Engineering immunoproteasome-expressing mesenchymal stromal cells: A potent cellular vaccine for lymphoma and melanoma in mice
Bibliographic record
Abstract
Dendritic cells (DCs) excel at cross-presenting antigens, but their effectiveness as cancer vaccine is limited. Here, we describe a vaccination approach using mesenchymal stromal cells (MSCs) engineered to express the immunoproteasome complex (MSC-IPr). Such modification instills efficient antigen cross-presentation abilities associated with enhanced major histocompatibility complex class I and CD80 expression, de novo production of interleukin-12, and higher chemokine secretion. This cross-presentation capacity of MSC-IPr is highly dependent on their metabolic activity. Compared with DCs, MSC-IPr hold the ability to cross-present a vastly different epitope repertoire, which translates into potent re-activation of T cell immunity against EL4 and A20 lymphomas and B16 melanoma tumors. Moreover, therapeutic vaccination of mice with pre-established tumors efficiently controls cancer growth, an effect further enhanced when combined with antibodies targeting PD-1, CTLA4, LAG3, or 4-1BB under both autologous and allogeneic settings. Therefore, MSC-IPr constitute a promising subset of non-hematopoietic antigen-presenting cells suitable for designing universal cell-based cancer vaccines.
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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) | 0.001 | 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".