HLA-E and NKG2A Mediate Resistance to BCG Immunotherapy in Non-Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
ABSTRACT Background Bacillus Calmette-Guérin (BCG) is the standard of care treatment for high-risk non-muscle-invasive bladder cancer (NMIBC), yet many patients develop recurrent disease despite evidence of ongoing immune activation. We investigated mechanisms of immune escape in BCG-unresponsive tumors and evaluated the therapeutic potential of targeting the HLA-E/NKG2A axis. Methods Single-cell RNA sequencing, spatial immunophenotyping, proteomic profiling, and functional ex vivo assays were performed using tumors and urine samples from patients with BCG-naïve and BCG-unresponsive NMIBC. Results BCG-unresponsive tumors were enriched for HLA-E-expressing malignant cells compared with BCG-naïve tumors. Increased HLA-E expression was associated with enhanced IFN-γ signaling and was induced by IFN-γ stimulation in primary tumor cells and bladder cancer tumor lines. Spatial analyses demonstrated accumulation of NKG2A + NK and CD8 T cells in proximity to HLA-E high tumor cells, with increased NKG2A:HLA-E interactions in BCG-unresponsive tumors. Despite high expression of cytotoxic mediators, NKG2A + effector cells displayed impaired degranulation. Blockade of NKG2A with monalizumab restored degranulation of and cytotoxicity by tumor-infiltrating lymphocytes in autologous tumor co-cultures. Conclusions BCG-unresponsive NMIBC tumors are enriched for HLA-E-expressing tumor cells and NKG2A + effector lymphocytes, with increased engagement of the HLA-E/NKG2A axis within the tumor microenvironment. These findings identify the HLA-E/NKG2A axis as a therapeutic vulnerability and provide a rationale for clinical evaluation of NKG2A blockade as a bladder-sparing strategy for patients with BCG-unresponsive disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".