Humanized mouse models of <i>KRAS</i> -mutated colorectal and pancreatic cancers with HLA-class-I match for pre-clinical evaluation of cancer immunotherapies
Bibliographic record
Abstract
Cancer immunotherapy promises to treat challenging cancers including KRAS-mutated colorectal cancer (CRC) and pancreatic ductal adenocarcinoma (PDAC). However, pre-clinical animal models that better mimic patient tumor and immune system interactions are required. While humanized mice are promising vehicles for pre-clinical immunotherapy testing, currently used cancer models retain limitations, such as lack of a human thymus for human leukocyte antigen (HLA)-based education of human T cells. As cytotoxic T lymphocyte (CTL) activity underlies many immunotherapies, we developed more clinically relevant KRAS-mutated CRC and PDAC humanized cancer models using transgenic NRG-A2 mice expressing HLA-A2.1 to enable HLA-class-I match between mouse tissues (including the thymus), the humanized immune system and human tumors. Using these novel humanized cancer models and a CTL-mediated combination (immuno)therapy with clinical potential, we were able to recapitulate the complexity and therapy-induced changes reported in patient biopsies, demonstrating the use of these HLA-matched models for pre-clinical validation of novel immunotherapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".