Frequent Mutations in TP53 and CDKN2A Found by Next-Generation Sequencing of Head and Neck Cancer Cell Lines
Bibliographic record
Abstract
OBJECTIVE: To conduct high-throughput mutational analysis in 6 commonly used head and neck cancer cell lines. Comprehensive mutation analysis of primary head and neck squamous cell carcinoma (HNSCC) tumors has recently been reported, and mutations in the NOTCH receptors, TP53 and CDKN2A, were key findings. Established cell lines are valuable tools to study cancer in vitro. Similar high-throughput mutational analysis of head and neck cancer cell lines is necessary to confirm their mutational profile. DESIGN: DNA was extracted from American Type Culture Collection (ATCC) cell lines Cal27, Detroit562, FaDu, SCC4, SCC15, and SCC25. Cell line identity was confirmed by short tandem repeat (STR) analysis, and human papillomavirus (HPV) infection status was assessed by real-time polymerase chain reaction. A total of 535 cancer-associated genes were sequenced through a limited exome capture on the Illumina HiSeq system. SETTING: London Regional Cancer Program. RESULTS: The identity of the 6 cell lines was confirmed by STR analysis, and all lines tested negative for HPV infection. We achieved an average of 129-fold coverage with paired-end 100 base-pair reads. Sequencing revealed an average of 38 damaging mutations in each cell line (range, 30-45). The TP53 mutations, predicted to confer loss of function, were noted in all cell lines, and damaging CDKN2A mutations were found in all lines except SCC15. CONCLUSIONS: High-throughput sequencing of head and neck cancer cell lines revealed similar mutations to those observed in primary tumors. Thus, these lines reflect the tumor biology of HNSCC and can serve as valuable models to study HNSCC in vitro.
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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) | 0.001 | 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".