A retrospective analysis exploring the association of pretreatment neutrophil-to-lymphocyte ratio and immune checkpoint inhibitor outcomes in patients with advanced NSCLC and liver metastases
Bibliographic record
Abstract
Background: Liver metastases (LM) in advanced non-small-cell lung cancer (NSCLC) are associated with poor clinical outcomes. The tolerogenic immune microenvironment in the liver may contribute to inferior response to immune checkpoint inhibitors (ICIs). We hypothesized that the presence of LM may be associated with an expanded peripheral myeloid population, using the neutrophil-to-lymphocyte ratio (NLR) as a surrogate in patients treated with ICIs. Objectives: We evaluated the impact of LM and NLR on clinical outcomes in patients with advanced NSCLC treated with ICIs. Design: This was a retrospective analysis conducted at a single cancer center. Methods: We reviewed the records of 324 patients with advanced NSCLC treated with programmed death ligand-1 (PD-L1) inhibitors as monotherapy or in combination with cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors. Clinical outcomes, including progression-free survival (PFS) and overall survival (OS), were evaluated among patients with and without LM in NLR-High (NLR ⩾ 5) and NLR-Low (NLR < 5) subgroups. Results: = 0.014). Patients with LM who were NLR-High (LM+ NLR-H) had shorter median PFS and median OS (2.0 months and 5.4 months, respectively) compared to patients who had LM and were NLR-Low (LM+ NLR-L, median PFS 4.0 months and median OS 13.3 months). This trend was also observed within the PD-L1 > 50% subgroup. In 42 patients with evaluable response and LM, 25/42 (59.5%) of patients had concordant responses in the liver and extra-hepatic sites. Conclusion: Patients with LM who are NLR-High had the poorest clinical outcomes to ICI, and this appeared to be irrespective of PD-L1 status. Ongoing translational work will provide further insight into tumor myeloid subpopulations that may correlate with treatment resistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".