BCG immunotherapy response associated immune transcriptomic and spatial immune profiles of non-muscle invasive bladder tumors
Bibliographic record
Abstract
Abstract Bladder cancer is a management-intensive disease that leads to ~200, 000 deaths worldwide every year. Upon initial diagnosis, approximately 75% of cases are categorized as non-muscle-invasive bladder cancer (NMIBC). Adjuvant Bacillus Calmette-Guérin (BCG) immunotherapy is the gold standard treatment for high risk NMIBC patients. Despite being used for over 40 years, many BCG patients exhibit tumor recurrence, a subset of which display progression to a muscle-invasive form of the disease that requires undesired bladder removal surgery. We recently demonstrated that the interferon response induced by BCG could be further complemented via synergistic activation of the Stimulator of Interferon Genes (STING) pathway. Towards clinical translation of this combinatorial immunomodulation and in order to determine immune biomarkers associated with response to BCG, we retrospectively investigated the immune transcriptomic profiles of tumors from BCG responders and non-responders. Interestingly, tumors from BCG non-responders showed a higher immune cell abundance and higher immune checkpoint gene expression. To further independently validate the transcriptomic findings at protein levels, we conducted spatial immune profiling of 12 immune cell markers (Ki-67+, CD8+ proliferating T cells, FoxP3+ T regulatory cells, CD79+ B cells, PD-1, PD-L1 and IDO1 immune checkpoints, CD68+ and CD163+ M1 and M2 macrophages) in a cohort of 571 NMIBC tumors. Preliminary findings showed that tumors from BCG non-responders are characterized by infiltration of immunosuppressive T regulatory cells. Findings from this study will have the potential to guide future immune biomarker guided immunomodulatory treatment combinations.
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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) | 0.001 | 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".