Preclinical validation: LV/IL-12 transduction of patient leukemia cells for immunotherapy of AML
Bibliographic record
Abstract
Interleukin-12 (IL-12) is a potent cytokine that may be harnessed to treat cancer. To date, nearly 100 IL-12-based clinical trials have been initiated worldwide. Yet systemic administration of IL-12 is toxic. Different strategies are being developed to reduce such toxicities by restricting IL-12 distribution. Our previous studies employed lentivector-mediated expression of murine IL-12 in tumor cells and demonstrated effective protection in both mouse leukemia and solid tumor challenge models. In this study, we carried out preclinical validation studies using a novel lentivector to engineer expression of human IL-12 in acute myeloid leukemia blast cells isolated from 21 patients. Acute myeloid leukemia cells were transduced with a bicistronic lentivector that encodes the human IL-12 cDNA as a fusion, as well as a LNGFR (ΔLNGFR)/mutant thymidylate kinase cassette as a marking and cell-fate control element. A range of 20–70% functional transduction efficiencies was achieved. Transduced acute myeloid leukemia cells produced bioactive IL-12 protein and displayed dose-dependent sensitivity to the prodrug 3′-azido-3′-deoxythymidine. In vitro immortalization assays using transduced mouse hematopoietic stem cells demonstrated minimal genotoxic risk from our IL-12 vector. Scale-up transduction and cell processing was subsequently validated in a GMP facility to support our (now approved) Clinical Trial Application (CTA).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".