Early cancer detection using low-coverage whole-genome sequencing of cell-free DNA fragments

• Published in: Journal of clinical oncology Vol. 39; no. 15_suppl; p. e22510
• Main Authors: Yang, Shunli, Zhihua, Pei, Yu, Jianing, Zhao, Xiuyu, Liu, Yiqian, Zhang, Yaxi, Chen, Geng, Chen, Weizhi, He, Ji
• Format: Journal Article
• Language: English
• Published: 20-05-2021
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2021.39.15_suppl.e22510

Abstract only e22510 Background: Recent advances in circulating cell-free DNA (cfDNA) of plasma have shown that tumor diagnosis based on tumor-specific genetic and epigenetic changes (e.g., somatic mutations, copy number variations, and DNA methylation) is a promising non-invasive method. However, the number of tumor-specific genomic variants identified by whole-genome sequencing (WGS) in early cancer patients is very limited. Moreover, the mutations generated by clonal hematopoiesis in cfDNA can further confound the detection of cancer-specific mutations. It has been shown that ctDNA and cfDNA fragments have differences in length distribution. Compared with a limited number of genomic mutations, cfDNA fragment size index (FSI) is more abundant and easier to be detected. Methods: We designed a novel method for fragment detection of plasma cfDNA based on low-coverage WGS. The fragment length differences between healthy individuals and tumor patients were systematically analyzed. The training dataset includes 50 healthy individuals and 354 patients from eight different cancers. After the data preprocessing, we calculated the weight of fragmental bins and built a model for distinguishing healthy individuals from cancer patients. An independent dataset involving 22 healthy controls and 340 cancer patients was used to validate the model. The performance of our method was measured by the area under the curve (AUC) using the one-versus-all approach. Results: In our analysis, a total of 504 markers were selected from the dataset for model construction. Our model performed well for all cancer types on both training (AUC = 0.804) and validation (AUC = 0.837) datasets. Conclusions: The good performance of our model in large-scale plasma samples demonstrates the potential clinical application of cfDNA fragment analysis in early cancer detection based on low-coverage WGS.