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Rosca OC, Vele OE. Microsatellite Instability, Mismatch Repair, and Tumor Mutation Burden in Lung Cancer. Surg Pathol Clin 2024; 17:295-305. [PMID: 38692812 DOI: 10.1016/j.path.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Since US Food and Drug Administration approval of programmed death ligand 1 (PD-L1) as the first companion diagnostic for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer, many patients have experienced increased overall survival. To improve selection of ICI responders versus nonresponders, microsatellite instability/mismatch repair deficiency (MSI/MMR) and tumor mutation burden (TMB) came into play. Clinical data show PD-L1, MSI/MMR, and TMB are independent predictive immunotherapy biomarkers. Harmonization of testing methodologies, optimization of assay design, and results analysis are ongoing. Future algorithms to determine immunotherapy eligibility might involve complementary use of current and novel biomarkers. Artificial intelligence could facilitate algorithm implementation to convert complex genetic data into recommendations for specific ICIs.
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Affiliation(s)
- Oana C Rosca
- Molecular Pathologist/Cytopathologist, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell; Department of Pathology and Laboratory Medicine, 2200 Northern Boulevard, Suite 104, Greenvale, NY 11548, USA.
| | - Oana E Vele
- Molecular Pathologist/Cytopathologist, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell; Department of Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, NY 10075, USA
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2
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Ahmed J, Das B, Shin S, Chen A. Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies. Cancers (Basel) 2023; 15:5841. [PMID: 38136385 PMCID: PMC10741991 DOI: 10.3390/cancers15245841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.
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Affiliation(s)
- Jibran Ahmed
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Biswajit Das
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Sarah Shin
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Alice Chen
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
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3
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Han Y, Wang J, Sun T, Ouyang Q, Li J, Yuan J, Xu B. Predictive biomarkers of response and survival following immunotherapy with a PD-L1 inhibitor benmelstobart (TQB2450) and antiangiogenic therapy with a VEGFR inhibitor anlotinib for pretreated advanced triple negative breast cancer. Signal Transduct Target Ther 2023; 8:429. [PMID: 37973901 PMCID: PMC10654734 DOI: 10.1038/s41392-023-01672-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/17/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023] Open
Abstract
In our phase Ib trial (ClinialTrials.gov Identifier: NCT03855358), benmelstobart (TQB2450), a novel humanized IgG1 antibody against PD-L1, plus antiangiogenic multikinase inhibitor, anlotinib, demonstrated promising antitumor activities in pretreated triple negative breast cancer (TNBC) patients. We conducted explorative analyses of genomic biomarkers to explore the associations with treatment response and survival outcomes. Targeted next generation sequencing (NGS) was undertaken toward circulating tumor DNA (ctDNA) collected from peripheral blood samples prior to the start of treatment and after disease progression. A total of 31 patients received targeted NGS and functional driver mutations in 29 patients were analyzed. The most frequent mutations were TP53 (72%), MLL3 (28%), and PIK3CA (17%). At a blood-based tumor mutational burden (bTMB) cutoff of 6.7 mutations per megabase, patients with low bTMB showed better response to anlotinib plus TQB2450 (50% vs. 7%, P = 0.015) and gained greater PFS benefits (7.3 vs. 4.1 months, P = 0.012) than those with high bTMB. At a maximum somatic allele frequency (MSAF) cutoff of 10%, a low MSAF indicated a better objective response (43% vs. 20%) as well as a significantly longer median PFS (7.9 vs. 2.7 months, P < 0.001). Patients with both low MSAF and low bTMB showed a notably better objective response to anlotinib plus TQB2450 (70% vs. 11%, P < 0.001) and a significantly longer median PFS (11.0 vs. 2.9 months, P < 0.001) than patients with other scenarios. Our findings support future studes and validation of MSAF and the combined bTMB-MSAF classification as predictive biomarkers of immune checkpoint inhibitor-based regimens in advanced TNBC patients.
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Affiliation(s)
- Yiqun Han
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiayu Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Tao Sun
- Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, 110042, China
| | | | - Jianwen Li
- Geneplus-Shenzhen, Shenzhen, 518118, China
| | - Jie Yuan
- Geneplus-Shenzhen, Shenzhen, 518118, China
| | - Binghe Xu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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O’Sullivan B, Seoighe C. Comprehensive and realistic simulation of tumour genomic sequencing data. NAR Cancer 2023; 5:zcad051. [PMID: 37746635 PMCID: PMC10516706 DOI: 10.1093/narcan/zcad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/25/2023] [Accepted: 09/08/2023] [Indexed: 09/26/2023] Open
Abstract
Accurate identification of somatic mutations and allele frequencies in cancer has critical research and clinical applications. Several computational tools have been developed for this purpose but, in the absence of comprehensive 'ground truth' data, assessing the accuracy of these methods is challenging. We created a computational framework to simulate tumour and matched normal sequencing data for which the source of all loci that contain non-reference bases is known, based on a phased, personalized genome. Unlike existing methods, we account for sampling errors inherent in the sequencing process. Using this framework, we assess accuracy and biases in inferred mutations and their frequencies in an established somatic mutation calling pipeline. We demonstrate bias in existing methods of mutant allele frequency estimation and show, for the first time, the observed mutation frequency spectrum corresponding to a theoretical model of tumour evolution. We highlight the impact of quality filters on detection sensitivity of clinically actionable variants and provide definitive assessment of false positive and false negative mutation calls. Our simulation framework provides an improved means to assess the accuracy of somatic mutation calling pipelines and a detailed picture of the effects of technical parameters and experimental factors on somatic mutation calling in cancer samples.
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Affiliation(s)
- Brian O’Sullivan
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway H91 TK33, Ireland
| | - Cathal Seoighe
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway H91 TK33, Ireland
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Marcos Rubio A, Everaert C, Van Damme E, De Preter K, Vermaelen K. Circulating immune cell dynamics as outcome predictors for immunotherapy in non-small cell lung cancer. J Immunother Cancer 2023; 11:e007023. [PMID: 37536935 PMCID: PMC10401220 DOI: 10.1136/jitc-2023-007023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2023] [Indexed: 08/05/2023] Open
Abstract
The use of immune checkpoint inhibitors (ICIs) continues to transform the therapeutic landscape of non-small cell lung cancer (NSCLC), with these drugs now being evaluated at every stage of the disease. In contrast to these advances, little progress has been made with respect to reliable predictive biomarkers that can inform clinicians on therapeutic efficacy. All current biomarkers for outcome prediction, including PD-L1, tumor mutational burden or complex immune gene expression signatures, require access to tumor tissue. Besides the invasive nature of the sampling procedure, other disadvantages of tumor tissue biopsies are the inability to capture the complete spatial heterogeneity of the tumor and the difficulty to perform longitudinal follow-up on treatment. A concept emerges in which systemic immune events developing at a distance from the tumor reflect local response or resistance to immunotherapy. The importance of this cancer 'macroenvironment', which can be deciphered by comprehensive analysis of peripheral blood immune cell subsets, has been demonstrated in several cutting-edge preclinical reports, and is corroborated by intriguing data emerging from ICI-treated patients. In this review, we will provide the biological rationale underlying the potential of blood immune cell-based biomarkers in guiding treatment decision in immunotherapy-eligible NSCLC patients. Finally, we will describe new techniques that will facilitate the discovery of more immune cell subpopulations with potential to become predictive biomarkers, and reflect on ways and the remaining challenges to bring this type of analysis to the routine clinical care in the near future.
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Affiliation(s)
- Alvaro Marcos Rubio
- VIB UGent Center for Medical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
| | - Celine Everaert
- VIB UGent Center for Medical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
| | - Eufra Van Damme
- VIB UGent Center for Medical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
| | - Katleen De Preter
- VIB UGent Center for Medical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
| | - Karim Vermaelen
- Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
- Tumor Immunology Laboratory, Department of Pulmonary Medicine, University Hospital Ghent, Ghent, Belgium
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Menon V, Brash DE. Next-generation sequencing methodologies to detect low-frequency mutations: "Catch me if you can". MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108471. [PMID: 37716438 PMCID: PMC10843083 DOI: 10.1016/j.mrrev.2023.108471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/18/2023]
Abstract
Mutations, the irreversible changes in an organism's DNA sequence, are present in tissues at a variant allele frequency (VAF) ranging from ∼10-8 per bp for a founder mutation to ∼10-3 for a histologically normal tissue sample containing several independent clones - compared to 1%- 50% for a heterozygous tumor mutation or a polymorphism. The rarity of these events poses a challenge for accurate clinical diagnosis and prognosis, toxicology, and discovering new disease etiologies. Standard Next-Generation Sequencing (NGS) technologies report VAFs as low as 0.5% per nt, but reliably observing rarer precursor events requires additional sophistication to measure ultralow-frequency mutations. We detail the challenge; define terms used to characterize the results, which vary between laboratories and sometimes conflict between biologists and bioinformaticists; and describe recent innovations to improve standard NGS methodologies including: single-strand consensus sequence methods such as Safe-SeqS and SiMSen-Seq; tandem-strand consensus sequence methods such as o2n-Seq and SMM-Seq; and ultrasensitive parent-strand consensus sequence methods such as DuplexSeq, PacBio HiFi, SinoDuplex, OPUSeq, EcoSeq, BotSeqS, Hawk-Seq, NanoSeq, SaferSeq, and CODEC. Practical applications are also noted. Several methods quantify VAF down to 10-5 at a nt and mutation frequency (MF) in a target region down to 10-7 per nt. By expanding to > 1 Mb of sites never observed twice, thus forgoing VAF, other methods quantify MF < 10-9 per nt or < 15 errors per haploid genome. Clonal expansion cannot be directly distinguished from independent mutations by sequencing, so it is essential for a paper to report whether its MF counted only different mutations - the minimum independent-mutation frequency MFminI - or all mutations observed including recurrences - the larger maximum independent-mutation frequency MFmaxI which may reflect clonal expansion. Ultrasensitive methods reveal that, without their use, even mutations with VAF 0.5-1% are usually spurious.
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Affiliation(s)
- Vijay Menon
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06520-8040, USA.
| | - Douglas E Brash
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06520-8040, USA; Department of Dermatology, Yale School of Medicine, New Haven, CT 06520-8059, USA; Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520-8028, USA.
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Andersen LVB, Larsen MJ, Davies H, Degasperi A, Nielsen HR, Jensen LA, Kroeldrup L, Gerdes AM, Lænkholm AV, Kruse TA, Nik-Zainal S, Thomassen M. Non-BRCA1/BRCA2 high-risk familial breast cancers are not associated with a high prevalence of BRCAness. Breast Cancer Res 2023; 25:69. [PMID: 37316882 DOI: 10.1186/s13058-023-01655-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 05/09/2023] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Familial breast cancer is in most cases unexplained due to the lack of identifiable pathogenic variants in the BRCA1 and BRCA2 genes. The somatic mutational landscape and in particular the extent of BRCA-like tumour features (BRCAness) in these familial breast cancers where germline BRCA1 or BRCA2 mutations have not been identified is to a large extent unknown. METHODS We performed whole-genome sequencing on matched tumour and normal samples from high-risk non-BRCA1/BRCA2 breast cancer families to understand the germline and somatic mutational landscape and mutational signatures. We measured BRCAness using HRDetect. As a comparator, we also analysed samples from BRCA1 and BRCA2 germline mutation carriers. RESULTS We noted for non-BRCA1/BRCA2 tumours, only a small proportion displayed high HRDetect scores and were characterized by concomitant promoter hypermethylation or in one case a RAD51D splice variant previously reported as having unknown significance to potentially explain their BRCAness. Another small proportion showed no features of BRCAness but had mutationally active tumours. The remaining tumours lacked features of BRCAness and were mutationally quiescent. CONCLUSIONS A limited fraction of high-risk familial non-BRCA1/BRCA2 breast cancer patients is expected to benefit from treatment strategies against homologue repair deficient cancer cells.
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Affiliation(s)
- Lars V B Andersen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Martin J Larsen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Helen Davies
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Andrea Degasperi
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | | | - Louise A Jensen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lone Kroeldrup
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Anne-Marie Gerdes
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Anne-Vibeke Lænkholm
- Department of Surgical Pathology, Zealand University Hospital, 4000, Roskilde, Denmark
| | - Torben A Kruse
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Serena Nik-Zainal
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
- European Sperm Bank, Copenhagen, Denmark
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
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Duncavage EJ, Coleman JF, de Baca ME, Kadri S, Leon A, Routbort M, Roy S, Suarez CJ, Vanderbilt C, Zook JM. Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation: A Joint Report of the Association for Molecular Pathology, Association for Pathology Informatics, and College of American Pathologists. J Mol Diagn 2023; 25:3-16. [PMID: 36244574 DOI: 10.1016/j.jmoldx.2022.09.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented.
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Affiliation(s)
- Eric J Duncavage
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Joshua F Coleman
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Monica E de Baca
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Pacific Pathology Partners, Seattle, Washington
| | - Sabah Kadri
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Anne and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Annette Leon
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Color Health, Burlingame, California
| | - Mark Routbort
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas
| | - Somak Roy
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio
| | - Carlos J Suarez
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Palo Alto, California
| | - Chad Vanderbilt
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Justin M Zook
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Biomarker and Genomic Sciences Group, National Institute of Standards and Technology, Gaithersburg, Maryland
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