Comprehensive Assessment of PD-L1 Staining Heterogeneity in Pulmonary Adenocarcinomas Using Tissue Microarrays
Bibliographic record
Abstract
Checkpoint inhibitors directed against programmed death receptor 1 (PD-1) and its ligand (PD-L1) changed the treatment of advanced lung non-small cell carcinomas. The decision to treat patients is influenced by PD-L1 expression by tumor cells, but evidence indicates that this staining is heterogenous within a tumor. As PD-L1 staining is tested mostly on biopsies, false negative results can occur due to sampling issues. The clinical impact of this heterogeneity has not been established. We selected 241 patients who underwent pulmonary resection for adenocarcinoma. Tissue microarrays were constructed with five 1 mm cores representative of the histologic patterns observed in each tumor and stained for PD-L1. For each core, the histologic pattern and the percentage of PD-L1 positive tumor cells were noted. Staining heterogeneity was defined as cases with both positive and negative cores at positivity thresholds of 1%, 10%, and 50% of tumor cells. At the 50% cut-off, 37.8% of patients were PD-L1 positive, whereas 22.4% showed staining heterogeneity. Among patients with 1 negative core, 26.5% also had a positive core and could have been misclassified based on 1 biopsy. Mean staining of PD-L1 was higher in solid (47.9%) and micropapillary (24.2%) patterns and was lower in acinar (14.1%), papillary (3.4%), and lepidic (6.4%) architectures. A significant proportion of patients presented a heterogenous staining for PD-L1. A total of 26.5% of patients negative on 1 core turned out to be positive on another core, which raises the consideration of rebiopsy, in particular when lepidic, acinar, or papillary patterns are observed on a biopsy.
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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) | 0.000 | 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".