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Hartman P, Beckman K, Silverstein K, Yohe S, Schomaker M, Henzler C, Onsongo G, Lam HC, Munro S, Daniel J, Billstein B, Deshpande A, Hauge A, Mroz P, Lee W, Holle J, Wiens K, Karnuth K, Kemmer T, Leary M, Michel S, Pohlman L, Thayanithy V, Nelson A, Bower M, Thyagarajan B. Next generation sequencing for clinical diagnostics: Five year experience of an academic laboratory. Mol Genet Metab Rep 2019; 19:100464. [PMID: 30891420 PMCID: PMC6403447 DOI: 10.1016/j.ymgmr.2019.100464] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 02/25/2019] [Indexed: 01/13/2023] Open
Abstract
Clinical laboratories have adopted next generation sequencing (NGS) as a gold standard for the diagnosis of hereditary disorders because of its analytic accuracy, high throughput, and potential for cost-effectiveness. We describe the implementation of a single broad-based NGS sequencing assay to meet the genetic testing needs at the University of Minnesota. A single hybrid capture library preparation was used for each test ordered, data was informatically blinded to clinically-ordered genes, and identified variants were reviewed and classified by genetic counselors and molecular pathologists. We performed 2509 sequencing tests from August 2012 till December 2017. The diagnostic yield has remained steady at 25%, but the number of variants of uncertain significance (VUS) included in a patient report decreased over time with 50% of the patient reports including at least one VUS in 2012 and only 22% of the patient reports reporting a VUS in 2017 (p = .002). Among the various clinical specialties, the diagnostic yield was highest in dermatology (60% diagnostic yield) and ophthalmology (42% diagnostic yield) while the diagnostic yield was lowest in gastrointestinal diseases and pulmonary diseases (10% detection yield in both specialties). Deletion/duplication analysis was also implemented in a subset of panels ordered, with 9% of samples having a diagnostic finding using the deletion/duplication analysis. We have demonstrated the feasibility of this broad-based NGS platform to meet the needs of our academic institution by aggregating a sufficient sample volume from many individually rare tests and providing a flexible ordering for custom, patient-specific panels.
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Affiliation(s)
- Paige Hartman
- University of Minnesota Medical School, Duluth, MN, United States of America
| | - Kenneth Beckman
- University of Minnesota Genomics Center, University of Minnesota, Minneapolis, MN, United States of America
| | - Kevin Silverstein
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, United States of America
| | - Sophia Yohe
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States of America
| | - Matthew Schomaker
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Christine Henzler
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, United States of America
| | - Getiria Onsongo
- Department of Mathematics, Statistics, and Computer Science, Macalaster College, St Paul, MN, United States of America
| | - Ham Ching Lam
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, United States of America
| | - Sarah Munro
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, United States of America
| | - Jerry Daniel
- University of Minnesota Genomics Center, University of Minnesota, Minneapolis, MN, United States of America
| | - Bradley Billstein
- University of Minnesota Genomics Center, University of Minnesota, Minneapolis, MN, United States of America
| | - Archana Deshpande
- University of Minnesota Genomics Center, University of Minnesota, Minneapolis, MN, United States of America
| | - Adam Hauge
- Illumina Inc, San Diego, CA, United States of America
| | - Pawel Mroz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States of America
| | - Whiwon Lee
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America.,Division of Genetics and Metabolism, University of Minnesota Health, Minneapolis, MN, United States of America
| | | | - Katie Wiens
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America.,Division of Genetics and Metabolism, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Kylene Karnuth
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Teresa Kemmer
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Michaela Leary
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Stephen Michel
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Laurie Pohlman
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Venugopal Thayanithy
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Andrew Nelson
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States of America
| | - Matthew Bower
- Molecular Diagnostics Laboratory, University of Minnesota Health, Minneapolis, MN, United States of America.,Division of Genetics and Metabolism, University of Minnesota Health, Minneapolis, MN, United States of America
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States of America
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Nelson AC, Boone J, Cartwright D, Thyagarajan B, Kincaid R, Lambert AP, Karnuth K, Henzler C, Yohe S. Optimal detection of clinically relevant mutations in colorectal carcinoma: sample pooling overcomes intra-tumoral heterogeneity. Mod Pathol 2018; 31:343-349. [PMID: 29027537 DOI: 10.1038/modpathol.2017.120] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 08/04/2017] [Accepted: 08/05/2017] [Indexed: 11/09/2022]
Abstract
Intra-tumoral genomic heterogeneity is a well-established biologic property of human malignancies with emerging roles in cancer progression and therapy resistance. However, its impact on the clinical utility of genomic testing in patient management remains unclear. Furthermore, best practices to account for heterogeneity in the provision of highly accurate, clinically valid molecular testing have yet to be firmly established. Genomic biomarkers for the management of colorectal carcinoma are both well-established (ie, KRAS, NRAS) and emerging (BRAF, PIK3CA, and others) in respect to therapy selection and clinical trial eligibility. Critically, standard colorectal carcinoma management requires the exclusion of KRAS and NRAS mutations; thus optimal procedures to control for potential intra-tumoral heterogeneity are clinically important. Here, we assessed heterogeneity among three intra-tumoral sites within 99 colorectal carcinomas cases on a CLIA-validated oncology next generation sequencing assay and examined whether a pooling strategy overcame any discordant results. Overall, 11% of cases demonstrated discordant mutation results between sites; 2% of cases were discrepant for mutations within RAS genes while the remainder was discrepant in PIK3CA. Half of the discrepant cases were associated with borderline tumor cellularity assessment. Further, a sample pooling strategy across all three sites successfully detected the relevant mutation in all but one case. Our results indicate that intra-tumoral genomic heterogeneity of clinically relevant genes within colorectal carcinoma is a relatively infrequent occurrence and that a simple strategy to pool DNA from several tumor sites with adequate cellularity minimizes the risk of false negative results even further to ensure optimal patient management.
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Affiliation(s)
- Andrew C Nelson
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Jamie Boone
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - David Cartwright
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Robyn Kincaid
- Molecular Diagnostics Laboratory, M Health, Minneapolis, MN, USA
| | - Aaron P Lambert
- Molecular Diagnostics Laboratory, M Health, Minneapolis, MN, USA
| | - Kylene Karnuth
- Molecular Diagnostics Laboratory, M Health, Minneapolis, MN, USA
| | - Christine Henzler
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Sophia Yohe
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
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