Effects of serotonergic drugs on immune checkpoint inhibitor response: a pooled analysis of individual patient data from four Canadian Cancer Trials Group (CCTG) trials
Bibliographic record
Abstract
Background Immune checkpoint inhibitors (ICIs) are used to treat various cancers. Serotonin receptors are expressed by immune cells. In preclinical models, serotonergic drugs affect T-cell cytokine production, proliferation and apoptosis. Interactions between clinical serotonergic drug use and dual ICI treatment remain unknown.Methods Individual patient data were pooled from 4 Canadian Cancer Trials Group (CCTG) trials of patients treated with dual ICI ± chemotherapy (n = 684). Serotonergic drug use was correlated with clinicopathologic characteristics, best overall response (BOR)/iBOR per RECIST 1.1/iRECIST, progression-free survival (PFS)/iPFS, overall survival (OS) and immune-related adverse events (irAEs) using Cochran – Mantel – Haenszel and log-rank tests.Results Eighty-three (12%) patients used serotonergic drugs at baseline and 118 (17%) at any time on trial. By multivariate analysis, serotonergic drug use at baseline was significantly associated with decreased iBOR (p = 0.04), but not PFS (p = 0.21), iPFS (p = 0.28), OS (p = 0.30) or incidence of grade 1/2 or 3/4 irAE (p = 0.85 and 0.99 respectively). Results were not significantly different with serotonergic drug use at any time on trial.Conclusion Use of serotonergic drugs did not impact PFS or OS in patients treated with dual ICI ± chemotherapy. This study supports the safe use of serotonergic drugs in the context of dual ICI therapy.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".