Small Biopsies Misclassify up to 35% of PD-L1 Assessments in Advanced Lung Non–Small Cell Lung Carcinomas
Bibliographic record
Abstract
Pembrolizumab is an FDA-approved immune-checkpoint (IC) inhibitor that targets programmed cell death protein PD-1, and recent phase III trials have demonstrated its superiority over chemotherapy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). Eligibility for treatment with Pembrolizumab is based on demonstration of PD-L1 expression on tumoral cells using the approved companion test 22C3 PharmDx (Dako). Access to the drug depends on a tumor proportion score (TPS) expressing the PD-L1 protein above predetermined cutoffs. The scoring interpretation guide requires a minimum of 100 viable cells to be considered adequate for evaluation. Recent studies have questioned the adequacy of the sampling process when small biopsies are utilized. To further explore this concern, the viable tumor area of 426 consecutive NSCLC biopsies and surgical excisions submitted for PD-L1 assessment was measured and recorded with corresponding PD-L1 expression. About 14.6% of all biopsies measured <2 mm creating 2 groups (<2 mm and ≥2 mm) whose PD-L1 categories distribution [negative (<1%), low expressor (≥1% and <50%), and positive (≥50%)] were compared. Results were significantly different between both groups (χ test; P=0.0012). To help understand this difference, 1,407,000 in silico simulated biopsies of various sizes were performed on 201 numerical tumors created from digitalized full sections and analyzed. Not only the same results shown in actual biopsies were reproduced, but the model calculated that up to 35% of very small biopsies were misclassified including a mixture of false negative and false positive results. The percentage decreased to 10% with a threshold of 5 mm. In era of precision medicine, appropriate sampling is more than ever critical to achieve accurate assessment of the NSCLC PD-L1. Ignored in most clinical trials, recording of biopsy size would permit refining data analysis and increase predictive accuracy of current and future biomarkers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".