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Affiliation(s)
- Matthew S Lebo
- Bioinformatics and Laboratory of Molecular Medicine, Partners Personalized Medicine, 65 Landsdowne Street, Cambridge, MA 02139, USA; Pathology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Limin Hao
- Bioinformatics and Laboratory of Molecular Medicine, Partners Personalized Medicine, 65 Landsdowne Street, Cambridge, MA 02139, USA
| | - Chiao-Feng Lin
- Bioinformatics and Laboratory of Molecular Medicine, Partners Personalized Medicine, 65 Landsdowne Street, Cambridge, MA 02139, USA
| | - Arti Singh
- Bioinformatics and Laboratory of Molecular Medicine, Partners Personalized Medicine, 65 Landsdowne Street, Cambridge, MA 02139, USA
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2
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Contreras AL, Andal JJL, Lo RM, Ang DC. Pre-analytics, Current Testing Technologies, and Limitations of Testing. Genomic Med 2020. [DOI: 10.1007/978-3-030-22922-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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3
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Carter AB. Considerations for Genomic Data Privacy and Security when Working in the Cloud. J Mol Diagn 2019; 21:542-552. [DOI: 10.1016/j.jmoldx.2018.07.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 05/16/2018] [Accepted: 07/02/2018] [Indexed: 01/21/2023] Open
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4
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Wrzeszczynski KO, Felice V, Abhyankar A, Kozon L, Geiger H, Manaa D, London F, Robinson D, Fang X, Lin D, Lamendola-Essel MF, Khaira D, Dikoglu E, Emde AK, Robine N, Shah M, Arora K, Basturk O, Bhanot U, Kentsis A, Mansukhani MM, Bhagat G, Jobanputra V. Analytical Validation of Clinical Whole-Genome and Transcriptome Sequencing of Patient-Derived Tumors for Reporting Targetable Variants in Cancer. J Mol Diagn 2018; 20:822-835. [PMID: 30138725 PMCID: PMC6198246 DOI: 10.1016/j.jmoldx.2018.06.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/24/2018] [Accepted: 06/21/2018] [Indexed: 02/07/2023] Open
Abstract
We developed and validated a clinical whole-genome and transcriptome sequencing (WGTS) assay that provides a comprehensive genomic profile of a patient's tumor. The ability to fully capture the mappable genome with sufficient sequencing coverage to precisely call DNA somatic single nucleotide variants, insertions/deletions, copy number variants, structural variants, and RNA gene fusions was analyzed. New York State's Department of Health next-generation DNA sequencing guidelines were expanded for establishing performance validation applicable to whole-genome and transcriptome sequencing. Whole-genome sequencing laboratory protocols were validated for the Illumina HiSeq X Ten platform and RNA sequencing for Illumina HiSeq2500 platform for fresh or frozen and formalin-fixed, paraffin-embedded tumor samples. Various bioinformatics tools were also tested, and CIs for sensitivity and specificity thresholds in calling clinically significant somatic aberrations were determined. The validation was performed on a set of 125 tumor normal pairs. RNA sequencing was performed to call fusions and to confirm the DNA variants or exonic alterations. Here, we present our results and WGTS standards for variant allele frequency, reproducibility, analytical sensitivity, and present limit of detection analysis for single nucleotide variant calling, copy number identification, and structural variants. We show that The New York Genome Center WGTS clinical assay can provide a comprehensive patient variant discovery approach suitable for directed oncologic therapeutic applications.
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Affiliation(s)
| | | | | | | | | | - Dina Manaa
- New York Genome Center, New York, New York
| | | | | | | | - David Lin
- New York Genome Center, New York, New York
| | | | | | | | | | | | | | | | - Olca Basturk
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Umesh Bhanot
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alex Kentsis
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Pediatrics, Weill Cornell Medical College of Cornell University and Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Govind Bhagat
- Columbia University Medical Center, Columbia University, New York, New York
| | - Vaidehi Jobanputra
- New York Genome Center, New York, New York; Columbia University Medical Center, Columbia University, New York, New York.
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5
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Zomnir MG, Lipkin L, Pacula M, Dominguez Meneses E, MacLeay A, Duraisamy S, Nadhamuni N, Al Turki SH, Zheng Z, Rivera M, Nardi V, Dias-Santagata D, Iafrate AJ, Le LP, Lennerz JK. Artificial Intelligence Approach for Variant Reporting. JCO Clin Cancer Inform 2018; 2:CCI.16.00079. [PMID: 30364844 PMCID: PMC6198661 DOI: 10.1200/cci.16.00079] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Purpose Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.
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Affiliation(s)
| | - Lev Lipkin
- All authors: Massachusetts General Hospital, Boston, MA
| | - Maciej Pacula
- All authors: Massachusetts General Hospital, Boston, MA
| | | | | | | | | | | | - Zongli Zheng
- All authors: Massachusetts General Hospital, Boston, MA
| | - Miguel Rivera
- All authors: Massachusetts General Hospital, Boston, MA
| | | | | | | | - Long P. Le
- All authors: Massachusetts General Hospital, Boston, MA
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6
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Roy S, Coldren C, Karunamurthy A, Kip NS, Klee EW, Lincoln SE, Leon A, Pullambhatla M, Temple-Smolkin RL, Voelkerding KV, Wang C, Carter AB. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines. J Mol Diagn 2018; 20:4-27. [DOI: 10.1016/j.jmoldx.2017.11.003] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 10/06/2017] [Accepted: 11/06/2017] [Indexed: 12/17/2022] Open
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7
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Yohe S, Thyagarajan B. Review of Clinical Next-Generation Sequencing. Arch Pathol Lab Med 2017; 141:1544-1557. [PMID: 28782984 DOI: 10.5858/arpa.2016-0501-ra] [Citation(s) in RCA: 203] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT - Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS. OBJECTIVE - To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care. DATA SOURCES - The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center. CONCLUSIONS - The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.
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Affiliation(s)
- Sophia Yohe
- From the Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis
| | - Bharat Thyagarajan
- From the Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis
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Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, Tsimberidou AM, Vnencak-Jones CL, Wolff DJ, Younes A, Nikiforova MN. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 2017; 19:4-23. [PMID: 27993330 DOI: 10.1016/j.jmoldx.2016.10.002] [Citation(s) in RCA: 1189] [Impact Index Per Article: 169.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 10/03/2016] [Accepted: 10/13/2016] [Indexed: 01/01/2023] Open
Abstract
Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.
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Affiliation(s)
- Marilyn M Li
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Laboratory Medicine, Division of Genomic Diagnostics, the Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
| | - Michael Datto
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Duke University School of Medicine, Durham, North Carolina
| | - Eric J Duncavage
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Shashikant Kulkarni
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Baylor Genetics, Houston, Texas
| | - Neal I Lindeman
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Somak Roy
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Apostolia M Tsimberidou
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Cindy L Vnencak-Jones
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Daynna J Wolff
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Anas Younes
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marina N Nikiforova
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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9
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Harris G, O'Toole S, George P, Browett P, Print C. Massive parallel sequencing of solid tumours - challenges and opportunities for pathologists. Histopathology 2016; 70:123-133. [DOI: 10.1111/his.13067] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Gavin Harris
- Department of Molecular Medicine and Pathology and Bioinformatics Institute; University of Auckland; Auckland New Zealand
- Canterbury Health Laboratories; Christchurch New Zealand
| | - Sandra O'Toole
- Department of Tissue Pathology and Diagnostic Oncology; Royal Prince Alfred Hospital; Camperdown NSW Australia
- Sydney Medical School; Sydney University; Sydney Australia
- The Kinghorn Cancer Centre; Garvan Institute of Medical Research; Darlinghurst NSW Australia
| | - Peter George
- Canterbury Health Laboratories; Christchurch New Zealand
| | - Peter Browett
- Department of Molecular Medicine and Pathology and Bioinformatics Institute; University of Auckland; Auckland New Zealand
| | - Cristin Print
- Department of Molecular Medicine and Pathology and Bioinformatics Institute; University of Auckland; Auckland New Zealand
- Maurice Wilkins Centre; c/o University of Auckland; Auckland New Zealand
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