Improving outcome and prognosis prediction in non-muscle invasive bladder cancer using a gene expression score

Background: Perioperative cisplatin-based chemotherapy (CBC) can improve the outcome of patients with muscle-invasive bladder cancer (MIBC), but it is still to be defined which patients benefit. Mutations in DNA damage response genes (DDRG) can predict the response to CBC. The value of DDRG expression as a marker of CBC treatment effect remains unclear. Material and methods: RNA expression of the nine key DDRG (BCL2, BRCA1, BRCA2, ERCC2, ERCC6, FOXM1, RAD50, RAD51, and RAD52) was assessed by qRT-PCR in a cohort of 61 MICB patients (median age 66 y, 48 males, 13 females) who underwent radical cystectomy in a tertiary care center. The results were validated in the The Cancer Genome Atlas (TCGA) cohort of MIBC (n = 383). Gene expression was correlated with disease-free survival (DFS) and overall survival (OS). Subgroup analyses were performed in patients who received adjuvant cisplatin-based chemotherapy (ACBC) (Mannheim n = 20 and TCGA n = 75). Results: Low expression of RAD52 was associated with low DFS in both the Mannheim and the TCGA cohorts (Mannheim: p = 0.039; TCGA: p = 0.017). This was especially apparent in subgroups treated with ACBC (Mannheim: p = 0.0059; TCGA: p = 0.012). Several other genes showed an influence on DFS in the Mannheim cohort (BRCA2, ERCC2, FOXM1) where low expression was associated with poor DFS (p < 0.05 for all). This finding was not fully supported by the data in the TCGA cohort, where high expression of FOXM1 and BRCA2 correlated with poor DFS. Conclusion: Low expression of RAD52 correlated with decreased DFS in the Mannheim and the TCGA cohort. This effect was especially pronounced in the subset of patients who received ACBC, making it a promising indicator for response to ACBC on the level of gene expression.

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Evaluation of a 29 gene classifier for basal/non basal prediction in muscle-invasive bladder cancer FFPE samples.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.6_suppl.467 ◽

2018 ◽

Vol 36 (6_suppl) ◽

pp. 467-467

Author(s):

Yves Allory ◽

Nanor Sirab ◽

Damien Drubay ◽

David Gentien ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Diagnostic Tool ◽

Predictive Accuracy ◽

External Validation ◽

Invasive Bladder Cancer ◽

Muscle Invasive Bladder Cancer ◽

Data Set ◽

Ffpe Samples ◽

Muscle Invasive

467 Background: Recent and independent muscle-invasive bladder cancer (MIBC) molecular classifications identified the basal / squamous-like (BASQ) tumours as an intrinsic and robust subtype, with a poor outcome and possible chemosensitivity to cisplatin based regimen, making mandatory the development of a diagnostic tool for their identification in routine samples. Our study aimed to evaluate the diagnostic accuracy of a Nanostring classifier for tumor subtype prediction on FPPE specimens. Methods: Two series of MIBC were used (CIT n = 51 & Stransky n = 22) for which BASQ tumours were identified previously using Affymetrix transcriptome data obtained from frozen samples (Rebouissou, Science Trans Med 2014). 29 genes were selected to predict the basal subtype, RNA expression of matched frozen and FFPE samples was studied using Nanostring technology. To define the classifiers for Affymetrix, frozen and FFPE Nanostring expression matrix on CIT samples the centroid of each cluster was calculated using the expression of 29 genes. Internal validation used leave-one-out cross-validation to train and test the prediction accuracy of the new classifier. For external validation, the CIT samples were used as training set and the Stransky samples as validation set. Predictive accuracy expressed as percentage of correctly classified samples is provided. Results: Correlations between Affymetrix, frozen and FFPE Nanostring data set were checked for gene expression and samples. Using CIT samples as train and test set, the predictive accuracy for BASQ tumour identification was for Affymetrix, frozen Nanostring and FFPE Nanostring classifiers, 90.20% [78.59%; 96.74%], 88.24% [76.13%; 95.56%] and 92.16% [81.12%; 97.82%], respectively. Using training on CIT samples and test on Stransky samples, this predictive accuracy was 90.91% [70.84%; 98.88%] both for Affymetrix, frozen Nanostring and FFPE Nanostring classifiers. Conclusions: The 29 gene expression Nanostring codeset was able to identify reliably basal / squamous like tumours on FFPE samples from MIBC in comparison with the gold standard approach based on transcriptomic profile, appearing as a promising diagnostic tool.

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