Novel EXO-T vaccine using polyclonal CD4+ T cells armed with HER2-specific exosomes for HER2-positive breast cancer
Bibliographic record
Abstract
Rong Li,1,2 Rajni Chibbar,3 Jim Xiang1,2 1Cancer Research Cluster, Saskatchewan Cancer Agency, Saskatoon, SK, Canada; 2Department of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada; 3Department of Pathology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada Abstract: Breast cancer is the leading cause of death in women globally. The human epidermal growth factor receptor 2 (HER2)-positive breast cancer is often associated with poor prognosis and high mortality. Even though anti-HER2 monoclonal antibodies have improved the clinical outcome, resistance to the antibody therapy becomes a major obstacle in the treatment of HER2-positive breast cancer patients. Alternative approaches are therefore needed. HER2-specific vaccines have been developed to trigger patient’s immune system against HER2-positive breast cancer. This article describes the development of novel HER2-specific exosome (EXO)-T vaccine using polyclonal CD4+ T cells armed with HER2-specific dendritic cell-released EXO and demonstrates its therapeutic effect against HER2-positive tumor in double-transgenic HER2/HLA-A2 mice with HER2-specific self-immune tolerance. Therefore, our novel HER2-specific EXO-T vaccines are likely to assist in the treatment of HER2-positive breast cancer patients. Keywords: EXO-T vaccine, polyclonal CD4+, T cell, HER2, exosome, breast cancer
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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".