Reovirus oncolysis as a novel purging strategy for autologous stem cell transplantation
Bibliographic record
Abstract
Hematologic stem cell rescue after high-dose cytotoxic therapy is extensively used for the treatment of many hematopoietic and solid cancers. Gene marking studies suggest that occult tumor cells within the autograft may contribute to clinical relapse. To date purging of autografts contaminated with cancer cells has been unsuccessful. The selective oncolytic property of reovirus against myriad malignant histologies in in vitro, in vivo, and ex vivo systems has been previously demonstrated. In the present study we have shown that reovirus can successfully purge cancer cells within autografts. Human monocytic and myeloma cell lines as well as enriched ex vivo lymphoma, myeloma, and Waldenström macroglobulinemia patient tumor specimens were used in an experimental purging model. Viability of the cell lines or purified ex vivo tumor cells of diffuse large B-cell lymphoma, chronic lymphocytic leukemia, Waldenström macroglobulinemia, and small lymphocytic lymphoma was significantly reduced after reovirus treatment. Further, [35S]-methionine labeling and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of cellular proteins demonstrated reovirus protein synthesis and disruption of host cell protein synthesis as early as 24 hours. Admixtures of apheresis product with the abovementioned tumor cells and cell lines treated with reovirus showed complete purging of disease. In contrast, reovirus purging of enriched ex vivo multiple myeloma, Burkitt lymphoma, and follicular lymphoma was incomplete. The oncolytic action of reovirus did not affect CD34+ stem cells or their long-term colony-forming assays even after granulocyte colony-stimulating factor (G-CSF) stimulation. Our results indicate the ex vivo use of an unattenuated oncolytic virus as an attractive purging strategy for autologous stem cell transplantations.
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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) | 0.000 | 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 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".