Development of a Multiplex Quantitative PCR Signature to Predict Progression in Non–Muscle-Invasive Bladder Cancer

Departments of ¹Urology, ²Pathology, and ³Biostatistics, ⁴Michigan Center for Translational Pathology, ⁵Comprehensive Cancer Center, and ⁶Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan

^* To whom correspondence should be addressed. E-mail: arul{at}umich.edu.

	Abstract

In bladder cancer, clinical grade and stage fail to capture outcome. We developed a clinically applicable quantitative PCR (QPCR) gene signature to predict progression in non–muscle-invasive bladder cancer. Comparative metaprofiling of 12 DNA microarray data sets (comprising 631 samples and 241,298 probe sets) identified 96 genes, which showed differential expression in seven clinical outcome categories, or were identified as outliers, historic markers, or housekeeping genes. QPCR was done to determine mRNA expression from 96 bladder tumors. Fifty-seven genes differentiated T2 from non-T2 tumors (P < 0.05). Principal components analysis and Cox regression models were used to predict probability of T2 progression for non-T2 patients, placing them into high- and low-risk groups based on their gene expression. At 2 years, high-risk patients exhibited greater T2 progression (45% for high-risk patients versus 12% for low-risk patients; P = 0.003, log-rank test). This difference remained significant within T1 tumors (61% for high-risk patients versus 22% for low-risk patients; P = 0.02) and Ta tumors (29% for high-risk patients versus 0% for low-risk patients; P = 0.03). The best multivariate Cox model included stage and gender, and this signature provided predictive improvement over both (P = 0.002, likelihood ratio test). Immunohistochemistry was done for two genes in the signature not previously described in bladder cancer, ACTN1 and CDC25B, corroborating their up-regulation at the protein level with disease progression. Thus, we identified a 57-gene QPCR panel to help predict progression of non–muscle-invasive bladder cancers and delineate a systematic, generalizable approach to converting microarray data into a multiplex assay for cancer progression. [Cancer Res 2009;69(9):3810–8]

Key Words: urinary bladder neoplasms, disease progression, gene expression profiling, pcr, Bioinformatics