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:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Project description:PURPOSE In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. METHODS In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. RESULTS Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). CONCLUSION Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

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Predective Value of Prognosis-Related Gene Expression Study in Primary Bladder Cancer

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