Impact of cannabinoids on cancer outcomes in patients receiving immune checkpoint inhibitor immunotherapy
Bibliographic record
Abstract
Cannabinoids relieve pain, nausea, anorexia and anxiety, and improve quality of life in several cancer patients. The immunotherapy with checkpoint inhibitors (ICIs), although very successful in a subset of patients, is accompanied by moderate to severe immune-related adverse events (ir-AE) that often necessitate its discontinuation. Because of their role in symptomatic relief, cannabinoids have been used in combination with immune checkpoint inhibitor (ICI) immunotherapy. A few studies strongly suggest that the use of medicinal cannabis in cancer patients attenuates many of the ir-AE associated with the use of ICI immunotherapy and increase its tolerability. However, no significant beneficial effects on overall survival, progression free survival or cancer relapses were observed; rather, some of the studies noted adverse effects of concurrent administration of cannabinoids with ICI immunotherapy on the clinical benefits of the latter. Because of cannabinoids’ well documented immunosuppressive effects mediated through the cannabinoid recptor-2 (CB2), we propose considering this receptor as an inhibitory immune checkpoint per se . A simultaneous neutralization of CB2, concurrent with cannabinoid treatment, may lead to better clinical outcomes in cancer patients receiving ICI immunotherapy. In this regard, cannabinoids such as cannabidiol (CBD) and cannabigerol (CBG), with little agonism for CB2, may be better therapeutic choices. Additional strategies e.g., the use of monoacylglycerol lipase (MAGL) inhibitors that degrade some endocannabinoids as well as lipogenesis and formation of lipid bilayers in cancer cells may also be explored. Future studies should take into consideration gut microbiota, CYP450 polymorphism and haplotypes, cannabinoid-drug interactions as well as genetic and somatic variations occurring in the cannabinoid receptors and their signaling pathways in cancer cells for personalized cannabis-based therapies in cancer patients receiving ICIs. This may lead to rational knowledge-based regimens tailored to individual cancer patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".