Intratumoral heterogeneity in programmed death‐ligand 1 immunoreactivity is associated with variation in non‐small cell lung carcinoma histotype
Bibliographic record
Abstract
AIMS: Accurate assessment of programmed death-ligand 1 (PD-L1) levels in non-small cell lung carcinoma (NSCLC) samples is complicated by intratumoral heterogeneity. We aimed to: (i) establish whether intratumoral PD-L1 variation is associated with differences in local histotype; (ii) identify histotypes associated with a tendency for there to be higher or lower PD-L1 scores; and (iii) estimate the frequency of clinically significant discordance in PD-L1 levels between intratumoral histotype areas. METHODS AND RESULTS: We reviewed 166 NSCLC resection specimens clinically tested for PD-L1 with the 22C3 pharmDx assay. Multiple histotypes were present in 55% (68/123) of non-mucinous adenocarcinoma samples. Solid histotypes had significantly higher PD-L1 levels than other histotypes, both when samples were grouped by predominant histotype, and when histotype areas within a tumour were compared (P < 0.02). Lepidic areas had significantly lower PD-L1 levels than other histotype areas within the same tumour (P < 0.02). Discordance between intratumoral histotype areas at a clinically relevant threshold (PD-L1 tumour proportion score of 1% or 50%) was present in 32% (22/68) of non-mucinous adenocarcinoma specimens with multiple histotype areas. The lepidic histotype was most frequently involved in discordance. CONCLUSIONS: Intratumoral heterogeneity in PD-L1 is associated with variation in histotype. Over-representation of solid areas may increase the PD-L1 score assigned to a tumour, whereas over-representation of lepidic areas may decrease the PD-L1 score. Evaluation of how histotype representation impacts on the predictive value of PD-L1 testing is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".