Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis
Shuo Li, Zorawar Noor, Weihua Zeng, Mary L. Stackpole, Xiaohui Ni, Yonggang Zhou, Zuyang Yuan, Wing Hung Wong, Vatche G. Agopian, Steven M. Dubinett, Frank Alber, Wenyuan Li*, Edward B. Garon*, Xianghong Jasmine Zhou*
*    Joint-corresponding authors




Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content in cfDNA. Thus sensitive and accurate detection of low VAF mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this important challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of the clonal landscape, for the first time cfSNV can profile mutations even in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in terms of sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing of cfDNA a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide a promising biomarker to effectively predict immunotherapy outcomes.




  This software is only for academia users. Users are prohibited from transferring this software to others. For commercial users, please contact Prof. Jasmine Zhou (

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