Effects of cannabinoids on immune checkpoint inhibitor response: CCTG pooled analysis of individual patient data
Bibliographic record
Abstract
Background Immune checkpoint inhibitors (ICIs) benefit patients across various tumor types. ICIs block cancer and T-cell interactions whereas cannabinoids may inhibit T-cell activation, reducing lysis of tumor cells. Interactions between cannabinoid use and dual ICI treatment remain unknown.Methods Individual patient data from 4 Canadian Cancer Trials Group (CCTG) trials of patients treated with dual ICI ± chemotherapy (n = 684) were pooled. Cochran – Mantel – Haenszel and log-rank tests (stratified by trial/treatment arms) correlated cannabinoid use 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).Results Sixty-five (9.5%) patients took cannabinoids at any time on trial, 32 (4.7%) of which were using cannabinoids at baseline. By multivariate analysis, cannabinoid use at baseline was significantly associated with improved iPFS (0.05), but not iBOR (p = 0.15), PFS (p = 0.12), OS (p = 0.35) or incidence of grade 1/2 or 3/4 irAEs (p = 0.96 and 0.65 respectively). Results were not significantly different with cannabinoid use at any time on trial.Conclusion Improved iPFS with cannabinoid use in patients treated with durvalumab plus tremelimumab ± chemotherapy did not translate into OS benefits. This study supports the safe use of cannabinoids in the context of combination ICI therapy.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".