Dataset Information

The prediction of interferon treatment effects based on time series microarray gene expression profiles.

ABSTRACT: BACKGROUND: The status of a disease can be reflected by specific transcriptional profiles resulting from the induction or repression activity of a number of genes. Here, we proposed a time-dependent diagnostic model to predict the treatment effects of interferon and ribavirin to HCV infected patients by using time series microarray gene expression profiles of a published study. METHODS: In the published study, 33 African-American (AA) and 36 Caucasian American (CA) patients with chronic HCV genotype 1 infection received pegylated interferon and ribavirin therapy for 28 days. HG-U133A GeneChip containing 22283 probes was used to analyze the global gene expression in peripheral blood mononuclear cells (PBMC) of all the patients on day 0 (pretreatment), 1, 2, 7, 14, and 28. According to the decrease of HCV RNA levels on day 28, two categories of responses were defined: good and poor. A voting method based on Student's t test, Wilcoxon test, empirical Bayes test and significance analysis of microarray was used to identify differentially expressed genes. A time-dependent diagnostic model based on C4.5 decision tree was constructed to predict the treatment outcome. This model not only utilized the gene expression profiles before the treatment, but also during the treatment. Leave-one-out cross validation was used to evaluate the performance of the model. RESULTS: The model could correctly predict all Caucasian American patients' treatment effects at very early time point. The prediction accuracy of African-American patients achieved 85.7%. In addition, thirty potential biomarkers which may play important roles in response to interferon and ribavirin were identified. CONCLUSION: Our method provides a way of using time series gene expression profiling to predict the treatment effect of pegylated interferon and ribavirin therapy on HCV infected patients. Similar experimental and bioinformatical strategies may be used to improve treatment decisions for other chronic diseases.

SUBMITTER: Huang T

PROVIDER: S-EPMC2546378 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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The prediction of interferon treatment effects based on time series microarray gene expression profiles.

Huang Tao T Tu Kang K Shyr Yu Y Wei Chao-Chun CC Xie Lu L Li Yi-Xue YX

Journal of translational medicine 20080809

<h4>Background</h4>The status of a disease can be reflected by specific transcriptional profiles resulting from the induction or repression activity of a number of genes. Here, we proposed a time-dependent diagnostic model to predict the treatment effects of interferon and ribavirin to HCV infected patients by using time series microarray gene expression profiles of a published study.<h4>Methods</h4>In the published study, 33 African-American (AA) and 36 Caucasian American (CA) patients with chr ...[more]

PMID: 18691426

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Project description:BackgroundCells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets.ResultsIn this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters.ConclusionWe successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.

Project description:BACKGROUND: High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatment, and prevention of diseases. There are however significant challenges to properly exploiting this data due to its massive scale and complexity. In particular, existing techniques are found to be ill suited to finding patterns of changing activity over a limited interval of an experiments time frame. The Time-Series Explorer (TSE) was developed to overcome this limitation by allowing users to explore their data by controlling an animated scatter-plot view. MaTSE improves and extends TSE by allowing users to visualize data with missing values, cross reference multiple conditions, highlight gene groupings, and collaborate by sharing their findings. RESULTS: MaTSE was developed using an iterative software development cycle that involved a high level of user feedback and evaluation. The resulting software combines a variety of visualization and interaction techniques which work together to allow biologists to explore their data and reveal temporal patterns of gene activity. These include a scatter-plot that can be animated to view different temporal intervals of the data, a multiple coordinated view framework to support the cross reference of multiple experimental conditions, a novel method for highlighting overlapping groups in the scatter-plot, and a pattern browser component that can be used with scatter-plot box queries to support cooperative visualization. A final evaluation demonstrated the tools effectiveness in allowing users to find unexpected temporal patterns and the benefits of functionality such as the overlay of gene groupings and the ability to store patterns. CONCLUSIONS: We have developed a new exploratory analysis tool, MaTSE, that allows users to find unexpected patterns of temporal activity in gene expression time-series data. Overall, the study acted well to demonstrate the benefits of an iterative software development life cycle and allowed us to investigate some visualization problems that are likely to be common in the field of bioinformatics. The subjects involved in the final evaluation were positive about the potential of MaTSE to help them find unexpected patterns in their data and characterized MaTSE as an exploratory tool valuable for hypothesis generation and the creation of new biological knowledge.

Dataset Information

The prediction of interferon treatment effects based on time series microarray gene expression profiles.

Publications

The prediction of interferon treatment effects based on time series microarray gene expression profiles.

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