Enhanced Anticancer Effect of a Combination of S-adenosylmethionine (SAM) and Immune Checkpoint Inhibitor (ICPi) in a Syngeneic Mouse Model of Advanced Melanoma
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICPi) targeting PD-1/PD-L1 pathway have shown marked success in patients with advanced melanoma. However, 60–70% of patients fail to respond warranting a therapeutic intervention that could increase response rates. We and others have shown that S-adenosylmethionine (SAM), a universal methyl donor, has significant anti-cancer effects in numerous cancers previously, however, its effect on melanoma progression has not been evaluated. Interestingly, SAM was reported to be essential for T cells activation and proliferation and thus could potentially cooperate with ICPi and block melanoma progression. In this study, we examined anti-tumor effects of SAM and ICPi alone and in combination in a well-established melanoma mouse model wherein syngeneic C57BL/6 mouse were sub-cutaneously (orthotopic) injected with B16-F1 cells. Treatment of mice with either SAM or anti-PD-1 antibody alone resulted in significant reduction in tumor volumes and weights; effects which were highest in mice treated with a combination of SAM+anti-PD-1. RNA-sequencing analysis of the primary tumors showed numerous differentially expressed genes (DEGs) following treatment with SAM+anti-PD-1 which was shown to down-regulate cancer, MAPK and tyrosine kinase pathways. Indeed, SAM+anti-PD-1 reversed the aberrant expression of some known melanoma genes. Tumor immunophenotyping revealed SAM+anti-PD-1 combination was significantly more effective than either SAM or anti-PD-1 as the CD8+ T cells were having higher activation, proliferation and cytokines production as compared to all other groups. This study shows that combination of, currently approved agents, SAM and ICPi can effectively block melanoma via alteration of key genes/pathways implicated in cancer and immune response pathways, providing the rationale for the initiation of clinical trials with SAM and ICPi.
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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 | 0.000 | 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 | 0.000 |
| 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".