Optimizing Cancer Genome Sequencing and Analysis

Malachi Griffith, Christopher A. Miller, Obi L. Griffith, Kilannin Krysiak, Zachary L. Skidmore, Avinash Ramu, Jason R. Walker, Ha X. Dang, Lee Trani, David E. Larson, Ryan T. Demeter, Michael C. Wendl, Joshua F. McMichael, Rachel E. Austin, Vincent Magrini, Sean D. McGrath, Amy Ly, Shashikant Kulkarni, Matthew G. Cordes, Catrina C. FronickRobert S. Fulton, Christopher A. Maher, Li Ding, Jeffery M. Klco, Elaine R. Mardis, Timothy J. Ley, Richard K. Wilson

Research output: Contribution to journalArticlepeer-review

123 Scopus citations

Abstract

Summary Tumors are typically sequenced to depths of 75x-100x (exome) or 30x-50x (whole genome). We demonstrate that current sequencing paradigms are inadequate for tumors that are impure, aneuploid, or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ∼312x) whole genome sequencing and exome capture (up to ∼433x) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested multiple alignment and variant calling algorithms and validated ∼200,000 putative SNVs by sequencing them to depths of ∼1,000x. Additional targeted sequencing provided over 10,000x coverage and ddPCR assays provided up to ∼250,000x sampling of selected sites. We evaluated the effects of different library generation approaches, depth of sequencing, and analysis strategies on the ability to effectively characterize a complex tumor. This dataset, representing the most comprehensively sequenced tumor described to date, will serve as an invaluable community resource (dbGaP: phs000159).

Original languageEnglish
Article number35
Pages (from-to)210-223
Number of pages14
JournalCell Systems
Volume1
Issue number3
DOIs
StatePublished - Sep 23 2015

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