Specific cannabinoids revive adaptive immunity by reversing immune evasion mechanisms in metastatic tumours
Bibliographic record
Abstract
Emerging cancers are sculpted by neo-Darwinian selection for superior growth and survival but minimal immunogenicity; consequently, metastatic cancers often evolve common genetic and epigenetic signatures to elude immune surveillance. Immune subversion by metastatic tumours can be achieved through several mechanisms; one of the most frequently observed involves the loss of expression or mutation of genes composing the MHC-I antigen presentation machinery (APM) that yields tumours invisible to Cytotoxic T lymphocytes, the key component of the adaptive cellular immune response. Fascinating ethnographic and experimental findings indicate that cannabinoids inhibit the growth and progression of several categories of cancer; however, the mechanisms underlying these observations remain clouded in uncertainty. Here, we screened a library of cannabinoid compounds and found molecular selectivity amongst specific cannabinoids, where related molecules such as Δ9-tetrahydrocannabinol, cannabidiol, and cannabigerol can reverse the metastatic immune escape phenotype in vitro by inducing MHC-I cell surface expression in a wide variety of metastatic tumours that subsequently sensitizing tumours to T lymphocyte recognition. Remarkably, H3K27Ac ChIPseq analysis established that cannabigerol and gamma interferon induce overlapping epigenetic signatures and key gene pathways in metastatic tumours related to cellular senescence, as well as APM genes involved in revealing metastatic tumours to the adaptive immune response. Overall, the data suggest that specific cannabinoids may have utility in cancer immunotherapy regimens by overcoming immune escape and augmenting cancer immune surveillance in metastatic disease. Finally, the fundamental discovery of the ability of cannabinoids to alter epigenetic programs may help elucidate many of the pleiotropic medicinal effects of cannabinoids on human physiology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".