Potent induction of B- and T-cell immunity against human carcinoembryonic antigen-expressing tumors in human carcinoembryonic antigen transgenic mice mediated by direct lentivector injection
Bibliographic record
Abstract
The applicability of immunotherapy would be dramatically broadened to a greater number of recipients if direct "off-the-shelf" products could be engineered to engender functionally potent immune responses against true "self"-tumor antigens. This would obviate the need for ex vivo culture of dendritic cells or T cells on a patient-by-patient basis, for example. The carcinoembryonic antigen (CEA) is a glycoprotein expressed in normal gut epithelium that is up-regulated in the majority of colon cancers, non-small cell lung cancers, and half of all breast cancers. Such properties make CEA an excellent and important target for cancer immunotherapy. In this study, we show stabilization of 14-day established s.c. mGC4CEA tumors in human CEA (huCEA) transgenic mice following two direct low-dose injections of 0.15x10(6) transducing units of a lentiviral vector (LV) that directs expression of huCEA (LV-huCEA). This stabilization result was reproducible and detailed analyses including antibody assays, multiplex cytokine analyses on unstimulated splenocytes, lymph node cell characterizations, tetramer staining, and immunofluorescence staining of tumor sections showed that this outcome correlated with both a cellular and humoral immune response. Similar tumor outcomes were not seen when mice were vaccinated with a control LV that engineered expression of enGFP only. The long-term potency of this vaccination strategy was also studied and revealed the requirement for maintenance of tumor antigen-specific immunity for efficient tumor control. These data support the use of direct injections of low doses of LV-huCEA for enhancement of tumor immunotherapy directed against CEA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".