Myeloid immunosuppressive state as a predictor of rapidly progressive phenotype and poor survival in advanced non-small cell lung cancer (NSCLC) patients treated with PD-1/PD-L1 inhibitors.
Bibliographic record
Abstract
e20594 Background: Immune subpopulations within the tumor microenvironment (TME) play a central role in determining response to checkpoint inhibitors. Myeloid derived suppressor cells, a heterogeneous population of immature myeloid cells, have a predominantly immunosuppressive role by stimulating T regulatory cells. We hypothesize that elevated myeloid-to-lymphocyte measures in the peripheral blood predict for greater numbers of myeloid derived suppressor cells in the TME and worse outcomes. Methods: In advanced NSCLC patients who received immunotherapy between 2010-2018, baseline characteristics collected retrospectively included age, sex, histology, stage, smoking status, ethnicity, PD-L1 expression and tumor genotype. Pre-treatment neutrophil/lymphocyte (NLR) and monocyte/lymphocyte ratios (MLR) were log transformed and analyzed using cox and logistic regression models. Results: Among 219 eligible patients, a high NLR was associated with shorter time-to-treatment-failure (HR 1.38, 95%CI 1.09-1.75, p = 0.008) and poorer OS (HR 1.62, 95%CI 1.23-2.14, p < 0.001), independent of PD-L1 levels. Disproportionate increases in NLR and MLR were highly correlated (Spearman’s rho = 0.78). Further, higher NLR (p = 0.09) or MLR (p = 0.06) tended to associate with best overall response (BOR) to immunotherapy, with higher rates of progressive disease (PD) and lower rates of clinical response. A high NLR (p = 0.01) and MLR (p = 0.02) were associated with a rapidly progressive phenotype defined by PD as the BOR and duration of therapy ≤2 months. This remained significant after adjusting for confounders in a multivariate model (p = 0.03 for NLR and p = 0.03 for MLR). No associations were observed between high myeloid counts and other clinical prognostic factors such as liver metastases. Conclusions: A myeloid immunosuppressive state characterized by a disproportionate increase in peripheral immune myeloid populations is significantly associated with primary refractory disease, rapidly progressive phenotype, and poorer survival. Further investigation into myeloid mediated mechanisms of resistance is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".