Xuanhui Sharron Lin1, Lan Hu2, Kirley Sandy1, Mick Correll2, John Quackenbush2, Chin-Lee Wu3, William Scott McDougal4. 1. Department of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. 2. Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA. 3. Department of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA; Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. Electronic address: cwu2@partners.org. 4. Department of Urology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. Electronic address: wmcdougal@partners.org.
Abstract
OBJECTIVES: To identify gene signatures in transitional cell carcinoma that can differentiate high-grade T1 nonprogressive (T1NP) bladder cancer (BCa) from those T1 progressive (T1P) tumors that progress to muscularis propria-invasive T2 tumors. MATERIALS AND METHODS: We performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded BCa specimens with clinical pathologic characteristics best representing the general clinical development of the disease. For the T1NP group, only patients with long-term follow-up (6-17y) and periodic examinations (average of 4 resections and 9 cytology tests) were selected. For the T1P group, only patients in whom a complete resection was performed after a minimum of 8 months after the initial T1 diagnosis were selected, therefore eliminating the possibility of underdiagnosis. 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 T1NP and T1P samples to identify differentially expressed genes. RESULTS: 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 with T1NP BCa and patients with T1P BCa. 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 T1 BCa progression. CONCLUSIONS: 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. We 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. Our 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.
OBJECTIVES: To identify gene signatures in transitional cell carcinoma that can differentiate high-grade T1 nonprogressive (T1NP) bladder cancer (BCa) from those T1 progressive (T1P) tumors that progress to muscularis propria-invasive T2 tumors. MATERIALS AND METHODS: We performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded BCa specimens with clinical pathologic characteristics best representing the general clinical development of the disease. For the T1NP group, only patients with long-term follow-up (6-17y) and periodic examinations (average of 4 resections and 9 cytology tests) were selected. For the T1P group, only patients in whom a complete resection was performed after a minimum of 8 months after the initial T1 diagnosis were selected, therefore eliminating the possibility of underdiagnosis. 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 T1NP and T1P samples to identify differentially expressed genes. RESULTS: 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 with T1NP BCa and patients with T1P BCa. 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 T1 BCa progression. CONCLUSIONS: 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. We 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. Our 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.
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