Location of CD39<sup>+</sup> T cell subpopulations within tumors predict differential outcomes in non-small cell lung cancer
Bibliographic record
Abstract
Abstract An improved mechanistic understanding of immunosuppressive pathways in NSCLC is important to develop novel diagnostic and therapeutic approaches. Here, we reveal that the prognostic significance of the rate limiting ectonucleotidases in adenosine production CD39 and CD73 requires knowledge of cell type specific expression and localisation within tumours. In a cohort of early treatment naïve NSCLC patients, high stromal expression of CD39 and CD73 predicts poor outcome. CD39 expression amongst T cells identifies CD39+CD4+ Tregs which predict poor outcome and CD39+CD103+ CTL which confer a survival benefit if high densities are observed inside of the tumour nest. Bulk RNA Seq shows that the TME of NSCLC upregulates regulatory pathways in CD4+ T cells and exhaustion in CD8+ T cells. Analysis of single-cell RNASeq datasets illustrates that CD39+CD4+ Tregs are enriched in Treg signature gene sets, and CD39+CD103+ CTL show gene signatures indicative of an exhausted cytotoxic phenotype with an upregulated expression of CXCL13. Combined knowledge of patterns of distribution and location are required to understand the prognostic impact of CD39+ T cell populations in NSCLC. This study provides an improved understanding of the spatial and functional characteristics of CD39+ T cells and illustrates their significance to patient outcome.
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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".