Abstract Background: Lung cancer is the most lethal malignancy worldwide and urgently requires effective early detection strategies. Current non-invasive approaches based on plasma cell-free DNA (cfDNA) fragmentomics often suffer from limited sensitivity in early-stage patients due to low tumor DNA fractions. Methods: We developed a novel computational feature, First-Order Transition Probability (FOTP), to capture nucleotide sequential dependencies within cfDNA fragments. Using low-pass whole genome sequencing data from 1,036 participants, we systematically analyzed cfDNA fragment ends to identify discriminative regions for cancer detection and trained a support vector machine (SVM) model leveraging the FOTP features. Results: Analysis revealed that the first 10 bp at the 5′ end of cfDNA fragments contained the most discriminative information. The SVM model achieved an area under the ROC curve (AUC) of 0.942, with 73.9% sensitivity for stage I and 81.8% for stage II lung cancer at 95% specificity, significantly outperforming existing fragmentomic features. Nucleotide frequency stability and entropy patterns beyond the initial 10 bp supported the biological basis of the approach, reflecting nuclease cleavage biases and chromatin features. The method generalized robustly across independent cohorts and multi-cancer validation sets, showing potential for tissue-of-origin prediction. Conclusions: FOTP is a biologically interpretable and highly efficient feature for early cancer detection. It captures key nucleotide dependencies at cfDNA fragment ends, enhancing sensitivity for early-stage lung cancer and other cancers. Impact: This approach offers a scalable and generalizable strategy for early cancer screening.
Ji et al. (Fri,) studied this question.