Generation of Genetically Engineered Mouse Lung Organoid Models for Squamous Cell Lung Cancers Allows for the Study of Combinatorial Immunotherapy
Bibliographic record
Abstract
PURPOSE: Lung squamous cell carcinoma (LSCC) is a deadly disease for which only a subset of patients responds to immune checkpoint blockade (ICB) therapy. Therefore, preclinical mouse models that recapitulate the complex genetic profile found in patients are urgently needed. EXPERIMENTAL DESIGN: We used CRISPR genome editing to delete multiple tumor suppressors in lung organoids derived from Cre-dependent SOX2 knock-in mice. We investigated both the therapeutic efficacy and immunologic effects accompanying combination PD-1 blockade and WEE1 inhibition in both mouse models and LSCC patient-derived cell lines. RESULTS: We show that multiplex gene editing of mouse lung organoids using the CRISPR-Cas9 system allows for efficient and rapid means to generate LSCCs that closely mimic the human disease at the genomic and phenotypic level. Using this genetically defined mouse model and three-dimensional tumoroid culture system, we show that WEE1 inhibition induces DNA damage that primes the endogenous type I IFN and antigen presentation system in primary LSCC tumor cells. These events promote cytotoxic T-cell-mediated clearance of tumor cells and reduce the accumulation of tumor-infiltrating neutrophils. Beneficial immunologic features of WEE1 inhibition are further enhanced by the addition of anti-PD-1 therapy. CONCLUSIONS: We developed a mouse model system to investigate a novel combinatory approach that illuminates a clinical path hypothesis for combining ICB with DNA damage-inducing therapies in the treatment of LSCC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".