Nguyen Van Thien Chi, Vo Dac Ho, Tran Thi Trang, Tran Thanh Truong, Nguyen Thi Hue Hanh, Vo Truong Dang Huy, Van Thi Tuong Vi, Vu Thi Luyen, Lam Minh Quang, Nguyen Giang Thi Huong, Tran Trung Hieu, Pham Ngoc Tan, Trac Quang Thinh, Nguyen Trong Hieu, Phan Thi Van, Dao Thi Huyen, Nguyen Huu Tam Phuc, Nguyen Luu Hong Dang, Nguyen Duy Sinh, Tang Hung Sang, Giang Hoa, Phan Minh Duy, Nguyen Hoai-Nghia, Tran Le Son
Research and Development Department, Medical Genetics Institute, Ho Chi Minh, Vietnam.
Future Oncol. 2025 May;21(11):1391-1402. doi: 10.1080/14796694.2025.2483154. Epub 2025 Mar 25.
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.
通过低剂量计算机断层扫描（LDCT）进行肺癌（LC）筛查面临诸多挑战，包括高假阳性率和低患者依从性。基于循环肿瘤DNA（ctDNA）的检测提供了一种微创替代方法，但受高成本和低灵敏度限制，尤其是在早期检测方面。本研究引入一种具有成本效益的全基因组浅层测序方法，通过分析多种游离DNA（cfDNA）特征来检测肺癌。
我们开发了一种具有浅层测序覆盖度（0.5×）的多模态cfDNA检测方法，该方法整合了片段组学、核小体、末端基序和拷贝数变异分析。在一个发现队列（99例肺癌患者，168例健康对照）上训练并在一个独立队列（58例肺癌患者，71例对照）上验证的机器学习模型表现出强大性能。
该集成模型表现出色，在发现队列和验证队列中AUC均为0.97，特异性均为92%，灵敏度分别为94%和90%。值得注意的是，在所有肺癌阶段的灵敏度方面，它均优于基于热点突变的检测方法和多癌种SPOT-MAS检测方法。
该检测方法为肺癌检测提供了一种经济高效、准确且微创的方法，克服了当前筛查方法的局限性。它是一种有前景的辅助工具，可改善肺癌的早期检测和患者预后。
