Dataset Information

Long-term validation of a molecular progression-associated gene classifier for prediction of muscle invasion in primary non-muscle-invasive bladder cancer.

ABSTRACT: Our previous study reported a clinically applicable prognostic gene classifier for primary non-muscle-invasive bladder cancer (NMIBC). The present study aimed to perform long-term validation of this classifier in the prediction of muscle-invasive disease. Previously published gene expression profiles were used from 176 patients with NMIBC with extended follow-up. Progression was defined as development of muscle invasion or metastasis, and the progression risk score was calculated using the previously developed eight-gene progression classifier. During median follow-up of 72.8 (interquartile range, 37.0-118.7) months, 26 (14.8%) patients progressed to muscle-invasive bladder cancer. The molecular progression risk score was significantly associated with clinicopathological variables, including tumor number, stage, grade and multivariate risk assessment tools (P<0.05 in each case). Multivariate Cox regression analysis revealed that molecular progression risk score was an independent predictor of development of invasive tumor, either as a continuous variable [hazard ratio (HR), 1.489; 95% confidence interval (CI), 1.216-1.823; P<0.001] or as a categorical variable (HR, 5.026; 95% CI, 1.619-15.608; P=0.005). In conclusion, the present results confirmed the clinical utility of the progression-associated gene classifier for prediction of development of muscle invasion in NMIBC. The molecular progression risk score may aid in selecting patients who could benefit from more aggressive therapeutic intervention.

SUBMITTER: Kang HW

PROVIDER: S-EPMC5530216 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Long-term validation of a molecular progression-associated gene classifier for prediction of muscle invasion in primary non-muscle-invasive bladder cancer.

Kang Ho Won HW Seo Sung Pil SP Jeong Pildu P Ha Yun-Sok YS Kim Won Tae WT Kim Yong-June YJ Lee Sang-Cheol SC Yun Seok-Joong SJ Kim Wun-Jae WJ

Oncology letters 20170619 2

Our previous study reported a clinically applicable prognostic gene classifier for primary non-muscle-invasive bladder cancer (NMIBC). The present study aimed to perform long-term validation of this classifier in the prediction of muscle-invasive disease. Previously published gene expression profiles were used from 176 patients with NMIBC with extended follow-up. Progression was defined as development of muscle invasion or metastasis, and the progression risk score was calculated using the previ ...[more]

PMID: 28781684

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Project description:Purpose: We aimed to develop and validate a novel gene signature from published data and improve the prediction of survival in muscle-invasive bladder cancer (MIBC). Methods: We searched the published gene signatures associated with the overall survival (OS) of MIBC and compiled all 274 genes to develop a novel gene signature. RNAseq data of TCGA (the Cancer Genome Atlas) bladder cohort were downloaded. All genes were included in a univariate Cox hazard ratio model. We then used a reduced multivariate Cox regression model, which included only genes achieving P < 0.05 in the univariate model. A total of 172 patients at Fudan University Shanghai Cancer Center (FUSCC) and 61 patients from GEO datasets were used as an external validation set. Results: A total of 327 patients in the TCGA cohort were enrolled. We identified 274 genes from eight published papers on the OS of MIBC. Using the TCGA database, we identified 12 genes that correlated with OS (P < 0.05 in both univariate and multivariate analyses). By integrating these genes with the RT-qPCR data in our validation dataset and GEO datasets, we confirmed that the power for predicting OS of the 12-gene panel (AUC of 0.741 and 0.727, respectively) was higher than just clinical data (including gender, age, T stage, grade, and N stage) alone in the TCGA and FUSCC cohort (AUC of 0.667 and 0.631, respectively). Additionally, upon combining the clinical data and 12-gene panel together, the AUC increased to 0.768, 0.757, and 0.88 in the TCGA, FUSCC and GSE13507 cohorts, respectively. Conclusions: Applying published gene signatures and TCGA data, we successfully built and externally validated a novel 12-gene signature for the survival of MIBC.Brief explanationWe systemically reviewed all published prognostic gene signatures of muscle-invasive bladder cancer (MIBC) and integrated the genes in the TCGA MIBC cohort. This new gene panel was validated in a newly established MIBC cohort in GEO and FUSCC. This method can help update the previous established panels in a new way.

Project description:Brief descriptionThe results demonstrate that the European Organisation for Research and Treatment of Cancer (EORTC) scale provides the best recurrence and progression prediction in comparison with European Association of Urology (EAU) and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk scores among a mixed population of patients with non-muscle-invasive bladder who were treated with, or without, Bacillus Calmette-Guerin (BCG) and without any immediate postoperative chemotherapy. The study highlights the role of tumor diameter and extent in transition prediction. This retrospective cohort analysis of 322 patients with newly diagnosed non-muscle-invasive bladder cancer (NMIBC) assesses the concordance and accuracy of predicting recurrence and progression by EAU-recommended tools (EAU risk groups, EORTC, and CUETO). One-year and five-year c-indices ranged from 0.55 to 0.66 for recurrence and from 0.72 to 0.82 for progression. AUCROC of predictions ranged from 0.46 for 1-year recurrence risk based on CUETO groups, to 0.82 for 1-year progression risk based on EAU risk groups. Diameter (HR: 1.91; 95% CI: 1.39-2.61) and tumor extent (HR: 1.21; 95% CI: 1.01-1.46 for recurrence; HR: 3.1; 95% CI: 1.40-6.87 for progression) were shown to be significant predictors in multistate analysis. Lower accuracy of prediction was observed for patients treated with BCG maintenance immunotherapy. The EORTC model (overall c-index c = 0.64; 95% CI: 0.61-0.68) was superior to the EAU (P = .035; .62; 95% CI: 0.59-0.66) and CUETO (P < .001; c = 0.53; 95% CI: 0.50-0.56) models in predicting recurrence. The EORTC model (c = 0.82; 95% CI: 0.77-0.86) also performed better than CUETO (P = .008; c = 0.73; 95% CI: 0.66-0.81) but there was no sufficient evidence that it performed better than EAU (P = .572; c = 0.81; 95% CI: 0.77-0.84) for predicting progression. EORTC and CUETO gave similar predictions for progression in BCG-treated EAU high-risk patients (P = .48). We share anonymized individual patient data. In conclusion, despite moderate accuracy, EORTC provided the best recurrence and progression prediction for a mixed population of patients treated with, or without BCG, and without immediate postoperative chemotherapy.

Dataset Information

Long-term validation of a molecular progression-associated gene classifier for prediction of muscle invasion in primary non-muscle-invasive bladder cancer.

Publications

Long-term validation of a molecular progression-associated gene classifier for prediction of muscle invasion in primary non-muscle-invasive bladder cancer.

Similar Datasets

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