A cfDNA fragmentomics-based liquid biopsy assay for early detection of oral cancer
Early detection of oral cancer improves outcomes, but remains limited by the invasiveness and low sensitivity of current screening methods. This study included 342 participants (200 in the training and 142 in the validation cohorts). Plasma cfDNA underwent low-depth whole-genome sequencing, with data normalized to a standardized 5× coverage via down-sampling to ensure consistent analysis. Three cfDNA-derived features, including fragment size ratio (FSR), copy number variation (CNV), and repetitive element profiles (REP), were extracted and integrated using a stacked machine learning ensemble to generate prediction scores. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity with 95% confidence intervals. In the training cohort, oral cancer patients had a median age of 53 years (91% male), while healthy individuals had a median age of 57 years (37% male). In the validation cohort, the median ages were 53 and 57 years, with male proportions of 83.1% and 38.0% in the cancer and healthy groups, respectively. Cancer samples exhibited shorter cfDNA fragments, recurrent CNV gains at 3q and 8q, and losses at 3p and 5q. The individual AUCs for FSR, CNV, and REP were 0.973, 0.983, and 0.979 in the training cohort, and 0.960, 0.982, and 0.972 in the validation cohort, respectively. The integrated stacked model achieved AUCs of 0.996 (95% CI: 0.991-1.000) and 0.988 (95% CI: 0.975–0.988) in the training and validation cohorts, respectively. To prioritize high specificity for large-scale screening, a decision threshold was established in the training cohort to target 98.0% specificity, which yielded a corresponding sensitivity of 98.0%. When this pre-fixed threshold was blindly applied to the independent validation cohort, a sensitivity of 94.4% and a specificity of 95.8% were achieved. Prediction scores were not significantly associated with age, sex, or tumor site. The cfDNA fragmentomics-based stacked model distinguished oral cancer from healthy individuals, including early stage disease, supporting its potential for noninvasive approach for oral cancer detection.