Abstract PO-086: Generation of HPV-negative tobacco-associated oral cancer models using <i>p53</i> and <i>Cdkn2a</i> mutant mice
Bibliographic record
Abstract
Abstract Background. Genetically engineered mouse models (GEMMs) in combination with chemical carcinogenesis are powerful tools to assess the functional role of driver mutations commonly seen in human oral cancer. The tumor suppressors TP53 and CDKN2A, which regulate cell-cycle checkpoints relevant to DNA repair are the two most frequent mutations found in HPV-negative head and neck squamous cell carcinoma (HNSCC). Recent studies have demonstrated that the combination of TP53 and CDKN2A mutations is associated with aggressive tumors in animal models, and patients with tumors bearing both mutations have poor treatment outcomes. In this study, we used wild-type, mutant p53, mutant Cdkna2a, and double mutant p53/Cdkn2 mice to understand their role in oral carcinogenesis through modulation of the epithelial tumor compartment as well as its interaction with the tumor immune microenvironment. Methods. First, the mouse strains: K14Cre, p53 loss-of function (LOF), p53R172H, p53R245W and Cdkn2a-LOF were backcrossed into C57BL/6 genetic background using marker assisted speed congenics. Chemical oral carcinogenesis was induced using a low dose of 4-Nitro quinoline 1-oxide in the drinking water for 16 weeks. The tongues were harvested and fixed in 10% neutral-buffered formalin and process for histopathological studies. A total of 15 to 20 slides per genotype were analyzed by a pathologist in blinded approach quantify pre-malignant lesions and cancers in each genotype group. Representative unstained tumor sections were selected for HTG EDGEseq targeted RNA sequencing using the tumor response panel and the assay was run by HTG molecular diagnostics lab. Preliminary Imaging mass cytometry (IMC) was used for validation of immune markers in the tumor microenvironment. Results. Mutant p53 and Cdkn2a-LOF accelerate oral tumor development in a 4NQO mouse oral carcinogenesis model. Mutant p53R172H and Cdkn2a showed a more aggressive phenotype of tumor progression compared to other p53 mutants and wild-type mouse genotypes. Interestingly, the tumor microenvironment of Cdkn2a-LOF showed higher expression of immune checkpoint inhibitors compared to mutant p53R172H mice. Furthermore, the interplay of mutant p53 and Cdkn2a in double mutant mice resulted in more aggressive tumors compared to single mutations, which correlates closely with what is seen in human HNSCC patients with tumors bearing TP53 and CDKN2A. Finally, we have established several tumorigenic cell lines containing single and double p53/Cdkn2a mutations. Conclusions. Combining single- and double-mutant p53 and Cdkn2a mice with chemical carcinogenesis provides complementary model systems to study oral cancer immune evasion, develop strategies for prevention, and optimize therapies targeting the immune system and tumor microenvironment. These models complement human clinical trials by providing models for testing trial-generated hypotheses. Citation Format: Allison Nipper, Yewen Shi, Alanis E. Rodriguez Rosario, Nicholas Balbin, Roberto Rangel Jr., Liping Dong, Arnoldo Corona, Ratna Veeramachaneni, Guillermina Lozano, Andrew G. Sikora, Jeffrey N. Myers, Roberto Rangel. Generation of HPV-negative tobacco-associated oral cancer models using p53 and Cdkn2a mutant mice [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-086.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".