Strategic Combinations: The Future of Oncolytic Virotherapy with Reovirus
Bibliographic record
Abstract
The dominant cancer treatment modalities such as chemotherapy, radiotherapy, and even targeted kinase inhibitors and mAbs are limited by low efficacy, toxicity, and treatment-resistant tumor subclones. Oncolytic viral therapy offers a novel therapeutic strategy that has the potential to dramatically improve clinical outcomes. Reovirus, a double-stranded benign human RNA virus, is a leading candidate for therapeutic development and currently in phase III trials. Reovirus selectively targets transformed cells with activated Ras signaling pathways; Ras genes are some of the most frequently mutated oncogenes in human cancer and it is estimated that at least 30% of all human tumors exhibit aberrant Ras signaling. By targeting Ras-activated cells, reovirus can directly lyse cancer cells, disrupt tumor immunosuppressive mechanisms, reestablish multicellular immune surveillance, and generate robust antitumor responses. Reovirus therapy is currently being tested in combination with radiotherapy, chemotherapy, immunotherapy, and surgery. In this review, we discuss the current successes of these combinatorial therapeutic strategies and emphasize the importance of prioritizing combination oncolytic viral therapy as reovirus-based treatments progress in clinical development. Mol Cancer Ther; 15(5); 767-73. ©2016 AACR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".