Treating brain tumor-initiating cells using a combination of myxoma virus and rapamycin
Bibliographic record
Abstract
BACKGROUND: Intratumoral heterogeneity in glioblastoma multiforme (GBM) poses a significant barrier to therapy in certain subpopulation such as the tumor-initiating cell population, being shown to be refractory to conventional therapies. Oncolytic virotherapy has the potential to target multiple compartments within the tumor and thus circumvent some of the barriers facing conventional therapies. In this study, we investigate the oncolytic potential of myxoma virus (MYXV) alone and in combination with rapamycin in vitro and in vivo using human brain tumor-initiating cells (BTICs). METHODS: We cultured fresh GBM specimens as neurospheres and assayed their growth characteristics in vivo. We then tested the susceptibility of BTICs to MYXV infection with or without rapamycin in vitro and assessed viral biodistribution/survival in vivo in orthotopic xenografts. RESULTS: The cultured neurospheres were found to retain stem cell markers in vivo, and they closely resembled human infiltrative GBM. In this study we determined that (i) all patient-derived BTICs tested, including those resistant to temozolomide, were susceptible to MYXV replication and killing in vitro; (ii) MYXV replicated within BTICs in vivo, and intratumoral administration of MYXV significantly prolonged survival of BTIC-bearing mice; (iii) combination therapy with MYXV and rapamycin improved antitumor activity, even in mice bearing "advanced" BTIC tumors; (iv) MYXV treatment decreased expression of stem cell markers in vitro and in vivo. CONCLUSIONS: Our study suggests that MYXV in combination with rapamycin infects and kills both the BTICs and the differentiated compartments of GBM and may be an effective treatment even in TMZ-resistant patients.
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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".