The Spatial Proximity of CD8+ FoxP3+PD-1+ Cells to Tumor Cells: A More Accurate Predictor of Immunotherapy Outcomes in Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: To optimize precision immunotherapy for advanced NSCLC, comprehensive tumor immune microenvironment (TIME) characterization is crucial for efficacy prediction. Methods: Pretreatment tumor samples from 46 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors were analyzed. The subregional abundance and spatial proximity scores of TIME cell subpopulations in 27 samples were assessed via multiplex immunohistochemistry (mIHC) targeting pan-CK, CD163, CD8, FoxP3, PD-1, and PD-L1. Correlations between the TIME features, clinicopathologic factors, treatment response, and prognosis were evaluated. Results: CD8+FoxP3+ cells were identified in NSCLC tissues, predominantly expressing PD-1/PD-L1. The PD-L1 TPS subgroups showed significant immune cell density/proximity differences, but CD8+FoxP3+PD-1+ infiltration was PD-L1 TPS-independent. Responders had higher CD8+FoxP3+PD-1high density (p = 0.0497) and proximity scores (p = 0.0099) than non-responders. The CD8+FoxP3+PD-1+ presence and tumor proximity were essential for favorable outcomes. In low-PD-L1 TPS patients, the CD8+FoxP3+PD-1+ abundance and proximity scores strongly predicted the response (AUC: 0.79 and 0.75 vs. PD-L1 TPS AUC = 0.58). A survival analysis linked the presence and proximity score of CD8+FoxP3+PD-1+ cells to prolonged overall survival (OS) and progression-free survival (PFS). Notably, a low proximity score of CD8+FoxP3+PD-1+ cells emerged as an independent risk factor for a shorter PFS (HR = 6.16, 95% CI: 2.12–17.93, p = 0.001). Conclusion: The CD8+FoxP3+PD-1+ spatial proximity to tumor cells robustly predicts improved immunotherapy outcomes in advanced NSCLC.
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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 | 0.001 |
| 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".