Oncolytic Reovirus Effectively Targets Breast Cancer Stem Cells
Bibliographic record
Abstract
Recent evidence suggests that cancer stem cells (CSCs) play an important role in cancer, as these cells possess enhanced tumor-forming capabilities and are resistant to current anticancer therapies. Hence, novel cancer therapies will need to be tested for both tumor regression and CSC targeting. Herein we show that oncolytic reovirus that induces regression of human breast cancer primary tumor samples xenografted in immunocompromised mice also effectively targets and kills CSCs in these tumors. CSCs were identified based on CD24(-)CD44(+) cell surface expression and overexpression of aldehyde dehydrogenase. Upon reovirus treatment, the CSC population was reduced at the same rate as non-CSCs within the tumor. Immunofluorescence of breast tumor tissue samples from the reovirus- and mock-treated mice confirmed that both CSCs and non-CSCs were infectible by reovirus, and terminal deoxynucleotidyl transferase biotin-dUTP nick end labeling (TUNEL) assay showed that both populations died by apoptosis. Ras, which has been shown to mediate reovirus oncolysis, was found to be present at similar levels in all cell types, and this is consistent with their comparable sensitivity to reovirus. These experiments indicate that oncolytic reovirus has the potential to induce tumor regression in breast cancer patients. More important, the CSC population was equally reduced and was as susceptible to reovirus treatment as the non-CSC population.
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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) | 0.001 | 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".