Abstract 2190: A method for differentiating clonal driver mutations from subclonal emerging resistance mutations in circulating cell-free DNA

Background: Cell-free circulating tumor DNA analysis provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment. Deep sequencing of cell-free DNA (cfDNA) can potentially provide insights into tumor heterogeneity across multiple tumor sites in a patient, including emerging treatment-resistant subclones. However, the increased informational complexity of polyclonal cfDNA in circulation poses analysis challenges, particularly in tumors with abundant copy number alterations. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality, Methods: We analyzed a large clinical sequencing database of somatic point mutations and copy number alterations from targeted cfDNA sequencing of 21,807 consecutive patients across >50 cancer types (Guardant Health, CA). We evaluated a minimal cfDNA clonality model that relies on the relationship between variant allele frequency (VAF) in cfDNA and the level of tumor DNA in circulation (ctDNA level), while accounting for copy number alterations. Results: We found that the initial simple model of cfDNA clonality performed well on >90% of samples, given a relatively small targeted genomic region (70 genes, 150 kb). However, normalizing VAF by copy number is subject to error in some samples due to the effect of ctDNA level on variant detection, variable unique molecule coverage across samples, and non-linearity of VAF at high copy number. Therefore, we developed an improved cfDNA clonality model that incorporated these analytical and biological features, which was then trained on a portion of the large cfDNA data set. Our cfDNA clonality model accurately distinguished subclonal resistance from driver alterations in a test set of over 5,000 lung, colorectal, and breast cancer patients. Although numerous subclonal tumor-derived alterations were apparent in the initial test data set, leading to an apparent departure from mutual exclusivity in treatment-naïve tumors, robust mutual exclusivity was observed among cfDNA clonal driver alterations when our cfDNA clonality analysis method was applied. These results suggest our analytical approach can be used to identify treatment-associated emerging resistance alterations in patients from a single blood draw, including parallel evolution of distinct subclonal alterations. Conclusion: Managing cancer will likely depend on identifying emerging treatment-resistant subclones at or in anticipation of progression. Highly accurate deep sequencing of cfDNA, along with comprehensive models of cfDNA clonality, can elucidate subclonal structure of the tumor and identify emerging treatment resistance. Citation Format: Stephen Fairclough, Oliver Zill, Catalin Barbacioru, Justin Odegaard, Richard B. Lanman, AmirAli Talasaz, Darya Chudova. A method for differentiating clonal driver mutations from subclonal emerging resistance mutations in circulating cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2190.