Clinical validation of a tissue-agnostic genome-wide methylome enrichment molecular residual disease assay for head and neck malignancies
Bibliographic record
Abstract
BACKGROUND: Outcomes for patients with locally advanced head and neck cancer (HNC) treated with curative intent remain disappointing, with 5-year survival rates at 50%. Most recurrences occur within the first 2 years after treatment, providing a window of opportunity to identify patients with molecular residual disease (MRD). A tissue-agnostic test for MRD detection in patients with human papillomavirus (HPV) positive and negative HNC, where tissue is often scarce, is needed. PATIENTS AND METHODS: Patients with stage I-IVB HNC, including patients positive and negative for HPV, were enrolled and peripheral blood plasma was collected longitudinally at diagnosis and ∼3, 12, and 24 months after curative intent treatment. The full cohort includes 325 patients with 1155 samples. Samples were split into distinct sets to train and validate a classifier capable of identifying MRD using a tissue-agnostic genome-wide methylome enrichment platform. The primary endpoint was recurrence-free survival (RFS). RESULTS: With a median follow-up of 60 months, patients in the blinded validation set with MRD positivity experienced significantly worse RFS with a hazard ratio (HR) of 35.7 [95% confidence interval (CI) 10.8-117.8; P < 0.0001]. For patients with HPV negativity, HR was 42.3 (95% CI 9.8-182.3; P < 0.0001); for patients with HPV-positive oropharyngeal cancer, HR was 24.1 (95% CI 3.0-196.8; P < 0.0001). Moreover, the lead time between MRD positivity and clinical recurrence was up to 14.9 months, with a mean lead time of 4.1 months. Surveillance sensitivity was 91% (95% CI 77% to 97%) and specificity was 88% (95% CI 80% to 93%). CONCLUSIONS: Here we validate the clinical performance characteristics of a tissue-agnostic genome-wide methylome enrichment assay for MRD detection in patients with HNC. The MRD detection test showed high sensitivity for identifying recurrence at high specificity across different anatomical sites, HPV status, and treatment regimens, highlighting the broad applicability for MRD detection in patients with HNC.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".