Immune gene expression profiles in high-grade urothelial carcinoma of the bladder: a NanoString study
Bibliographic record
Abstract
Aims The advent of immune checkpoint inhibitor therapy has proven beneficial in a subset of high-grade urothelial carcinomas (HGUC) of the bladder. Although treatment selection is currently largely determined by programmed death-ligand 1 (PD-L1) status, multiple factors in the immune system may modulate the host immune response to HGUC and immunotherapy. In this pilot study, we used a transcriptomic approach to identify the immune milieu associated with PD-L1 expression to enhance our understanding of the HGUC immune evasion network. Methods The immune transcriptome of 40 HGUC cystectomy cases was profiled using the NanoString nCounter Human V.1.1 PanCancer Panel. All cases were assessed for associated PD-L1 status (SP263) using whole tissue sections. PD-L1 status was determined as high or low using 25% tumour and/or immune cell staining. Results The most significantly differentially expressed gene was PD-L1 messenger RNA ( CD274 ), which strongly correlated with protein expression (r=0.720, p<0.001). The sensitivity, specificity, positive and negative predictive values of CD274 for PD-L1 expression were 85%, 96%, 92% and 93%, respectively. The PD-L1 associated gene signature also included complement components C1QA and CD46 and NOD2 (innate immune system), proinflammatory cytokines CXCL14, CXCL16, CCL3, CCL3L1 and OSM along with the immune response mediator SMAD3, among others. Pathway analysis determined enrichment of these genes in interleukin-10 production, lymphocyte chemotaxis and aberrant IFNγ, NF-κB and ERK signalling networks. Conclusions We report key genes and pathways in the immune transcriptome and their association with PD-L1 status, which may be involved in immune evasion of HGUC and warrants further investigation.
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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".