Analysis of head and neck carcinoma progression reveals novel and relevant stage-specific changes associated with immortalisation and malignancy
Bibliographic record
Abstract
We report changes in the genomic landscape in the development of head and neck squamous cell carcinomas HNSCC from potentially premalignant lesions (PPOLS) to malignancy and lymph node metastases. Likely pathological mutations predominantly involved a relatively small set of genes reported previously (TP53, KMT2D, CDKN2A, PIK3CA, NOTCH1 and FAT1) but also other predicted cancer drivers (MGA, PABPC3, NR4A2, NCOR1 and MACF1). Notably, all these mutations arise early and are present in PPOLs. The most frequent genetic changes, which follow acquisition of immortality and loss of senescence, are of consistent somatic copy number alterations (SCNAs) involving chromosomal regions enriched for genes in known and previously unreported cancer-related pathways. We mapped the evolution of SCNAs in HNSCC progression. One of the earliest SCNAs involved deletions of CSMD1 (8p23.2). CSMD1 deletions or promoter hypermethylation were present in all of the immortal PPOLs and occurred at high frequency in the immortal HNSCC cell lines. Modulation of CSMD1 in cell lines revealed significant suppression of proliferation and invasion by forced expression, and significant stimulation of invasion by knockdown of expression. Known cancer drivers NOTCH1, PPP6C, RAC1, EIF4G1, PIK3CA showed significant increase in frequency of SCNA in transition from PPOLs to HNSCC that correlated with their expression. In the later stages of progression, HNSCC with and without nodal metastases showed some clear differences including high copy number gains of CCND1, hsa-miR-548k and TP63 in the metastases group.
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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 | 0.000 | 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) | 0.001 | 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".