Background/Objectives: High recurrence rates and intensive lifelong surveillance make bladder cancer among the costliest malignancies to treat. Although Bacillus Calmette-Guérin (BCG) immunotherapy is the standard treatment for high-risk non-muscle-invasive bladder cancer (NMIBC), up to 50% of patients fail to respond, and predictive biomarkers are lacking. Molecular profiling has established three BCG response subtypes (BRS1-3), with BRS3 characterized by an immunosuppressive, BCG-resistant phenotype; however, these features have not been validated at single-cell spatial resolution. Methods: We applied imaging mass cytometry (IMC) to 82 BCG-treated high-risk NMIBC samples and performed (i) single-cell IMC with unsupervised clustering to identify phenotypic cell clusters and quantify cluster abundances and (ii) a convolutional neural network-based gated attention multiple instance learning model trained on IMC images (IMC-GA-MIL) to predict BCG response. Cluster abundances were summarized using II (immune composition within the immune compartment), TT (tumor phenotypic composition), and IT (immune/stromal abundance relative to tumor cells) indices. Results: Single-cell IMC identified 18 distinct phenotypic cell clusters. In BCG responders, immune cells localized within the tumor compartment were enriched and independently protective (HR 0.67, 95% CI 0.49-0.92). BCG nonresponse was associated with a higher abundance of fibroblast-dominant clusters relative to tumor cells (IT index). Plasma cell-dominant clusters were the strongest predictors of progression (II index HR 2.28, 95% CI 1.37-3.79; IT index HR 1.25, 95% CI 1.06-1.48). The IMC-GA-MIL model predicted BCG response with 90% accuracy (9/10) and identified myeloid- and T-cell-associated marker patterns involving CD14, CD11b, CD68, CD8, and FOXP3 as the most informative contributors. Conclusions: Spatial single-cell profiling and IMC-GA-MIL identify spatial immune and stromal features associated with BCG failure. However, findings from both analyses should be considered exploratory and will require validation in larger, independent cohorts.
Spatial Immune Profiling and AI-Based Classifiers Identify Predictors of BCG Therapy Outcomes in High-Risk Non-Muscle-Invasive Bladder Cancer.
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作者:Lillesand Melinda, Austdal Marie, Mroz Jakub, Skaland Ivar, Gudlaugsson Einar, Jong Florus C de, Zuiverloon Tahlita C M, Engan Kjersti, Janssen Emiel A M
| 期刊: | Cancers | 影响因子: | 4.400 |
| 时间: | 2026 | 起止号: | 2026 Mar 13; 18(6):938 |
| doi: | 10.3390/cancers18060938 | ||
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