Effects of cannabinoids on immune checkpoint inhibitor response: CCTG pooled analysis of individual patient data
Bibliographic record
Abstract
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. 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). 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. 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. Immune checkpoint inhibitors (ICI) have become an important treatment option for cancer patients and has been associated with improved survival outcomes across various tumor types. Cannabinoids are active components of cannabis and include tetrahydrocannabinol (THC) and cannabidiol (CBD). Cannabis use has increased in Canadian cancer patients and is often used for symptom management. Some studies have suggested that cannabinoids can alter the function of the immune system, which could impact the effectiveness of immune checkpoint inhibitors. Therefore, we aimed to evaluate whether cannabinoid use might impact the effectiveness of combination ICI treatment with durvalumab plus tremelimumab (with/without chemotherapy) using data from four clinical trials completed through the Canadian Cancer Trials Group (CCTG). We found no significant difference in response, survival outcomes or adverse events in patients who used cannabinoids with combination ICI treatment compared to cannabinoid non-users. This study supports the safe use of cannabinoids in the context of combination ICI therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.002 | 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".