLUSIONS/SIGNIFICANCE: These functionally-based gene signatures can stratify patients treated with tamoxifen into groups with differing outcome, and potentially identify distinct mechanisms of tamoxifen resistance