Immune escape of multiple myeloma cells results from low miR29b and the ensuing epigenetic silencing of proteasome genes
Bibliographic record
Abstract
Abstract Background Activation of CD28 on multiple myeloma (MM) plasma cells, by binding to CD80 and CD86 on dendritic cells, decreases proteasome subunit expression in the tumor cells and thereby helps them evade being killed by CD8+ T cells. Understanding how CD28 activation leads to proteasome subunit downregulation is needed to design new MM therapies. Methods This study investigates the molecular pathway downstream of CD28 activation, using an in vitro model consisting of myeloma cell lines stimulated with anti-CD28-coated beads. Results We show that CD28 engagement on U266 and RPMI 8226 cells activates the PI3K/AKT pathway, reduces miR29b expression, increases the expression of DNA methyltransferase 3B (DNMT3B, a target of miR29b), and decreases immunoproteasome subunit expression. In vitro transfection of U266 and RPMI 8226 cells with a miR29b mimic downregulates the PI3K/AKT pathway and DNMT3B expression, restores proteasome subunit levels, and promotes myeloma cell killing by bone marrow CD8+ T cells from MM patients. Freshly purified bone marrow plasma cells (CD138+) from MM patients have lower miR29b and higher DNMT3B (mRNA and protein) than do cells from patients with monoclonal gammopathy of undetermined significance. Finally, in MM patients, high DNMT3B levels associate with shorter overall survival. Conclusions Altogether, this study describes a novel molecular pathway in MM. This pathway starts from CD28 expressed on tumor plasma cells and, through the PI3K-miR29b-DNMT3B axis, leads to epigenetic silencing of immunoproteasome subunits, allowing MM plasma cells to elude immunosurveillance. This discovery has implications for the design of innovative miR29b-based therapies for MM.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".