Applying RNA-sequencing analysis on archived specimens

Researchers at the , Massachusetts General Hospital and Harvard Medical School set out to identify gene signatures in transitional cell carcinoma that can differentiate high-grade nonprogressive vs progressive tumors.

They performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded bladder cancer (BCa) specimens with clinical pathologic characteristics best representing the general clinical development of the disease. Only samples in which muscularis propria was present and uninvolved were included, further assuring a correct diagnosis. The RNA-Seq reads were mapped to the human genome build NCBI 36 (hg18) using TopHat with no mismatch. After alignment to the transcriptome and expression quantification, a linear statistical model was built using Limma between nonprogressive and progressive samples to identify differentially expressed genes.

Overall, 5,561 genes were mapped to all samples and used for RNA-Seq analysis to identify a gene signature that was significantly and differentially expressed between patients. Signature-based stratification indicated the gene signature correlated notably with the time of T1 development to T2 tumor, suggesting that the molecular signature might be used as an independent predictor for the pace of high-grade BCa progression.


This is the first demonstration that RNA-Seq can be applied as a powerful tool to study BCa using formalin-fixed and paraffin-embedded specimens. The researchers identified a gene signature that can distinguish patients diagnosed with high-grade T1 BCas that remain as non-muscle invasive tumors from those patients with cancers progressing to muscle-invasive tumors. These findings will make future large-scale clinical cohort studies and clinical trial-based studies possible and help the development of prognostic tools for accurate prediction of T1 BCa progression that may considerably influence the clinical decision-making process, treatment regimen, and patient survival.

  • Sharron Lin X, Hu L, Sandy K, Correll M, Quackenbush J, Wu CL, Scott McDougal W. (2013) Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: Applying RNA-sequencing analysis on archived specimens. Urol Oncol [Epub ahead of print]. [abstract]