Copy Number Alteration Profiling from Plasma cfDNA WES in Advanced NSCLC

Circulating cell-free DNA (cfDNA) sequencing offers a minimally invasive approach for profiling tumor genomes, but detecting copy number alterations (CNAs) from cfDNA whole-exome sequencing (WES) remains technically challenging due to noise and guanine-cytosine (GC)-related bias. Building upon our previous study that characterized read count patterns in cfDNA WES data, we developed and evaluated an advanced pipeline for robust CNA detection in patients with advanced non-small cell lung cancer (NSCLC). Read count signals showed strong correlation with GC content, and applying locally estimated scatterplot smoothing (LOESS)-based GC bias correction effectively reduced false positives and improved CNA detection. The resulting cfDNA CNA profiles were reproducible within patients and showed strong concordance with The Cancer Genome Atlas (TCGA) tissue-level patterns for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). These findings demonstrate that cfDNA WES, when combined with appropriate bias correction, can serve as a practical and minimally invasive alternative for genomic characterization of NSCLC.