CD8+ Tumor-Infiltrating Lymphocyte Abundance Is a Positive Prognostic Indicator in Nasopharyngeal Cancer
Bibliographic record
Abstract
PURPOSE: Tumor-infiltrating lymphocytes (TIL) are immune cell populations found within tumors, critical in the antigen-specific host immune response. In this study, we aimed to elucidate the prognostic significance of CD3+, CD4+, and CD8+ TILs in nasopharyngeal cancer (NPC). EXPERIMENTAL DESIGN: Immune cell infiltration was quantified in NPC samples (n = 50) using RNA-sequencing (RNA-seq) data based on rearranged T-cell receptor (TCR) reads and the Estimation of Stromal and Immune cells in malignant tumors using expression data (ESTIMATE) immune score tool. The differential abundances of TIL subset populations were also characterized through IHC staining of formalin-fixed, paraffin-embedded samples from a training cohort (n = 35), which was a subset of the RNA-seq cohort (n = 50). RESULTS: In the RNA-seq cohort, patients with higher rearranged TCR reads experienced superior 5- and 10-year overall survival (OS; P < 0.001), and disease-free survival (DFS; P < 0.001). Similarly, patients with higher ESTIMATE immune scores experienced superior 5- and 10-year OS (P = 0.024) and DFS (P = 0.007). In the training cohort, high abundances of CD8+ TILs were significantly associated with improved 5- and 10-year OS (P = 0.003) and DFS (P = 0.005). These findings were corroborated in an independent validation cohort (n = 84), and combined analysis of the training and validation cohorts [n = 119 (35+84)], which further demonstrated improved 5- and 10-year survival in terms of locoregional control (P < 0.001) and distant metastasis (P = 0.03). CONCLUSIONS: Taken together, our study highlights the prognostic value of CD8+ TILs in NPC, and the potential of future investigations into cellular-based immunotherapies employing CD8+ lymphocytes.
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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".