Bibliographic record
Abstract
The adoptive transfer of cancer-specific T-cells has demonstrated success as a novel treatment strategy in some hematological malignancies but this approach has not yet achieved widespread curative potential in the majority of tumors. To circumvent many of the limitations currently facing adoptive cell therapies, our lab has recently developed a combination therapy involving the in vivo boosting of adoptively transferred tumor-specific memory T-cells with an oncolytic viral vaccine. While this represents a demonstrably powerful approach in preclinical models of cancer it is limited by its targeting of a single antigen. Therapeutic resistance is a common concern when targeting a single antigen or pathway and an ideal therapy would include built-in mechanisms to address the heterogeneity and mutability that is inherent to cancer. Thus the focus of this research involved the development of a strategy to target therapeutic resistance in the context of the adoptive cell transfer with oncolytic viral boost regimen. In order to address the single antigen limitations, the engineering of tumor-specific T-cells with a targeting capacity for a second antigen is described. In addition to their endogenous tumor target it is shown that these cells have specificity for and can kill cells expressing ligands for the natural killer group 2 member D receptor which are commonly upregulated on both cancer cells and components of the tumor microenvironment. Indeed it is demonstrated in an in vivo model of relapse that T-cells capable of targeting both antigens produce more consistent and prolonged remissions than those with only their endogenous targeting capacity. Furthermore pharmacological strategies for the enhancement of engineered T cell survival and efficacy are also described. Finally the early development of a chimeric tumor model to further characterize the potential of dual-specific T-cells to address tumor heterogeneity is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.699 | 0.397 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".