Utilizing dual-specific CD4+ T cells in combination with oncolytic virotherapy for treatment of cancer (P2118)
Bibliographic record
Abstract
Abstract Engineering T cell specificity to direct them against tumor cells utilizing chimeric antigen receptors (CARs) has emerged as an exciting technique for adoptive T cell therapy. The effectiveness of adoptive transfer therapies can be limited by a number of factors, including ability of T cells to engraft, the degree to which they infiltrate the tumor, and the immunosuppressive nature of the tumor. Oncolytic viruses (OVs) are capable of overcoming these limitations, and can serve as effective booster vaccines. In an effort to capitalize on these features, we have generated dual-specific T cells that bear TCRs specific for the OV, and CARs specific for the tumor. Using murine tumor models, we have observed significant boosting capacity of adoptively transferred dual-specific CD4+ or CD8+ T cells to tumor-bearing hosts in response to infusion with OV. Boosting with OV also dramatically influenced the distribution of the CAR-T cells, driving the cells to proliferate throughout the body. We now seek to examine the utility of dual-specific CD4+ T cells in adoptive transfer therapies. We have found that CAR-engineered CD4+ T cells are capable of mediating anti-tumor immunity, and are investigating the combination of these cells with OV to further enhance their anti-tumor efficacy. Given the promising clinical outcomes of both CAR T cells and OVs, we believe our combinatorial approach will provide a clinically feasable strategy to maximize the therapeutic potential of both therapies.
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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 | 0.001 |
| 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".