Integrated Omic Analysis of Oropharyngeal Carcinomas Reveals Human Papillomavirus (HPV)–dependent Regulation of the Activator Protein 1 (AP-1) Pathway
Bibliographic record
Abstract
HPV-positive oropharyngeal carcinoma (OPC) patients have superior outcomes relative to HPV-negative patients, but the underlying mechanisms remain poorly understood. We conducted a proteomic investigation of HPV-positive (n = 27) and HPV-negative (n = 26) formalin-fixed paraffin-embedded OPC biopsies to acquire insights into the biological pathways that correlate with clinical behavior. Among the 2,633 proteins identified, 174 were differentially abundant. These were enriched for proteins related to cell cycle, DNA replication, apoptosis, and immune response. The differential abundances of cortactin and methylthioadenosine phosphorylase were validated by immunohistochemistry in an independent cohort of 29 OPC samples (p = 0.023 and p = 0.009, respectively). An additional 1,124 proteins were independently corroborated through comparison to a published proteomic dataset of OPC. Furthermore, utilizing the Cancer Genome Atlas, we conducted an integrated investigation of OPC, attributing mechanisms underlying differential protein abundances to alterations in mutation, copy number, methylation, and mRNA profiles. A key finding of this integration was the identification of elevated cortactin oncoprotein levels in HPV-negative OPCs. These proteins might contribute to reduced survival in these patients via their established role in radiation resistance. Through interrogation of Cancer Genome Atlas data, we demonstrated that activation of the β1-integrin/FAK/cortactin/JNK1 signaling axis and associated differential regulation of activator protein 1 transcription factor target genes are plausible consequences of elevated cortactin protein levels.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".