Salivary DNA methylation derived estimates of biological aging, cellular frequency and protein expression as predictors of oral mucositis severity and survival in head and neck cancer patients
Bibliographic record
Abstract
BACKGROUND: Oral mucositis is a painful and debilitating condition that occurs in the majority of head and neck cancer patients receiving radiation and/or chemotherapy. While some patient and treatment related factors are known to contribute to the incidence and severity of disease, reliable biomarkers remain elusive. In the following study, we investigated the association of salivary DNA methylation derived biological aging, cellular frequency and protein concentration measures with the severity of oral mucositis and overall survival in a cohort of head and neck cancer (HNC) patients (n = 103). METHODS: DNA methylation profiling was performed on saliva samples obtained prior to treatment. Biological aging measures included Horvath2, PhenoAge, FitAge and GrimAge, and cellular frequency included epithelial and specific immune cell populations. RESULTS: Severe mucositis (i.e. grade 3 or 4) occurred in nearly half of patients. For malignant HNC patients (n = 84), every 1-SD increase in GrimAge was associated with 2.62-times risk of severe mucositis (95 % CI: 1.38, 5.57), while a 1-SD increase in monocyte frequency was associated with a decreased risk (OR [95 %CI]: 0.40 [0.18, 0.80]). Over a median follow-up of 53 months, 39 of 103 participants died. Six protein scores (TNFSF14, GCSF, MATN3, GDF8, nCDase, TNF-β) were associated with survival at q < 0.15. CONCLUSION: We provide evidence that the risk-related biological aging measure GrimAge may be a useful predictor of mucositis severity in HNC patients. Salivary monocyte frequency may be protective against mucositis, and this measure could be used as a predictive biomarker while also providing clues into the pathobiology of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".