Evaluating the significance of combining PD-L1 and TILs as biomarkers in non-small cell lung cancer patients treated with immunotherapy: a systematic review
Bibliographic record
Abstract
In advanced non-small cell lung cancer (NSCLC), programmed death-ligand 1 (PD-L1) expression is a well-established but suboptimal biomarker for predicting response to immune checkpoint inhibitors (ICIs). Tumor-infiltrating lymphocytes (TILs), particularly CD8+ subsets, have demonstrated potential as complementary biomarker. Despite existing data on each biomarker individually, the combined effect is not fully understood. A systematic search of Ovid/Medline, Embase, and Web of Science identified studies on CD8+ TILs and PD-L1 in NSCLC patients treated with ICIs. The primary outcomes were progression-free survival (PFS) and overall survival (OS). Secondary endpoints included objective response rate (ORR) and durable clinical benefit (DCB). Study quality was assessed using the Newcastle-Ottawa Scale. Thirteen studies (2490 patients) were included. PD-L1 expression was associated with longer PFS in 6 of 8 studies (HR: 0.67, 95% CI: 0.49-0.90) but did not significantly correlate with OS. TILs alone showed no significant predictive value for PFS or OS. However, combining both biomarkers provided the strongest predictive value for PFS (HR: 0.39, 95% CI: 0.27-0.57) and OS (HR: 0.42, 95% CI: 0.31-0.56). Combining PD-L1 and TILs may more effectively predict PFS and OS than either biomarker alone, though their clinical application remains complex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| 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".