Open Access Highly Accessed Research article

Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors

Justin M Balko1, Anil Potti2, Christopher Saunders3, Arnold Stromberg3, Eric B Haura4 and Esther P Black1*

Author Affiliations

1 Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536-0082, USA

2 Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA

3 Department of Statistics, University of Kentucky, Lexington, KY 40506-0027 USA

4 Thoracic Oncology program, The H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA

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BMC Genomics 2006, 7:289  doi:10.1186/1471-2164-7-289

Published: 10 November 2006

Additional files

Additional File 1:

Inventory of Affymetrix U133A microarray data as available on http://maduk.uky.edu webcite All training and validation sets are listed. At the MADUK home page, choose Public Login and 'PENNIB' as the experimenter to access all Affymetrix files.

Format: XLS Size: 38KB Download file

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Additional File 2:

Sheet 1: Probesets excluded from the analysis because they did not align to a single transcript in a BLAST alignment analysis, as determined by Girard et al, manuscript in preparation.

Sheet 2: SAM output, with parameters for analysis. These were probesets which were included in the subsequent GO analyses to determine deregulated signal transduction genes.

Format: XLS Size: 394KB Download file

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Additional File 3:

Genes 51–180 of the gene signature of EGFR TKI sensitivity. For each gene, the Affymetrix probe ID, gene name, gene description, and p-value are given.

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Additional File 4:

KEGG Pathway analysis of the 180 gene signature via GATHER. Genes contained under each significant pathway map are given.

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Additional File 5:

TRANSFAC analysis of the 180 gene signature via GATHER. Significant transcription factor binding sites and genes containing them are given.

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Additional File 6:

Diagonal linear discriminant analysis of NSCLC cell lines using an equally balanced predictor.

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Additional File 7:

Sweave scripts (.TEX and .RNW), PDF file describing contents of Sweave script, and .TXT files (training and validation data).

Format: ZIP Size: 68KB Download file

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Additional File 8:

Affymetrix U133A microarray data for the 180 probesets used for the DLDA models to classify the Moffitt tumors. MAS v5.0 values and present/absent calls are available for each tumor.

Format: XLS Size: 117KB Download file

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