Immune-based classification of HPV-associated oropharyngeal cancer with implications for biomarker-driven treatment de-intensification
Bibliographic record
Abstract
Background There is significant interest in treatment de-escalation for human papillomavirus-associated (HPV + ) oropharyngeal squamous cell carcinoma (OPSCC) patients given the generally favourable prognosis. However, 15–30% of patients recur after primary treatment, reflecting a need for improved risk-stratification tools. We sought to develop a molecular test to risk stratify HPV + OPSCC patients. Methods We created an immune score (UWO3) associated with survival outcomes in six independent cohorts comprising 906 patients, including blinded retrospective and prospective external validations. Two aggressive radiation de-escalation cohorts were used to assess the ability of UWO3 to identify patients who recur. Multivariate Cox models were used to assess the associations between the UWO3 immune class and outcomes. Findings A three-gene immune score classified patients into three immune classes (immune rich, mixed, or immune desert) and was strongly associated with disease-free survival in six datasets, including large retrospective and prospective datasets. Pooled analysis demonstrated that the immune rich group had superior disease-free survival compared to the immune desert (HR = 9.0, 95% CI: 3.2–25.5, P = 3.6 × 10 −5 ) and mixed (HR = 6.4, 95% CI: 2.2–18.7, P = 0.006) groups after adjusting for age, sex, smoking status, and AJCC8 clinical stage. Finally, UWO3 was able to identify patients from two small treatment de-escalation cohorts who remain disease-free after aggressive de-escalation to 30 Gy radiation. Interpretation With additional prospective validation, the UWO3 score could enable biomarker-driven clinical decision-making for patients with HPV + OPSCC based on robust outcome prediction across six independent cohorts. Prospective de-escalation and intensification clinical trials are currently being planned. Funding CIHR, European Union, and the NIH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".