Prognostic and Predictive Relevance of Tumor-Infiltrating Lymphocytes in Squamous Cell Head–Neck Cancer Patients Treated with Radical Radiotherapy/Chemo-Radiotherapy
Bibliographic record
Abstract
Microenvironmental conditions control the entrance and thriving of cytotoxic lymphocytes in tumors, allowing or preventing immune-mediated cancer cell death. We investigated the role of tumor-infiltrating lymphocyte (TIL) density in the outcome of radiotherapy in a series of squamous cell head−neck tumors (HNSCC). Moreover, we assessed the link between markers of hypoxia and TIL density. One-hundred twenty-one patients with HNSCC treated prospectively with radical radiotherapy/chemo-radiotherapy were analyzed. The assessment of TIL density was performed on hematoxylin and eosin biopsy sections before radiotherapy. TIL density ranged from 0.8 to 150 lymphocytes per ×40 optical field (median 27.5). Using the median value, patients were grouped into two categories of low and high TIL density. Early T-stage tumors had a significantly higher TIL density (p < 0.003), but we found no association with N-stage. Overexpression of HIF1α, HIF2α, and CA9 was significantly linked with poor infiltration by TILs (p < 0.03). A significant association of high TIL density with better disease-specific overall survival and improved locoregional relapse-free survival was noted (p = 0.008 and 0.02, respectively), which was also confirmed in multivariate analysis. It is concluded that HNSCC phenotypes that allow for the intratumoral accumulation of lymphocytes have a better outcome following radical radiotherapy/chemo-radiotherapy. Intratumoral-activated HIF- and CA9-related pathways characterize immunologically cold tumors and may be used as targets for therapeutic interventions.
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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 | 0.000 |
| 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) | 0.002 | 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".