Search

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy., Competing Interests: Ivan V. Ozerov, Ksenia V. Lezhnina, Quentin Vanhaelen, Sergey Medintsev, Alexander Aliper, Artem V. Artemov, Anton Buzdin, Nikolay Borisov and Alex Zhavoronkov are affiliated with Insilico Medicine, Inc., a company engaged in aging research, which uses and provides both paid and free services using a variety of pathway activation scoring algorithms and hence may have competing financial interests.

Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment. We have developed and applied the approach of "signature networks" which overcomes some of the drawbacks of clustering methods. We have demonstrated signature network assembly, functional analysis and logical operations on the networks that can be generated. In addition, we have used this technique in a proof of concept study to compare the effect of differential drug treatment using 4-hydroxytamoxifen and estrogen on the MCF-7 breast cancer cell line from a previously published study. We have shown that the two compounds can be differentiated by the networks of interacting genes. Both networks consist of a core module of genes including c-Fos as part of c-Fos/c-Jun heterodimer and c-Myc which is clearly visible. Using algorithms in our MetaCore software we are able to subtract the 4-hydroxytamoxifen and estrogen networks to further understand differences between these two treatments and show that the estrogen network is assembled around the core with other modules essential for all phases of the cell cycle. For example, Cyclin D1 is present in networks for the estrogen treated cells from two separate studies. These signature networks represent an approach to identify biomarkers and a general approach for discovering new relationships in complex high throughput toxicology data.

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research. [ABSTRACT FROM AUTHOR]

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. [ABSTRACT FROM AUTHOR]

Background: There is resurgence within drug and biomarker development communities for the use of primary tumorgraft models as improved predictors of patient tumor response to novel therapeutic strategies. Despite perceived advantages over cell line derived xenograft models, there is limited data comparing the genotype and phenotype of tumorgrafts to the donor patient tumor, limiting the determination of molecular relevance of the tumorgraft model. This report directly compares the genomic characteristics of patient tumors and the derived tumorgraft models, including gene expression, and oncogenic mutation status. Methods: Fresh tumor tissues from 182 cancer patients were implanted subcutaneously into immunecompromised mice for the development of primary patient tumorgraft models. Histological assessment was performed on both patient tumors and the resulting tumorgraft models. Somatic mutations in key oncogenes and gene expression levels of resulting tumorgrafts were compared to the matched patient tumors using the OncoCarta (Sequenom, San Diego, CA) and human gene microarray (Affymetrix, Santa Clara, CA) platforms respectively. The genomic stability of the established tumorgrafts was assessed across serial in vivo generations in a representative subset of models. The genomes of patient tumors that formed tumorgrafts were compared to those that did not to identify the possible molecular basis to successful engraftment or rejection. Results: Fresh tumor tissues from 182 cancer patients were implanted into immune-compromised mice with forty-nine tumorgraft models that have been successfully established, exhibiting strong histological and genomic fidelity to the originating patient tumors. Comparison of the transcriptomes and oncogenic mutations between the tumorgrafts and the matched patient tumors were found to be stable across four tumorgraft generations. Not only did the various tumors retain the differentiation pattern, but supporting stromal elements were preserved. Those genes down-regulated specifically in tumorgrafts were enriched in biological pathways involved in host immune response, consistent with the immune deficiency status of the host. Patient tumors that successfully formed tumorgrafts were enriched for cell signaling, cell cycle, and cytoskeleton pathways and exhibited evidence of reduced immunogenicity. Conclusions: The preservation of the patient's tumor genomic profile and tumor microenvironment supports the view that primary patient tumorgrafts provide a relevant model to support the translation of new therapeutic strategies and personalized medicine approaches in oncology. [ABSTRACT FROM AUTHOR]

The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of 3 years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest fivefold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of current data set was sufficient to provide a robust classifier. The classification results showed that a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than with efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models overpredicted the tumor response, but the variability in predictions was significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically induced mouse lung tumors and a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related end points. [ABSTRACT FROM PUBLISHER]

The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps - sets of genes coordinately involved in key biological processes - with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations. [ABSTRACT FROM AUTHOR]