bioRxivCancer Biology
The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients
O. Zill, K. Banks, S. Fairclough, S. Mortimer, J. Vowles, R. Mokhtari, D. R. Gandara, P. C. Mack, J. Odegaard, R. Nagy, A. Baca, H. Eltoukhy, D. Chudova, R. Lanman, A. Talasaz
Abstract
Cell-free DNA (cfDNA) sequencing provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment, but the presence of multiple alterations in circulation related to treatment and tumor heterogeneity pose analytical challenges. We present the somatic mutation landscape of 70 cancer genes from cfDNA deep-sequencing analysis of 21,807 patients with treated, late-stage cancers across >50 cancer types. Patterns and prevalence of cfDNA alterations in major driver genes for non-small cell lung, breast, and colorectal cancer largely recapitulated those from tumor tissue sequencing compendia (TCGA and COSMIC), with the principle differences in alteration prevalence being due to patient treatment. This highly sensitive cfDNA sequencing assay revealed numerous subclonal tumor-derived alterations, expected as a result of clonal evolution, but leading to an apparent departure from mutual exclusivity in treatment-naive tumors. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality. Upon applying these methods, robust mutual exclusivity was observed among predicted truncal driver cfDNA alterations, in effect distinguishing tumor-initiating alterations from secondary alterations. Treatment-associated resistance, including both novel alterations and parallel evolution, was common in the cfDNA cohort and was enriched in patients with targetable driver alterations. Together these retrospective analyses of a large set of cfDNA deep-sequencing data reveal subclonal structures and emerging resistance in advanced solid tumors.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
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