Unlocking the potential of dimethyl fumarate: enhancing oncolytic HSV-1 efficacy for wider cancer applications
Bibliographic record
Abstract
Immunotherapy and specifically oncolytic virotherapy has emerged as a promising option for cancer patients, with oncolytic herpes simplex virus-1 (oHSV-1) expressing granulocyte macrophage colony stimulating factor being the first OV to be approved by the FDA for treatment of melanoma. However, not all cancers are sensitive and responsive to oncolytic viruses (OVs). Our group has demonstrated that fumaric and maleic acid esters (FMAEs) are very effective in sensitizing cancer cells to OV infection. Of note, these FMAEs include dimethyl fumarate (DMF, also known as Tecfidera®), an approved treatment for multiple sclerosis and psoriasis. This study aimed to assess the efficacy of DMF in combination with oncolytic HSV-1 in preclinical cancer models. We demonstrate herewith that pre-treatment with DMF or other FMAEs leads to a significant increase in viral growth of oHSV-1 in several cancer cell lines, including melanoma, while decreasing cell viability. Additionally, DMF was able to enhance ex vivo oHSV-1 infection of mouse-derived tumor cores as well as human patient tumor samples but not normal tissue. We further reveal that the increased viral spread and oncolysis of the combination therapy occurs via inhibition of type I IFN production and response. Finally, we demonstrate that DMF in combination with oHSV-1 can improve therapeutic outcomes in aggressive syngeneic murine cancer models. In sum, this study demonstrates the synergistic potential of two approved therapies for clinical evaluation in cancer 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".