Angiotensin-converting enzyme (ACE) inhibitor prescription affects non-small-cell lung cancer (NSCLC) patients response to PD-1/PD-L1 immune checkpoint blockers
Bibliographic record
Abstract
Angiotensin-converting enzyme (ACE) inhibitors are frequently used to treat hypertension and congestive heart failure. Preclinical data show that ACE plays a role on both innate and adaptive immune responses. Since interactions between ACE inhibitors and immune checkpoint inhibitors (ICIs) have not been reported, the aim of this study is to investigate the influence of ACE inhibitors on non-small cell lung cancer (NSCLC) patients treated with programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors. We conducted a retrospective cohort analysis of NSCLC patients treated with PD-1/PD-L1 inhibitors. Clinical and co-medication data as well as tumor biopsies were collected. Groups were defined according to patients’ co-medications at the time of ICI initiation. Among the 178 patients included, 22 (13.1%) received ACE inhibitors. While baseline characteristics were similar in both groups, ACE inhibitors group had a shorter median PFS (Progression-Free Survival) compared to the control group: 1.97 vs. 2.56 months, p = .01 (HR = 1.8 CI95% 1.1–2.8). Using CIBERSORT, RNA sequencing suggested that tumors from the ACE inhibitors group had less M1 macrophages, activated mast cells, NK cells and memory activated T cells, thus suggesting an immunosuppressed state. In vitro, the ACE inhibitor, captopril, induced M2 marker at the cell surface of monocytes engaged into M1 differentiation. Thus, ACE inhibitors prescription concomitant to PD-1/PD-L1 inhibitors treatment seems to be associated with impaired outcome and with a tumor immunosuppressed state in patients with advanced NSCLC. These results should be validated in larger prospective cohorts.
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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 | 0.000 | 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".