Co-Targeting Luminal B Breast Cancer with S-Adenosylmethionine and Immune Checkpoint Inhibitor Reduces Primary Tumor Growth and Progression, and Metastasis to Lungs and Bone
Bibliographic record
Abstract
Breast cancer (BCa) is the most prevalent cancer in females and has a high rate of mortality, especially due to increased metastasis to skeletal and non-skeletal sites. Despite the marked clinical accomplishment of immune checkpoint inhibitor (CPI) therapy in patients with several cancers, it has had limited success in luminal subtypes of BCa. Accordingly, recent efforts have focused on combination therapy with CPI, including epigenetic modulators, to increase response rates of CPI in luminal BCa. We have previously shown that S-adenosylmethionine (SAM), the ubiquitous methyl donor, has strong anti-cancer effects in various cancers, including all subtypes of BCa. In the current study, we took a novel approach and examined the effect of CPI alone and in combination with SAM on tumor growth and metastasis in a syngeneic mouse model of luminal B BCa. We showed that SAM decreases cell proliferation, colony-formation (survival), and invasion of luminal B BCa cell lines (Eo771, R221A) in vitro. In in vivo studies, in Eo771 tumor-bearing mice, either SAM or anti-PD-1 antibody treatment alone significantly reduced tumor growth and progression, while the SAM+anti-PD-1 combination treatment had the highest anti-cancer efficacy of all groups. The SAM+anti-PD-1 combination reduced the percentage of animals with lung metastasis, as well as total metastatic lesion area, compared to control. Additionally, the SAM+anti-PD-1 combination significantly reduced the skeletal lesion area and protected tibial integrity to a greater extent than the monotherapies in an Eo771 bone metastasis model. Transcriptome analysis of Eo771 primary tumors revealed significant downregulation of pro-metastatic genes, including Matrix metalloproteinases (MMPs) and related pathways. On the other hand, CD8+ T cell infiltration, CD8+ T cell cytotoxicity (elevated granzymes), and immunostimulatory genes and pathways were significantly upregulated by the combination treatment. The results presented point to a combination of SAM with CPI as a possible treatment for luminal B BCa that should be tested in clinical studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".