Temporal Effect on <scp>PD</scp>‐<scp>L1</scp> Detection and Novel Insights Into Its Clinical Implications in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVES: Several studies rely on archived tissue blocks to assess the PD-L1 scores; however, a detailed analysis of potential variations of scores between fresh and archived tissue blocks still lacks. In addition, the prognostic implications of PD-L1 in lung cancers have not yet been completely understood. Here, we aimed to investigate the temporal variation in PD-L1 scores from clinical samples and the clinical implications of PD-L1 in non-small cell lung cancer (NSCLC). METHODS: NSCLC cases from January 2005 to June 2023 were considered for this study, and PD-L1 scores in archived and fresh tissue blocks were analyzed. Association of PD-L1 with various driver mutations was explored, and implications of PD-L1 in progression-free survival (PFS) and overall survival (OS) were analyzed. RESULTS: Our study revealed a significant disparity in PD-L1 scores between archived and fresh tissue blocks, and a temporal variation in scores within 6 months of tissue acquisition. Advanced-stage primary tumors, metastatic lymph nodes, and visceral pleural invasion revealed higher PD-L1 expression as presented by tumor proportion score (TPS). Notably, in fully resected stage I/II NSCLC cases, OS was better in the high PD-L1 (≥ 50% TPS) cohort with driver mutations compared to cases without driver mutations (hazard ratio-0.5129, 95% confidence interval 0.2058-1.084, p = 0.0779). In contrast, high PD-L1 was associated with worse OS compared to no PD-L1 (< 1% TPS) (hazard ratio-2.431, 95% confidence interval 1.144-6.656, p = 0.0242) in the cohort without driver mutations. Furthermore, the presence of a KRAS mutation favored the outcome of anti-PD-L1/PD1 immunotherapy in advanced NSCLC. CONCLUSION: PD-L1 detection from tissue blocks was found to vary temporally, urging for a prioritized consideration for patients with marginal scores when archived blocks are employed for its detection. Prognostic roles of PD-L1 were associated with driver mutations, and KRAS mutations favored the outcome of anti-PD-L1/PD1 therapy in advanced NSCLC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".