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.