Abstract 577: A unified pipeline to detect small mutations, structural variations and copy number alterations from targeted cell-free DNA sequencing in cancer

Abstract

Introduction: Analysis of cell-free DNA (cfDNA) has recently emerged as a non-invasive modality for guiding cancer diagnostics and treatment decisions. However, efforts have predominantly focused on the analysis of single nucleotide variants (SNVs) and insertions/deletions (indels). Despite the clinical significance of many larger variants such as structural variations (SVs) and copy number alterations (CNAs), detecting them in cfDNA remains a challenge. Given the lack of existing tools we seek to develop an integrated bioinformatic pipeline for SV and CNA detection in cfDNA following targeted hybrid-capture next-generation sequencing (NGS), along with standard SNV and indel analysis. Methods: SVs were first detected using Manta, Lumpy and Delly in plasma cfDNA in comparison with matched peripheral blood leukocyte (PBL) DNA samples from cancer patients, then combined to identify consensus SVs and genotyped throughout samples from patients and healthy individuals. Next, consensus SVs were called somatic events if they were supported by split reads and discordant read pairs in cfDNA samples from patients but not in matched PBL or healthy donor cfDNA samples. For CNA analysis, the ratio of read depth between patient-derived plasma cfDNA and a panel of healthy controls was calculated across genomic bins using the CNVkit tool, followed by bias correction and recentralization using CNA negative control genes to account for read coverage imbalances in targeted NGS. Last, SNV and indel analysis was integrated from the CAPP-Seq pipeline. Results: We applied our pipeline to targeted hybrid-capture NGS data from 48 patients across two independent cohorts of metastatic castration resistant prostate cancer (mCRPC). The targeted panel covered the full-length AR gene body and a hotspot region of TMPRSS2-ERG fusion break points. Consistent with earlier whole genome studies, we confirmed known CNAs and SVs in tumor suppressors, oncogenes and regulatory elements including AR gene and AR enhancer duplications (22/48, 46% of patients), TMPRSS2-ERG gene fusions (9/48, 19%), PTEN and TP53 loss (8/48, 17%). Notably, our pipeline outperformed FACTERA which did not detect any TMPRSS2-ERG gene fusions or AR/enhancer tandem duplications. Subsequent analysis showed high concordance between plasma cfDNA and matched tumor biopsies, and our pipeline recapitulated the landscape of SVs and CNAs in an in silico cfDNA simulation from tumor biopsies. Finally, we showed that alterations of the AR/enhancer locus detected by our pipeline were strongly associated with treatment resistance, patient progression-free and overall survival in mCRPC. Conclusion: We developed a unified pipeline for detection of SVs, CNAs and small mutations in cfDNA targeted sequencing with potential applications in monitoring cancer progression and predicting patient treatment response. Citation Format: Ha X. Dang, Jace Webster, Pradeep S. Chauhan, Steven H. Hartman, Wenjia Feng, Elisa M. Ledet, Haley Ellis, Patrick J. Miller, Ellen B. Jaeger, Sydney A. Caputo, Peter K. Harris, A. Oliver Sartor, Russell K. Pachynski, Aadel A. Chaudhuri, Christopher A. Maher. A unified pipeline to detect small mutations, structural variations and copy number alterations from targeted cell-free DNA sequencing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 577.