A concerted cell and serum proteomic approach for the identification of oral squamous cell carcinoma biomarkers
Bibliographic record
Abstract
5512 Background: Head and neck squamous cell carcinoma is the sixth most common cause of cancer deaths in the world. Despite extensive research, the 5-year survival rates have not changed significantly over the last decade. Presently, the lack of serum biomarkers for head and neck carcinoma limits early diagnosis and treatment monitoring of advanced disease. In this study, we used a proteomic approach on serum from mice bearing human oral squamous cell carcinoma xenografts and from conditioned cell culture medium from matched cell lines. Methods: Oral squamous cell carcinomas, with distinct invasive phenotypes, and adjacent normal tissue were obtained from human patients, and were transplanted orthotopicaly into tongues of RAG-2/γ(c) mice. After a 20% weight loss, the mice were sacrificed by exsanguinations. Two distinct proteomic protocols were used to analyze the mouse serum. The first involved albumin depletion followed by two-dimensional gel electrophoresis and MALDI. The second arm utilized the same albumin depletion followed by multidimensional-protein-information-technology (ESI-LC and MS/MS). The top candidate proteins, which were differentially expressed in the cancer bearing mice compared to matched controls were then validated by Western blot, immunoprecipitation, immunofluorescence, and/or immunohistochemistry analyses using mouse serum and conditioned medium from matched cell lines. Results: Orthotopic implantation of human tumor tissues in mouse tongue was successful in 100% of the mice tested. The human origin of these tumors was confirmed pathologically. A correlation between disease stages and invasiveness was observed. We identified over one hundred proteins as being differentially expressed between control and cancer-bearing mice (p < 0.05), including proteins involved in cell cytoskeleton signaling. The expression of these proteins was then validated in mouse serum, tissue xenografts, and conditioned medium from oral cancer matched established cell lines. Conclusion: We report the first proteomic in-vivo model of oral cancer for the identification of low molecular weight serum biomarkers. We identified candidates that can be exploited as potential markers for diagnosis of human oral squamous cell carcinoma. No significant financial relationships to disclose.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".