Combination of Paclitaxel and MG1 oncolytic virus as a successful strategy for breast cancer treatment
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common malignant disease amongst Western women. The lack of treatment options for patients with chemotherapy-resistant or recurrent cancers is pushing the field toward the rapid development of novel therapies. The use of oncolytic viruses is a promising approach for the treatment of disseminated diseases like breast cancer, with the first candidate recently approved by the Food and Drug Administration for use in patients. In this report, we demonstrate the compatibility of oncolytic virotherapy and chemotherapy using various murine breast cancer models. This one-two punch has been explored in the past by several groups with different viruses and drugs and was shown to be a successful approach. Our strategy is to combine Paclitaxel, one of the most common drugs used to treat patients with breast cancer, and the oncolytic Rhabdovirus Maraba-MG1, a clinical trial candidate in a study currently recruiting patients with late-stage metastatic cancer. METHODS: We used the EMT6, 4 T1 and E0771 murine breast cancer models to evaluate in vitro and in vivo the effects of co-treatment with MG1 and Paclitaxel. Treatment-induced cytotoxicity was assessed and plaque assays, flow cytometry, microscopy and immunocytochemistry analysis were performed to quantify virus production and transgene expression. Orthotopically implanted tumors were measured during and after treatment to evaluate efficacy and Kaplan-Meier survival curves were generated. RESULTS: Our data demonstrate not only the compatibility of the treatments, but also their synergistic cytopathic activity. With Paclitaxel, EMT6 and 4 T1 tumors demonstrated increased virus production both in vitro and in vivo. Our results also show that Paclitaxel does not impair the safety profile of the virus treatment. Importantly, when combined, MG1 and the drug controlled tumor growth and prolonged survival. CONCLUSIONS: The combination of MG1 and Paclitaxel improved efficacy in all of the breast cancer models we tested and thus is a promising alternative approach for the treatment of patients with refractory breast cancer. Our strategy has potential for rapid translation to the clinic, given the current clinical status of both agents.
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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".