Multidimensional Cell-Free DNA Fragmentomic Assay for Detection of Early-Stage Lung Cancer.

Abstract

<b>Rationale:</b> Cell-free DNA (cfDNA) analysis holds promise for early detection of lung cancer and benefits patients with higher survival. However, the detection sensitivity of previous cfDNA-based studies was still low to suffice for clinical use, especially for early-stage tumors. <b>Objectives:</b> Establish an accurate and affordable approach for early-stage lung cancer detection by integrating cfDNA fragmentomics and machine learning models. <b>Methods:</b> This study included 350 participants without cancer and 432 participants with cancer. The participants' plasma cfDNA samples were profiled by whole-genome sequencing. Multiple cfDNA features and machine learning models were compared in the training cohort to achieve an optimal model. Model performance was evaluated in three validation cohorts. <b>Measurements and Main Results:</b> A stacked ensemble model integrating five cfDNA features and five machine learning algorithms constructed in the training cohort (cancer: 113; healthy: 113) outperformed all the models built on individual feature-algorithm combinations. This integrated model yielded superior sensitivities of 91.4% at 95.7% specificity for cohort validation I (area under the curve [AUC], 0.984), 84.7% at 98.6% specificity for validation II (AUC, 0.987), and 92.5% at 94.2% specificity for additional validation (AUC, 0.974), respectively. The model's high performance remained consistent when sequencing depth was down to 0.5× (AUC, 0.966-0.971). Furthermore, our model is sensitive to identifying early pathological features (83.2% sensitivity for stage I, 85.0% sensitivity for <1 cm tumor at the 0.66 cutoff). <b>Conclusions:</b> We have established a stacked ensemble model using cfDNA fragmentomics features and achieved superior sensitivity for detecting early-stage lung cancer, which could promote early diagnosis and benefit more patients.