Cost-effective shallow genome-wide sequencing for profiling plasma cfDNA signatures to enhance lung cancer detection

Lung cancer (LC) screening via low-dose computed tomography (LDCT) faces challenges including high false-positive rates and low patient compliance. Circulating tumor DNA (ctDNA)-based tests offer a minimally invasive alternative but are limited by high costs and low sensitivity, particularly in early-stage detection. This study introduces a cost-effective, shallow genome-wide sequencing approach for LC detection by profiling multiple cell-free DNA (cfDNA) signatures. We developed a multimodal cfDNA assay with shallow sequencing coverage (0.5×) that integrates fragmentomic, nucleosome, end-motif, and copy number alteration analyses. A machine-learning model trained on a discovery cohort (99 LC patients, 168 healthy controls) and validated on an independent cohort (58 LC patients, 71 controls) demonstrated robust performance. The ensemble model exhibited outstanding performance, achieving an AUC of 0.97 and a specificity of 92% in both the discovery and validation cohorts, with sensitivities of 94% and 90%, respectively. Notably, it outperformed hotspot mutation-based assays and the multi-cancer SPOT-MAS assay in sensitivity across all LC stages. This assay provides a cost-effective, accurate, and minimally invasive method for LC detection, addressing the limitations of current screening methods. It represents a promising complementary tool to improve early detection and patient outcomes in LC. Lung cancer screening can be expensive and sometimes gives false positives, leading to unnecessary worry and extra tests. Many people also skip screening because it is inconvenient. A simple blood test could be a better option, but current ones are costly and not always accurate for early detection. In this study, researchers developed an affordable blood test that looks at tiny fragments of DNA in the blood. They used machine learning to train the test on samples from lung cancer patients and healthy people, then its accuracy was checked on a separate group. The test correctly detected lung cancer in 90% of cases and ruled out healthy individuals with 92% accuracy. It outperformed existing tests and could become an easy and effective way to catch lung cancer early, helping more people get treated in time.