The viral approach to breast cancer immunotherapy
Bibliographic record
Abstract
Despite years of intensive research, breast cancer remains the leading cause of death in women worldwide. New technologies including oncolytic virus therapies, virus, and phage display are among the most powerful and advanced methods that have emerged in recent years with potential applications in cancer prevention and treatment. Oncolytic virus therapy is an interesting strategy for cancer treatment. Presently, a number of viruses from different virus families are under laboratory and clinical investigation as oncolytic therapeutics. Oncolytic viruses (OVs) have been shown to be able to induce and initiate a systemic antitumor immune response. The possibility of application of a multimodal therapy using a combination of the OV therapy with immune checkpoint inhibitors and cancer antigen vaccination holds a great promise in the future of cancer immunotherapy. Display of immunologic peptides on bacterial viruses (bacteriophages) is also increasingly being considered as a new and strong cancer vaccine delivery strategy. In phage display immunotherapy, a peptide or protein antigen is presented by genetic fusions to the phage coat proteins, and the phage construct formulation acts as a protective or preventive vaccine against cancer. In our laboratory, we have recently tested a few peptides (E75, AE37, and GP2) derived from HER2/neu proto-oncogene as vaccine delivery modalities for the treatment of TUBO breast cancer xenograft tumors of BALB/c mice. Here, in this paper, we discuss the latest advancements in the applications of OVs and bacterial viruses display systems as new and advanced modalities in cancer immune therapeutics.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".