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Gene expression signatures predict outcome in non-muscle invasive bladder carcinoma - a multi-center validation study

ABSTRACT: Background Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle invasive bladder cancer are needed to improve treatment outcome. Methods We used custom designed oligonucleotide microarrays to validate four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ, and for predicting disease recurrence and progression. We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain and France. Molecular classifications were compared to pathological diagnosis and clinical outcome. The median follow-up time for the patients was 3.5 years. Results Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathological stage (P<0.001). Furthermore, the classifier added information regarding future disease progression of Ta or T1 tumors (P<0.001). The molecular 77-gene progression classifier was highly significantly correlated with progression free survival (P<0.001) and cancer specific survival (P=0.001). Furthermore, multivariate Cox´s regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade and treatment (hazard ratio 2.4, P=0.005). The diagnosis of carcinoma in situ (CIS) using a 68-gene classifier showed a highly significant correlation with histopathological CIS diagnosis (odds ratio 5.8, P<0.001) in multivariate logistic regression analysis. Conclusions We conclude that this multicenter validation study confirms the clinical utility of molecular classifiers to guide treatment decisions for patients initially diagnosed with non-muscle invasive bladder cancer. Keywords: Multi center validation study of gene expression signatures

ORGANISM(S): Homo sapiens

PROVIDER: GSE5479 | GEO | 2007/06/15

SECONDARY ACCESSION(S): PRJNA95995

REPOSITORIES: GEO

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Project description:This study aimed to identify the genetic signatures associated with disease prognosis in bladder cancer. We used 165 primary bladder cancer samples, 23 recurrent non-muscle invasive tumor tissues, 58 normal looking bladder mucosae surrounding cancer and 10 normal bladder mucosae for microarray analysis. Hierarchical clustering was used to stratify the prognosis-related gene classifiers. For validation, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) of top-ranked 14 genes was performed. On unsupervised hierarchical clustering using prognosis related gene-classifier, tumors were divided into 2 groups. The high risk gene signatures had significantly poor prognosis compared to low risk gene signatures (P<0.001 by the log-rank test, respectively). The prognosis-related gene classifiers correlated significantly with recurrence of non-muscle invasive bladder cancer (hazard ratio, 4.09; 95% confidence interval [CI], 1.94 to 8.64; P<0.001), and progression (hazard ratio, 23.68; 95% confidence interval [CI], 4.91 to 114.30; P<0.001), cancer-specific survival (hazard ratio, 29.25; 95% confidence interval [CI], 3.47 to 246.98; P=0.002) and overall survival (hazard ratio, 23.33; 95% confidence interval [CI], 4.97 to 109.50; P<0.001) of muscle invasive bladder cancer (p < 0.001, respectively). No patient with non-muscle invasive bladder cancer experienced cancer progression in low risk gene signature group. Prognosis-related gene classifiers validated by RT- PCR showed identical results. Prognosis related gene-classifiers provided strong predictive value for disease outcome. These gene classifiers could assist in selecting patients who might benefit from more aggressive therapeutic intervention or surveillance. Keywords: Gene expression, Bladder cancer, Prognosis 165 primary bladder cancer samples and 23 recurrent non-muscle invasive tumor tissues from 14 patients were taken in the Chungbuk National University Hospital. Only histologically verified transitional cell carcinoma samples were selected. Simultaneously 58 normal looking bladder mucosae surrounding cancer were obtained during the operation, which were histologically confirmed normal. Also, 10 normal bladder mucosae were obtained from patients with benign disease. The normal controls were determined to be free of cancer after revealing no malignant cells on urine cytology and no observable bladder cancer on cystoscopic examination during operation for their diseases, and were histologically reconfirmed normal.

Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmetteâ??GuÃ©rin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG Total RNA, extracted from the fresh frozen tissues of 48 pT1 bladder cancers, was isolated by TRIzol reagent (Life Technologies, NY, USA), according to the manufacturer's protocol. The quality and integrity of the RNA were confirmed by agarose gel electrophoresis and ethidium bromide staining, followed by visual examination under ultraviolet light. Five-hundred nanograms of total RNA were used for labeling hybridization according to the manufacturerâ??s protocol (Illumina HumanWG-6 BeadChip, version 2, Illumina, Inc., San Diego, CA, USA). Arrays were scanned with an Illumina Bead Array Reader confocal scanner (BeadStation 500GXDW; Illumina, Inc.) according to the manufacturer's instructions. Initial microarray gene expression data were obtained using Bead Studio software with gene expression analysis module (version 3.1.3, Illumina, Inc.).

Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both. The expression values of 204 epithelial ovarian cancer tissues are determined and used for the validation of a subclassification approach (Cancer Sci. 2009 Aug;100(8):1421-8.)

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Gene expression signatures predict outcome in non-muscle invasive bladder carcinoma - a multi-center validation study

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