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SWI/SNF complex alterations predict immunotherapy response in bladder cancer

Jian Zhang1, Yapeng Wang1, Qian Yan1

  • 1Department of Urology, Daping Hospital, Army Medical University, Chongqing, China.

Frontiers in Immunology
|December 24, 2025
PubMed
Abstract

Insights

SWI/SNF alterations in urothelial bladder cancer (UBC) predict better response to immunotherapy. These genetic changes create an inflamed tumor microenvironment, improving patient survival and guiding precision treatment strategies.

Area of Science:

  • Oncology
  • Genetics
  • Immunotherapy

Background:

  • Immune checkpoint inhibitors (ICIs) have transformed urothelial bladder cancer (UBC) treatment, but only benefit a subset of patients.
  • The role of SWItch/sucrose non-fermentable (SWI/SNF) chromatin remodeling complex alterations in UBC and their impact on ICI response are not well understood.

Purpose of the Study:

  • To investigate the frequency and functional significance of SWI/SNF gene mutations in UBC.
  • To evaluate SWI/SNF alterations as predictive biomarkers for response to immune checkpoint blockade therapy.

Main Methods:

  • Analysis of tumor specimens from 49 patients and integration with five public cohorts (2,280 cases).
  • Somatic alteration identification via sequencing and transcriptomic profiling via RNA sequencing.
  • Survival analysis, immune landscape characterization, and machine-learning-based prognostic modeling.

Main Results:

  • SWI/SNF alterations were found in 42.8% of UBC cases, notably in ARID1A, ARID1B, ARID2, SMARCA4, and PBRM1.
  • SWI/SNF-mutant tumors exhibited higher tumor mutational burden, increased neoantigen load, and an immune-inflamed microenvironment.
  • These tumors showed significantly improved overall survival with ICI treatment (p < 0.05), with genotype-specific models demonstrating strong prognostic discrimination.

Conclusions:

  • SWI/SNF alterations represent a key biomarker for stratifying UBC patients who respond to immunotherapy.
  • Developed genotype-specific prognostic models offer a practical framework for optimizing precision immunotherapy in UBC.
Keywords:
SWI/SNFbladder cancerimmune checkpoint inhibitorsmachine-learningprediction model

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