Prognostic Significance of CD4+ and CD8+ Tumor-Infiltrating Lymphocytes in Head and Neck Squamous Cell Carcinoma: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: It has been suggested that the presence of tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment is associated with a better prognosis in different types of cancer. In this systematic review and meta-analysis, we investigated the prognostic role of CD4+ and CD8+ TILs in head and neck squamous cell carcinoma (HNSCC). METHODS: PubMed, Cochrane, Embase, Scopus, and Web of Science were searched up to September 2020. This study was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist. Risk ratios from individual studies were displayed in forest plots and the pooled hazard ratios (HR) of death and corresponding confidence intervals (CI) were calculated according to random-effects models. Risk of bias of the included studies was assessed through the Newcastle-Ottawa scale. RESULTS: 28 studies met the inclusion criteria. Studies conducted on HNSCC subsites combined reported a significant reduction in the risk of death for both high CD4+ (HR: 0.77; 95% CI: 0.65-0.93) and high CD8+ TILs (HR: 0.64; 95% CI: 0.47-0.88). High CD4+ TILs were associated with significantly better overall survival among oropharyngeal HNSCC (HR: 0.52; 95% CI: 0.31-0.89), as well as high CD8+ TILS in Human papillomavirus -ve and +ve cancers (HR: 0.39; 95% CI: 0.16-0.93 and HR: 0.40; 95% CI 0.21-0.76 respectively). CD8+ TILs were also associated with improved survival in hypopharyngeal cancers (HR = 0.43 CI: 0.30-0.63). No significant association emerged for patients with cancer of the oral cavity or larynx. CONCLUSIONS: The findings from this meta-analysis demonstrate the prognostic significance of CD8+ and CD4+ TILs in HNSCC and variation in tumor subsite warrants further focused investigation. We highlight how TILs may serve as predictive biomarkers to risk stratify patients into treatment groups, with applications in immune-checkpoint inhibitors notable areas for further research.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.058 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".