The benefit of the doubt: PD-L1 status in unresectable stage III NSCLC management
Bibliographic record
Abstract
Programmed death ligand-1 (PD-L1) tumor proportion score (TPS), although imperfect, is the best biomarker identified so far to guide the treatment of metastatic non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICI).Landmark trials in the firstline metastatic setting support its predictive value for response to immunotherapy in this population (1,2).As such, international guidelines recommend single agent anti-PD-1/ PD-L1 treatment in the PD-L1 ≥50% population, while those agents should be combined with either chemotherapy, anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) or both in PD-L1 1-49% and <1% patients (3,4).The role of PD-L1 as a biomarker for consolidation durvalumab after chemoradiotherapy (CRT) in unresectable stage III NSCLC, however, remains controversial.The European Medicines Agency approved adjuvant durvalumab for PD-L1 ≥1% NSCLC (5), while North American health authorities did not make this distinction (6,7).In this study, Bryant et al. conducted a retrospective analysis of 312 patients treated with adjuvant durvalumab after CRT in 2017-2021 (8).Every absolute increase of 25% in PD-L1 TPS was associated with significant improvement of both progression-free survival (PFS) and overall survival (OS).There were also significant benefit in PFS for the PD-L1 ≥50% and 1-49% subgroups compared to the <1% subgroup.OS followed the same trend, although the difference between the 1-49% and <1% was not statistically significant.The cohort of durvalumab patients was compared to a historical cohort from 2015-2016 which did not receive durvalumab.PD-L1 expression was unknown in this second cohort.Subgroups of durvalumab patients with PD-L1 ≥50% and 1-49% both had improved PFS and OS compared to the no-durvalumab patients, but not the <1% subgroup.Bryant et al. results suggest a predictive role of PD-L1 TPS in patient receiving durvalumab.Findings from PACIFIC-R, a retrospective study including 1,399 patients started on durvalumab through an extended access program in 2017-2018, point in the same direction (9).Real-world PFS was longer in PD-L1 ≥1% (22.4 months) versus <1% (15.6 months) patients.Published OS data remain preliminary and do not address PD-L1 expression.Two prior, smaller retrospective analyses of patients who received durvalumab between 2017 and 2020 demonstrated improved outcomes in PD-L1 ≥50% compared to PD-L1 <1% patients, but there was no significant difference between the 1-49% and <1% groups (10,11).Do PD-L1 negative CRT patients actually benefit from adjuvant durvalumab?No conclusions can be drawn from Bryant et al. comparative analysis, since PD-L1 expression was unknown in the no-durvalumab cohort.A posthoc, unplanned analysis of 451 PD-L1 assessable patients from
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".