PD-1 and PD-L1 expression in mycosis fungoides and Sézary Syndrome
Bibliographic record
Abstract
BACKGROUND: The mechanisms involved in mycosis fungoides, and Sezary Syndrome progression are largely unknown. Over the last decade the interest in immune system contrast of neoplasm has grown owing to the introduction of immunotherapy. PD-1 and its ligand (PD-L1) are the target of several immunotherapy treatment. In the literature reports on the expression of PD-1 and PD-L1 have provided contrasting results. METHODS: In our analysis we investigated PD-1 expression in neoplastic cells and in tumor infiltrating lymphocytes (TILs) as well as PD-L1 expression in tumor cells and in tumor associated macrophages (TAMs). PD-L1 and PD-1 positive cells were counted in 5 high-power fields (HPF) and scored as the average number of positive neoplastic cells/TILs/TAMs per HPF. RESULTS: From databases of two institutions (Bologna and Florence) thirty-five patients corresponding to 43 biopsies were retrieved. In seven instances sequential biopsies were present. No statistically significant expression was observed comparing early to advanced stages by analysing PD-1 by tumor cells and TILs and of PD-L1 by tumor cells and TAMs. CONCLUSIONS: Our results corroborate that PD-1 and PD-L1 expression is not stage-dependent in mycosis fungoides and Sezary syndrome. However, PD-1 and PD-L1 expression in affected patients provides a rationale to schedule anti PD-1/PD-L1 drugs.
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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 | 0.001 | 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) | 0.002 | 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".