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Neoplasia. 2015 Apr;17(4):385-99. doi: 10.1016/j.neo.2015.03.004.

Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors.

Author information

1
Michigan Center for Translational Pathology, Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
2
Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
3
Thermo Fisher Scientific, Ann Arbor, MI, USA.
4
Department of Pathology, Oregon Health and Sciences University, Portland, OR, USA.
5
Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA; Department of Urology, Thomas Jefferson University, Philadelphia, PA, USA; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
6
Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.
7
Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI, USA; Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.
8
Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
9
Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Translational Pathology, Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
10
Michigan Center for Translational Pathology, Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Translational Pathology, Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA.
11
Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Thermo Fisher Scientific, Ann Arbor, MI, USA.
12
Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Translational Pathology, Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: tomlinss@umich.edu.

Abstract

Next-generation sequencing (NGS) has enabled genome-wide personalized oncology efforts at centers and companies with the specialty expertise and infrastructure required to identify and prioritize actionable variants. Such approaches are not scalable, preventing widespread adoption. Likewise, most targeted NGS approaches fail to assess key relevant genomic alteration classes. To address these challenges, we predefined the catalog of relevant solid tumor somatic genome variants (gain-of-function or loss-of-function mutations, high-level copy number alterations, and gene fusions) through comprehensive bioinformatics analysis of >700,000 samples. To detect these variants, we developed the Oncomine Comprehensive Panel (OCP), an integrative NGS-based assay [compatible with <20 ng of DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues], coupled with an informatics pipeline to specifically identify relevant predefined variants and created a knowledge base of related potential treatments, current practice guidelines, and open clinical trials. We validated OCP using molecular standards and more than 300 FFPE tumor samples, achieving >95% accuracy for KRAS, epidermal growth factor receptor, and BRAF mutation detection as well as for ALK and TMPRSS2:ERG gene fusions. Associating positive variants with potential targeted treatments demonstrated that 6% to 42% of profiled samples (depending on cancer type) harbored alterations beyond routine molecular testing that were associated with approved or guideline-referenced therapies. As a translational research tool, OCP identified adaptive CTNNB1 amplifications/mutations in treated prostate cancers. Through predefining somatic variants in solid tumors and compiling associated potential treatment strategies, OCP represents a simplified, broadly applicable targeted NGS system with the potential to advance precision oncology efforts.

PMID:
25925381
PMCID:
PMC4415141
DOI:
10.1016/j.neo.2015.03.004
[Indexed for MEDLINE]
Free PMC Article

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