Photo Credit: Md Babul Hosen

The following is a summary of “Evaluation and integration of cell-free DNA signatures for detection of lung cancer,” published in the September 2024 issue of Oncology by Xue et al.

Cell-free DNA (cfDNA) analysis has emerged as a promising approach for the early detection of lung cancer by leveraging non-genetic features. This study aimed to differentiate patients with lung cancer from healthy individuals through comprehensive cfDNA analysis. Peripheral blood samples were collected from 926 patients with lung cancer and 611 healthy individuals, followed by cfDNA extraction. The cfDNA was subjected to low-pass whole-genome sequencing and targeted methylation sequencing, allowing for the evaluation of various cfDNA characteristics. Researchers developed an ensemble stacked model named ESim-seq (Early Screening tech with Integrated Model), utilizing a customized algorithm that identified the most informative features. 

The ESim-seq model demonstrated high diagnostic accuracy in an independent validation cohort, achieving an area under the curve (AUC) of 0.948 (95% CI: 0.915–0.981). The model exhibited a sensitivity of 79.3% (95% CI: 71.5–87.0%) across all cancer stages, paired with a specificity of 96.0% (95% CI: 90.6–100.0%). Notably, the model’s sensitivity was 76.5% (95% CI: 67.3–85.8%) for stage I patients, 100% (95% CI: 100.0–100.0%) for both stage II and III patients, and 87.5% (95% CI: 64.6%–100.0%) for stage IV patients. In addition, investigators developed the LCSC model (Lung Cancer Subtype multiple Classification), which effectively distinguished between small cell lung cancer (SCLC) and patients with non-small cell lung cancer (NSCLC), achieving an AUC of 0.961 (95% CI: 0.949–0.957). 

This study establishes a robust framework for assessing cfDNA features and highlights the advantages of integrating multiple cfDNA characteristics for the early detection and classification of lung cancer. The results suggest that cfDNA-based screening could significantly enhance early diagnosis, improving patient outcomes.

Source: sciencedirect.com/science/article/abs/pii/S0304383524006116