Genomic landscape and tumor microenvironment dynamics in non-muscle invasive bladder cancer

Keywords

non-muscle invasive bladder cancer - BCG-Unresponsive - biomarker

Background: Bladder carcinoma, the second most common genitourinary malignancy, frequently presents as non-muscle invasive bladder cancer (NMIBC), a potentially curable stage. The primary therapeutic objective is to prevent progression to muscle-invasive disease, with bacillus Calmette-Guérin (BCG) immunotherapy as the standard treatment. However, variability in patient responses to BCG, coupled with current BCG shortages, underscores the urgent need for reliable biomarkers to predict treatment outcomes. We aim to explore the genomic landscape and tumor microenvironment dynamics in non-muscle invasive bladder cancer in patients treated with BCG to identify potential predictors of treatment response.

Methods: A total of 106 patients diagnosed with NMIBC and treated with intravesical BCG were included in this study. Patients were categorized into two groups: BCG-responsive (n = 47) and BCG-unresponsive (n = 59), based on their treatment outcomes at a single institution. Immunohistochemistry was employed to assess PD-L1 expression, while MSI was evaluated using a commercial multiplex PCR kit. The mRNA expression profile was analyzed using the nCounter PanCancer Pathways Panel, which encompasses 770 genes.

Results: Among the 106 patients studied, only one (<1%) exhibited microsatellite instability (MSI). PD-L1 expression was detected in 9.4% of cases, with no significant association found between PD-L1 expression and BCG responsiveness. Eight genes were identified as differentially expressed between the BCG-responsive and BCG-unresponsive groups. High mRNA expression levels of LAMB3, CCND2, and LILRA3 were associated with BCG responsiveness, while elevated mRNA expression of RRMA2, ANLN, UBEC2C, KIF2C, and MAGEA3/A6 were linked to BCG unresponsiveness. ROC curves were generated for each gene, with all except MAGEA3/A6 demonstrating an area under the curve (AUC) greater than 0.7. Lasso regression analysis highlighted five key genes: LAMB3, CCND2, LILRA3, UBEC2C, and KIF2C. A risk score model based on these five genes yielded an ROC curve with an AUC of 0.844, a sensitivity of 66.1%, and a specificity of 91.5%.

Conclusion: In NMIBC, microsatellite instability (MSI) is rare, and PD-L1 expression is observed in a small subset of cases. A five-gene signature associated with BCG responsiveness has been identified, suggesting a potential new prognostic tool for predicting response to BCG treatment in high-risk NMIBC patients.

Corresponding author: Luis Eduardo Rosa Zucca (e-mail: [e-mail]).

 

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Die Autoren geben an, dass kein Interessenkonflikt besteht.

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Artikel online veröffentlicht:
06. Mai 2025