Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes
Bibliographic record
Abstract
There is a pressing need to improve risk stratification and treatment selection for HPV-negative head and neck squamous cell carcinoma (HNSCC) due to the adverse side effects of treatment. One of the most important prognostic features is lymph nodes involvement. Previously, we demonstrated that tumor formation in patient-derived xenografts (i.e. engraftment) was associated with poor clinical outcomes in patients with HPV-negative HNSCC. However, assessing engraftment is challenging in clinical settings. Here, we perform transcriptomic and proteomic profiling of 88 HNSCC patients and find the relationship between engraftment and clinical outcomes is recapitulated by molecular phenotype. We identify LAMC2 and TGM3 as candidate prognostic biomarkers and validated their utility in an independent cohort containing 404 HPV-negative HNSCC patients. Strikingly, these markers significantly improve prediction of outcomes beyond nodal status alone and can significantly stratify patients without any nodal involvement. Overall, our study demonstrates how the molecular characteristics of engraftment can inform patient prognostication. ‘Engraftment of patient-derived xenografts in samples from head and neck squamous cell carcinoma is variable. Here, the authors use transcriptomic and proteomic profiling to correlate engraftment of PDX with molecular phenotype, and validate LAMC2 and TGM3 as potential biomarkers of stratification.
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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 | 0.001 |
| 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".