Proteome and phosphoproteome signatures of recurrence for HPV+ head and neck squamous cell carcinoma
Bibliographic record
Abstract
Abstract Background Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and the human papillomavirus (HPV+)-driven subtype is the fastest rising cancer in North America. Although most cases of HPV+ HNSCC respond favorably to the treatment via surgery followed by radiochemotherapy, up to 20% recur with a poor prognosis. The molecular and cellular mechanisms of recurrence are not fully understood. Methods To gain insights into the mechanisms of recurrence and to inform patient stratification and personalized treatment, we compared the proteome and phosphoproteome of recurrent and non-recurrent tumors by quantitative mass spectrometry. Results We observe significant differences between the recurrent and non-recurrent tumors in cellular composition, function, and signaling. The recurrent tumors are characterized by a pro-fibrotic and immunosuppressive tumor microenvironment (TME) featuring markedly more abundant cancer-associated fibroblasts, extracellular matrix (ECM), neutrophils, and suppressive myeloid cells. Defective T cell function and increased epithelial-mesenchymal transition potential are also associated with recurrence. These cellular changes in the TME are accompanied by reprogramming of the kinome and the signaling networks that regulate the ECM, cytoskeletal reorganization, cell adhesion, neutrophil function, and coagulation. Conclusions In addition to providing systems-level insights into the molecular basis of recurrence, our work identifies numerous mechanism-based, candidate biomarkers and therapeutic targets that may aid future endeavors to develop prognostic biomarkers and precision-targeted treatment for recurrent HPV+ HNSCC.
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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 | 0.001 | 0.001 |
| 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".