Reovirus-induced cell-mediated immunity for the treatment of multiple myeloma within the resistant bone marrow niche
Bibliographic record
Abstract
Background Multiple myeloma (MM) remains an incurable disease and oncolytic viruses offer a well-tolerated addition to the therapeutic arsenal. Oncolytic reovirus has progressed to phase I clinical trials and its direct lytic potential has been extensively studied. However, to date, the role for reovirus-induced immunotherapy against MM, and the impact of the bone marrow (BM) niche, have not been reported. Methods This study used human peripheral blood mononuclear cells from healthy donors and in vitro co-culture of MM cells and BM stromal cells to recapitulate the resistant BM niche. Additionally, the 5TGM1-Kalw/RijHSD immunocompetent in vivo model was used to examine reovirus efficacy and characterize reovirus-induced immune responses in the BM and spleen following intravenous administration. Collectively, these in vitro and in vivo models were used to characterize the development of innate and adaptive antimyeloma immunity following reovirus treatment. Results Using the 5TGM1-Kalw/RijHSD immunocompetent in vivo model we have demonstrated that reovirus reduces both MM tumor burden and myeloma-induced bone disease. Furthermore, detailed immune characterization revealed that reovirus: (i) increased natural killer (NK) cell and CD8 + T cell numbers; (ii) activated NK cells and CD8 + T cells and (iii) upregulated effector-memory CD8 + T cells. Moreover, increased effector-memory CD8 + T cells correlated with decreased tumor burden. Next, we explored the potential for reovirus-induced immunotherapy using human co-culture models to mimic the myeloma-supportive BM niche. MM cells co-cultured with BM stromal cells displayed resistance to reovirus-induced oncolysis and bystander cytokine-killing but remained susceptible to killing by reovirus-activated NK cells and MM-specific cytotoxic T lymphocytes. Conclusion These data highlight the importance of reovirus-induced immunotherapy for targeting MM cells within the BM niche and suggest that combination with agents which boost antitumor immune responses should be a priority.
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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 | 0.001 | 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".