Investigation of PD-L1 Biomarker Testing Methods for PD-1 Axis Inhibition in Non-squamous Non–small Cell Lung Cancer
Bibliographic record
Abstract
Inhibitors of the programmed cell death 1 (PD-1) signaling axis have recently demonstrated efficacy and are rapidly being incorporated into the treatment of non-small cell lung cancers (NSCLCs). Despite clear benefits to certain patients, the association of these responses with a predictive biomarker remains uncertain. Several different biomarkers have been proposed, with differing results and conclusions. This study compares multiple methods of biomarker testing for treatment of NSCLCs with PD1-axis inhibitors. Tissue microarrays of matched primary and metastatic NSCLCs were used to compare four different PD-1 ligand (PD-L1) IHC techniques, as well as RNA ISH. Additional cases with whole genome and transcriptome data were assessed for molecular correlates of PD-L1 overexpression. Eighty cases were included in the IHC study. Multiple IHC methodologies showed a high rate of agreement (Kappa = 0.67). When calibrated to RNA expression, agreement improved significantly (Kappa = 0.90, p=0.0049). PD-L1 status of primary and metastatic tumors was discordant in 17 (22%) cases. This study suggests that different IHC methodologies for PD-L1 assessment provide slightly different results. There is significant discordance between the PD-L1 status of primary tumors and lymph node metastases. RNA ISH may be a useful adjunct to complement PD-L1 IHC testing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".