Abstract LB-247: Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis
Authors: Shuo Li, Zorawar Noor, Weihua Zeng, Xiaohui Ni, Zuyang Yuan, Frank Alber, Wenyuan Li, Edward B. Garon, Xianghong Zhou, Wing Hung Wong, Vatche G. Agopian, Steven M. Dubinett M. Dubinett
Journal: Cancer Research (2020)

Abstract Liquid biopsy using cell-free DNA (cfDNA) is attractive for a wide range of clinical applications, including cancer detection, locating, and monitoring. However, developing these applications requires precise and sensitive calling of somatic single nucleotide variations (SNVs) from cfDNA sequencing data. To date, no SNV caller comprehensively addresses the unique challenges of cfDNA to provide reliable results. Here we present cfSNV, a revolutionary somatic SNV caller with five innovative techniques to overcome and exploit the unique properties of cfDNA: (1) a joint-genotype statistical model, to modeling the mixed nature of cfDNA and thus probabilistically deconvolute the mixture of tumor- and normal-derived reads; (2) hierarchical mutation profiling to detect clusters of mutations with different variant allele frequencies; (3) a data pre-processing step that exploits the overlapping read mates caused by short cfDNA fragments to improve data quality; (4) a new filtering procedure to remove misalignment errors that account for the non-random fragmentation pattern of cfDNA; and (5) a machine learning approach that incorporates the sequencing context to filter errors at the level of individual reads. These five techniques permit cfSNV to far outperform existing methods in sensitively and reliably detecting mutations in cfDNA, even with medium-coverage WES data. On both simulated data and real patient data, we demonstrate that cfSNV is superior to existing tools, especially for low-frequency somatic SNVs. On simulated data, without sacrificing precision, cfSNV showed 3.0- and 3.7-fold increase in sensitivity of all somatic SNVs, and specifically 11.1- and 138.5-fold increase in sensitivity of somatic SNVs with allele frequency &amp;lt; 1% comparing to MuTect and Strelka2 respectively. The superior performance of cfSNV has been further confirmed on real patient data from 18 metastatic breast cancer and prostate cancer patients. Besides comparison with existing tools, we also showed how the five novel techniques contribute to its performance. The applicability of cfSNV to WES data enables cfDNA to be used in a wide variety of clinical applications, and we present one example of such an application: a novel and effective immunotherapy response measure that we name truncal-bTMB. This measure exploits cfDNA's ability to provide comprehensive coverage of the mutation landscape and hence describe the clonal structure. We demonstrated this new marker allows a better selection of non-small-cell lung cancer patients for immunotherapy treatment. Citation Format: Shuo Li, Zorawar Noor, Weihua Zeng, Xiaohui Ni, Zuyang Yuan, Frank Alber, Wenyuan Li, Edward B. Garon, Xianghong Zhou, Wing Hung Wong, Vatche G. Agopian, Steven M. Dubinett M. Dubinett. Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-247.