Clinically relevant oral cancer model for serum proteomic eavesdropping on the tumour microenvironment.
Bibliographic record
Abstract
BACKGROUND: Serum proteomics has enormous potential in the identification of biomarkers and the development of new therapies for oral cancer. Current efforts are limited by the lack of a control subject. The human-mouse chimeric model offers a solution. OBJECTIVES: To develop and test two orthotopic xenograft mouse models of human oral squamous cell carcinoma for research in serum proteomics. METHODS: Advanced human oral cancer from three patients was implanted orthotopically into the tongues of 19 SCID and 4 RAG2/gamma(c) knockout (KO) mice. Adjacent normal tissue from each patient was also implanted into nine SCID and 4 RAG2/gamma(c) KO mice. The models were compared for tissue take, the presence of metastasis, and histologic invasiveness. Mouse serum was preserved for studies in serum proteomics. RESULTS: Tumour tissue was successfully implanted into SCID and RAG2/gamma(c) mice, and the invasiveness was confirmed pathologically. Three of the control mice demonstrated the persistence of normal tissue more than 1 month after implantation. This is the first time that this has been reported. The larger size of the RAG2/gamma(c) KO mouse facilitated serum collection for serum proteomics. CONCLUSIONS: Both RAG2/gamma(c) KO and SCID mouse are able to reliably engraft human oral cancer. Engraftment of normal oral tissue was less reliable. This is the first in vivo model allowing identification of proteins released from the tumour microenvironment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".