The online version of this article (doi:10.1186/s12894-015-0056-z) contains supplementary material, which is available to authorized users.
The author declares that they have no competing interests.
GD conceived of, designed, and implemented the database, and wrote the manuscript.
In the past ~15 years, the identification of diagnostic and prognostic biomarkers from gene expression data has increased our understanding of cancer biology and has led to advances in the personalized treatment of many cancers. A diagnostic biomarker is indicative of tumor status such as tumor stage, while a prognostic biomarker is indicative of disease outcome. Despite these advances, however, there are no clinically approved biomarkers for the treatment of bladder cancer, which is the fourth most common cancer in males in the United States and one of the most expensive cancers to treat. Although gene expression profiles of bladder cancer patients are publicly available, biomarker identification requires bioinformatics expertise that is not available to many research laboratories.
We collected gene expression data from 13 publicly available patient cohorts (N = 1454) and developed BC-BET, an online Bladder Cancer Biomarker Evaluation Tool for evaluating candidate diagnostic and prognostic gene expression biomarkers in bladder cancer. A user simply selects a gene, and BC-BET evaluates the utility of that gene’s expression as a diagnostic and prognostic biomarker. Specifically, BC-BET calculates how strongly a gene’s expression is associated with tumor presence (distinguishing tumor from normal samples), tumor grade (distinguishing low- from high-grade tumors), tumor stage (distinguishing non-muscle invasive from muscle invasive samples), and patient outcome (e.g., disease-specific survival) across all patients in each cohort. Patients with low-grade, non-muscle invasive tumors and patients with high-grade, muscle invasive tumors are also analyzed separately in order to evaluate whether the biomarker of interest has prognostic value independent of grade and stage.
Although bladder cancer gene expression datasets are publicly available, their analysis is computationally intensive and requires bioinformatics expertise. BC-BET is an easy-to-use tool for rapidly evaluating bladder cancer gene expression biomarkers across multiple patient cohorts.
Additional file 1: Table S1. Summary of patient cohorts, sample exclusion and processing for BC-BET. The Complete cohort columns correspond to all samples available at the given accession number or publication, while the BC-BET columns corresponds to the samples included in the database. Table S2. Association of stage (MI vs. NMI) and grade (HG vs. LG) with the best available endpoint. Statistically significant associations (P < 0.05) are highlighted in bold.12894_2015_56_MOESM1_ESM.docx
Additional file 2: Figure S1. Association of treatment with outcome in CNUH and DFCI cohorts. Kaplan-Meier curves for CNUH cohort showing association of disease-specific survival (DSS) and overall survival (OS) with (A) intravesical Bacillus Calmette-Guerin (BCG) therapy in patients with NMI tumors and (B) cisplatin-based adjuvant chemotherapy in patients with MI tumors. C, Association of recurrence-free survival (RFS) with adjuvant chemotherapy in patients with MI tumors in DFCI chort. P-values are calculated by log-rank test. Abbreviations: HR, hazard ratio; N+, nodal involvement (pN1-pN3); M+, distant metastasis.12894_2015_56_MOESM2_ESM.pptx
Additional file 3: Table S3. BC-BET results: evaluation of FGFR3. The spreadsheet consists of multiple sheets with a description of each sheet at the top. The TUMOR, GRADE, and STAGE sheets contain the results of the diagnostic biomarker evaluation comparing tumor vs. normal samples, HG vs. LG tumors, and MI vs. NMI tumors, respectively. The SURVIVAL, SURVIVAL.LG.NMI, SURVIVAL.HG.MI sheets contain the results of the prognostic biomarker evaluation in patients with both NMI and MI tumors, patients with LG, NMI tumors, and patients with HG, MI tumors, respectively.12894_2015_56_MOESM3_ESM.xlsx
Knauer M, Straver M, Rutgers E, Bender R, Cardoso F, Mook S, et al. The 70-gene MammaPrint signature is predictive for chemotherapy benefit in early breast cancer. Breast. 2009;18:S36–7.
Markopoulos C, Xepapadakis G, Venizelos V, Tsiftsoglou A, Misitzis J, Panoussis D, et al. Clinical Use of OncotypeDX recurrence score as an adjuvant-treatment decision tool in early breast cancer patients. Eur J Cancer. 2011;47:S379. CrossRef
Stein JP, Lieskovsky G, Cote R, Groshen S, Feng AC, Boyd S, et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J Clin Oncol. 2001;19(3):666–75. PubMed
Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genet. 2009;2:18.
Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res Off J Am Assoc Cancer Res. 2005;11(11):4044–55. CrossRef
Mostofi F, Sobin L, Tosoni I. Histological typing of urinary bladder tumours. International histological classification of tumours, No 19. Geneva: World Health Organization; 1973.
The International Agency for Research on Cancer. Non-invasive urothelial tumors. In: Eble JN, Epstein JI, Sesterhenn IA, Sauter G, editors. Pathology and genetics of tumours of the urinary system and male genital organs (IARC WHO classification of tumours). Lyon, France: IARC Press; 2004.
Edge SB, American Joint Committee on Cancer., American Cancer Society. AJCC cancer staging handbook : from the AJCC cancer staging manual, 7th edn. New York: Springer; 2010.
Bakkar AA, Wallerand H, Radvanyi F, Lahaye JB, Pissard S, Lecerf L, et al. FGFR3 and TP53 gene mutations define two distinct pathways in urothelial cell carcinoma of the bladder. Cancer Res. 2003;63(23):8108–12. PubMed
Dyrskjot L, Zieger K, Real FX, Malats N, Carrato A, Hurst C, et al. Gene expression signatures predict outcome in non-muscle-invasive bladder carcinoma: a multicenter validation study. Clin Cancer Res Off J Am Assoc Cancer Res. 2007;13(12):3545–51. CrossRef
Riester M, Taylor JM, Feifer A, Koppie T, Rosenberg JE, Downey RJ, et al. Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2012;18(5):1323–33. CrossRef
- An online tool for evaluating diagnostic and prognostic gene expression biomarkers in bladder cancer
Garrett M. Dancik
- BioMed Central
Neu im Fachgebiet Urologie
Meistgelesene Bücher in der Urologie
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