DR-Integrator: a new analytic tool for integrating DNA copy number and gene expression data

Summary: DNA copy number alterations (CNA) frequently underlie gene expression changes by increasing or decreasing gene dosage. However, only a subset of genes with altered dosage exhibit concordant changes in gene expression. This subset is likely to be enriched for oncogenes and tumor suppressor genes, and can be identified by integrating these two layers of genome-scale data. We introduce DNA/RNA-Integrator (DR-Integrator), a statistical software tool to perform integrative analyses on paired DNA copy number and gene expression data. DR-Integrator identifies genes with significant correlations between DNA copy number and gene expression, and implements a supervised analysis that captures genes with significant alterations in both DNA copy number and gene expression between two sample classes. Availability: DR-Integrator is freely available for non-commercial use from the Pollack Lab at http://pollacklab.stanford.edu/ and can be downloaded as a plug-in application to Microsoft Excel and as a package for the R statistical computing environment. The R package is available under the name ‘DRI’ at http://cran.r-project.org/. An example analysis using DR-Integrator is included as supplemental material. Contact: ksalari@stanford.edu; pollack1@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[2]  W. Kuo,et al.  High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays , 1998, Nature Genetics.

[3]  Ash A. Alizadeh,et al.  Genome-wide analysis of DNA copy number variation in breast cancer using DNA microarrays , 1999, Nature Genetics.

[4]  Ash A. Alizadeh,et al.  Genome-wide analysis of DNA copy-number changes using cDNA microarrays , 1999, Nature Genetics.

[5]  P. Brown,et al.  Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions , 2001, Genome Biology.

[6]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  M. Ringnér,et al.  Impact of DNA amplification on gene expression patterns in breast cancer. , 2002, Cancer research.

[8]  Christian A. Rees,et al.  Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[9]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  M. Wigler,et al.  Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.

[11]  C. Croce,et al.  MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Jaakko Astola,et al.  A strategy for identifying putative causes of gene expression variation in human cancers , 2004, J. Frankl. Inst..

[13]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  T. Golub,et al.  Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma , 2005, Nature.

[15]  L. Chin,et al.  High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. , 2006, Cancer cell.

[16]  Howard Y. Chang,et al.  Genetic regulators of large-scale transcriptional signatures in cancer , 2006, Nature Genetics.

[17]  J. A. Berger,et al.  Jointly analyzing gene expression and copy number data in breast cancer using data reduction models , 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[18]  J. Mesirov,et al.  GenePattern 2.0 , 2006, Nature Genetics.

[19]  E. Lander,et al.  Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma , 2007, Proceedings of the National Academy of Sciences.

[20]  R. Tibshirani,et al.  Spatial smoothing and hot spot detection for CGH data using the fused lasso. , 2008, Biostatistics.