Tests for finding complex patterns of differential expression in cancers: towards individualized medicine

James Lyons-Weiler¹^,2 , Satish Patel¹^,2 , Michael J Becich¹^,2 and Tony E Godfrey³^,4

¹Department of Pathology, Center for Biomedical Informatics, and Interdisciplinary Biomedical Graduate Program, University of Pittsburgh, PA 15232 USA

²Clinical Genomics Facility, Center for Pathology Informatics, Benedum Center for Oncology Informatics, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232 USA

³Departments of Surgery and Human Genetics, University of Pittsburgh Medical School, Pittsburgh, PA 15232 USA

⁴Mount Sinai School of Medicine, One Gustave Levy Place, Box 1668, East Building, Room 1070C, New York, NY 10029 USA

author email corresponding author email

BMC Bioinformatics 2004, 5:110doi:10.1186/1471-2105-5-110


Published:	12 August 2004

Abstract

Background

Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples.

Results

Interestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualized in a 'gene expression pattern grid'. The low degree of among-gene correlations in these genes suggests unique underlying genomic pathologies and high degree of unique tumor-specific differential expression. We compare the PPST and the ABA test to the parametric and non-parametric t-test by analyzing two independently published data sets from studies of progression in astrocytoma.

Conclusions

The PPST test resulted findings similar to the nonparametric t-test with higher self-consistency. These tests and the gene expression pattern grid may be useful for the identification of therapeutic targets and diagnostic or prognostic markers that are present only in subsets of cancer patients, and provide a more complete portrait of differential expression in cancer.