A gene expression ratio-based diagnostic test for bladder cancer

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Authors: Lingsheng Dong, Andrew J Bard, William G Richards, Matthew D Nitz, Dan Theodorescu, et al

Published Date January 2009 , Volume 2009:2

Lingsheng Dong¹, Andrew J Bard¹, William G Richards¹, Matthew D Nitz², Dan Theodorescu², Raphael Bueno¹, Gavin J Gordon¹

¹The Thoracic Surgery Oncology laboratory and the Division of Thoracic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; ²Departments of Urology and Molecular Physiology, University of Virginia, Charlottesville, VA, USA

Purpose: Bladder cancer is relatively common but early detection techniques such as cystoscopy and cytology are somewhat limited. We developed a broadly applicable, platform-independent and clinically relevant method based on simple ratios of gene expression to diagnose human cancers. In this study, we sought to determine whether this technique could be applied to the diagnosis of bladder cancer.

Experimental design: We developed a model for the diagnosis of bladder cancer using expression profiling data from 80 normal and tumor bladder tissues to identify statistically significant discriminating genes with reciprocal average expression levels in each tissue type. The expression levels of select genes were used to calculate individual gene pair expression ratios in order to assign diagnosis. The optimal model was examined in two additional published microarray data sets and using quantitative RT-PCR in a cohort of 13 frozen benign bladder urothelium samples and 13 bladder cancer samples from our institution.

Results: A five-ratio test utilizing six genes proved to be 100% accurate (26 of 26 samples) for distinguishing benign from malignant bladder tissue samples (P < 10⁻⁶).

Conclusions: We have provided a proof of principle study for the use of gene expression ratios in the diagnosis of bladder cancer. This technique may ultimately prove to be a useful adjunct to cytopathology in screening urine specimens for bladder cancer.

Keywords: bladder cancer, gene expression profiling, and diagnosis