CD8+ T Cells and PD-L1 Expression as Prognostic Indicators in a Low Prevalence of HPV-Associated Oropharyngeal Squamous Cell Carcinoma
Bibliographic record
Abstract
Human papillomavirus (HPV) infection detected in oropharyngeal squamous cell carcinoma (OPSCC) is associated with a better survival outcome from previous literature. However, Thailand and several Asian countries have a low prevalence of HPV-associated OPSCC and, therefore, have a low positive rate of immunostaining with p16. Tumor microenvironments (TME), including tumor-infiltrating CD8+ lymphocytes (CD8+ TIL) and programmed death ligand 1 (PD-L1), are proposed as significant prognostic indicators in addition to p16. Objectives: Explore the expression p16, CD8+ TIL, and PD-L1 and its value as prognostic indicators for overall survival (OS) in patients with OPSCC. Materials and Methods: Data from patients with OPSCC diagnosed from 2012 to 2018 were recovered from medical records and national registry. All available glass slides and slides of immunohistochemistry (IHC) of p16, CD8, and PD-L1 were reviewed. The TME was classified into four types according to the expression pattern of PD-L1 and CD8+TIL. Overall survival (OS) was assessed using the Kaplan–Meier method and Cox regression model analysis. Results: In 160 OPSCC patients, p16 was positive in 27 (16.88%). The density of CD8+ TIL was higher in the p16+ and PD-L1+ groups (p = 0.005, 0.039); however, there was no association between p16 and the status of PD-L1. P16 and CD8+ TIL were significant prognostic factors for better OS (p = 0.007, 0.001), but not PD-L1 status (p = 0.317). Among the four types of TME, carcinoma showed mainly type IV TME (PD-L1−/TIL+), while OPSCCs with type I TME (PD-L1+/TIL+) had the best survival outcome. Conclusions: The positivity of p16 and the density of CD8+ TIL were associated with better OS in OPSCC, while the status of PD-L1 was not significantly related to OS. OPSCC with type I TME (PD-L1+/TIL+) showed the best prognosis of all types of TME.
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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 | 0.001 | 0.001 |
| 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".