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Bhattarai P, Gunasekaran TI, Belloy ME, Reyes-Dumeyer D, Jülich D, Tayran H, Yilmaz E, Flaherty D, Turgutalp B, Sukumar G, Alba C, McGrath EM, Hupalo DN, Bacikova D, Le Guen Y, Lantigua R, Medrano M, Rivera D, Recio P, Nuriel T, Ertekin-Taner N, Teich AF, Dickson DW, Holley S, Greicius M, Dalgard CL, Zody M, Mayeux R, Kizil C, Vardarajan BN. Rare genetic variation in fibronectin 1 (FN1) protects against APOEε4 in Alzheimer's disease. Acta Neuropathol 2024; 147:70. [PMID: 38598053 PMCID: PMC11006751 DOI: 10.1007/s00401-024-02721-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 04/11/2024]
Abstract
The risk of developing Alzheimer's disease (AD) significantly increases in individuals carrying the APOEε4 allele. Elderly cognitively healthy individuals with APOEε4 also exist, suggesting the presence of cellular mechanisms that counteract the pathological effects of APOEε4; however, these mechanisms are unknown. We hypothesized that APOEε4 carriers without dementia might carry genetic variations that could protect them from developing APOEε4-mediated AD pathology. To test this, we leveraged whole-genome sequencing (WGS) data in the National Institute on Aging Alzheimer's Disease Family Based Study (NIA-AD FBS), Washington Heights/Inwood Columbia Aging Project (WHICAP), and Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA) cohorts and identified potentially protective variants segregating exclusively among unaffected APOEε4 carriers. In homozygous unaffected carriers above 70 years old, we identified 510 rare coding variants. Pathway analysis of the genes harboring these variants showed significant enrichment in extracellular matrix (ECM)-related processes, suggesting protective effects of functional modifications in ECM proteins. We prioritized two genes that were highly represented in the ECM-related gene ontology terms, (FN1) and collagen type VI alpha 2 chain (COL6A2) and are known to be expressed at the blood-brain barrier (BBB), for postmortem validation and in vivo functional studies. An independent analysis in a large cohort of 7185 APOEε4 homozygous carriers found that rs140926439 variant in FN1 was protective of AD (OR = 0.29; 95% CI [0.11, 0.78], P = 0.014) and delayed age at onset of disease by 3.37 years (95% CI [0.42, 6.32], P = 0.025). The FN1 and COL6A2 protein levels were increased at the BBB in APOEε4 carriers with AD. Brain expression of cognitively unaffected homozygous APOEε4 carriers had significantly lower FN1 deposition and less reactive gliosis compared to homozygous APOEε4 carriers with AD, suggesting that FN1 might be a downstream driver of APOEε4-mediated AD-related pathology and cognitive decline. To validate our findings, we used zebrafish models with loss-of-function (LOF) mutations in fn1b-the ortholog for human FN1. We found that fibronectin LOF reduced gliosis, enhanced gliovascular remodeling, and potentiated the microglial response, suggesting that pathological accumulation of FN1 could impair toxic protein clearance, which is ameliorated with FN1 LOF. Our study suggests that vascular deposition of FN1 is related to the pathogenicity of APOEε4, and LOF variants in FN1 may reduce APOEε4-related AD risk, providing novel clues to potential therapeutic interventions targeting the ECM to mitigate AD risk.
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Affiliation(s)
- Prabesh Bhattarai
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Tamil Iniyan Gunasekaran
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Michael E Belloy
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Dolly Reyes-Dumeyer
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Dörthe Jülich
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA
| | - Hüseyin Tayran
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Elanur Yilmaz
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Delaney Flaherty
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Bengisu Turgutalp
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - Gauthaman Sukumar
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Camille Alba
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Elisa Martinez McGrath
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Daniel N Hupalo
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Dagmar Bacikova
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Yann Le Guen
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Rafael Lantigua
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
- Department of Medicine, College of Physicians and Surgeons, Columbia University New York, New York, USA
| | - Martin Medrano
- School of Medicine, Pontificia Universidad Catolica Madre y Maestra, Santiago, Dominican Republic
| | - Diones Rivera
- Department of Neurology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
- School of Medicine, Universidad Pedro Henriquez Urena (UNPHU), Santo Domingo, Dominican Republic
| | - Patricia Recio
- Department of Neurology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Tal Nuriel
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Andrew F Teich
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, 32224, USA
| | - Scott Holley
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06520, USA
| | - Michael Greicius
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Clifton L Dalgard
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA
- The American Genome Center, Center for Military Precision Health, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Michael Zody
- New York Genome Center, New York, NY, 10013, USA
| | - Richard Mayeux
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
- Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, 1051 Riverside Drive, New York, NY, 10032, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St., New York, NY, 10032, USA
| | - Caghan Kizil
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA.
- Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
| | - Badri N Vardarajan
- Department of Neurology, Columbia University Irving Medical Center, Columbia University New York, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, Columbia University, New York, NY, USA.
- Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
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2
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Yamada M, Keller RR, Gutierrez RL, Cameron D, Suzuki H, Sanghrajka R, Vaynshteyn J, Gerwin J, Maura F, Hooper W, Shah M, Robine N, Demarest P, Bayin NS, Zapater LJ, Reed C, Hébert S, Masilionis I, Chaligne R, Socci ND, Taylor MD, Kleinman CL, Joyner AL, Raju GP, Kentsis A. Childhood cancer mutagenesis caused by transposase-derived PGBD5. Sci Adv 2024; 10:eadn4649. [PMID: 38517960 PMCID: PMC10959420 DOI: 10.1126/sciadv.adn4649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024]
Abstract
Genomic rearrangements are a hallmark of most childhood tumors, including medulloblastoma, one of the most common brain tumors in children, but their causes remain largely unknown. Here, we show that PiggyBac transposable element derived 5 (Pgbd5) promotes tumor development in multiple developmentally accurate mouse models of Sonic Hedgehog (SHH) medulloblastoma. Most Pgbd5-deficient mice do not develop tumors, while maintaining normal cerebellar development. Ectopic activation of SHH signaling is sufficient to enforce cerebellar granule cell progenitor-like cell states, which exhibit Pgbd5-dependent expression of distinct DNA repair and neurodevelopmental factors. Mouse medulloblastomas expressing Pgbd5 have increased numbers of somatic structural DNA rearrangements, some of which carry PGBD5-specific sequences at their breakpoints. Similar sequence breakpoints recurrently affect somatic DNA rearrangements of known tumor suppressors and oncogenes in medulloblastomas in 329 children. This identifies PGBD5 as a medulloblastoma mutator and provides a genetic mechanism for the generation of oncogenic DNA rearrangements in childhood cancer.
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Affiliation(s)
- Makiko Yamada
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | - Ross R. Keller
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | | | - Daniel Cameron
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | - Hiromichi Suzuki
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, Japan
| | - Reeti Sanghrajka
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
| | - Jake Vaynshteyn
- Departments of Neurology and Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey Gerwin
- Departments of Neurology and Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesco Maura
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - William Hooper
- Computational Biology, New York Genome Center, New York, NY, USA
| | - Minita Shah
- Computational Biology, New York Genome Center, New York, NY, USA
| | - Nicolas Robine
- Computational Biology, New York Genome Center, New York, NY, USA
| | - Phillip Demarest
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | - N. Sumru Bayin
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
- Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, UK
| | - Luz Jubierre Zapater
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | - Casie Reed
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
| | - Steven Hébert
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Ignas Masilionis
- Single-Cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Single-Cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas D. Socci
- Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael D. Taylor
- Department of Pediatrics—Hematology/Oncology and Neurosurgery, Baylor College of Medicine, Houston, TX, USA
- Hematology-Oncology Section, Texas Children’s Cancer Center, Houston, TX, USA
- The Arthur and Sonia Labatt Brain Tumour Research Centre and the Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Claudia L. Kleinman
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Alexandra L. Joyner
- Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA
- Biochemistry, Cell and Molecular Biology Program and Neuroscience Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA
| | - G. Praveen Raju
- Departments of Neurology and Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex Kentsis
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA
- Departments of Pediatrics, Pharmacology, and Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA
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Simpson JT. Detecting Somatic Mutations Without Matched Normal Samples Using Long Reads. bioRxiv 2024:2024.02.26.582089. [PMID: 38464143 PMCID: PMC10925087 DOI: 10.1101/2024.02.26.582089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
DNA sequencing of tumours to identify somatic mutations has become a critical tool to guide the type of treatment given to cancer patients. The gold standard for mutation calling is comparing sequencing data from the tumour to a matched normal sample to avoid mis-classifying inherited SNPs as mutations. This procedure works extremely well, but in certain situations only a tumour sample is available. While approaches have been developed to find mutations without a matched normal, they have limited accuracy or require specific types of input data (e.g. ultra-deep sequencing). Here we explore the application of single molecule long read sequencing to calling somatic mutations without matched normal samples. We develop a simple theoretical framework to show how haplotype phasing is an important source of information for determining whether a variant is a somatic mutation. We then use simulations to assess the range of experimental parameters (tumour purity, sequencing depth) where this approach is effective. These ideas are developed into a prototype somatic mutation caller, smrest, and its use is demonstrated on two highly mutated cancer cell lines. Finally, we argue that this approach has potential to measure clinically important biomarkers that are based on the genome-wide distribution of mutations: tumour mutation burden and mutation signatures.
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Affiliation(s)
- Jared T. Simpson
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
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4
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Bhattarai P, Gunasekaran TI, Reyes-Dumeyer D, Jülich D, Tayran H, Yilmaz E, Flaherty D, Lantigua R, Medrano M, Rivera D, Recio P, Ertekin-Taner N, Teich AF, Dickson DW, Holley S, Mayeux R, Kizil C, Vardarajan BN. Rare genetic variation in Fibronectin 1 ( FN1 ) protects against APOEe4 in Alzheimer's disease. bioRxiv 2024:2024.01.02.573895. [PMID: 38260431 PMCID: PMC10802344 DOI: 10.1101/2024.01.02.573895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The risk of developing Alzheimer's disease (AD) significantly increases in individuals carrying the APOEε4 allele. Elderly cognitively healthy individuals with APOEε4 also exist, suggesting the presence of cellular mechanisms that counteract the pathological effects of APOEε4 ; however, these mechanisms are unknown. We hypothesized that APOEε4 carriers without dementia might carry genetic variations that could protect them from developing APOEε4- mediated AD pathology. To test this, we leveraged whole genome sequencing (WGS) data in National Institute on Aging Alzheimer's Disease Family Based Study (NIA-AD FBS), Washington Heights/Inwood Columbia Aging Project (WHICAP), and Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA) cohorts and identified potentially protective variants segregating exclusively among unaffected APOEε4 carriers. In homozygous unaffected carriers above 70 years old, we identified 510 rare coding variants. Pathway analysis of the genes harboring these variants showed significant enrichment in extracellular matrix (ECM)-related processes, suggesting protective effects of functional modifications in ECM proteins. We prioritized two genes that were highly represented in the ECM-related gene ontology terms, (FN1) and collagen type VI alpha 2 chain ( COL6A2 ) and are known to be expressed at the blood-brain barrier (BBB), for postmortem validation and in vivo functional studies. The FN1 and COL6A2 protein levels were increased at the BBB in APOEε4 carriers with AD. Brain expression of cognitively unaffected homozygous APOEε4 carriers had significantly lower FN1 deposition and less reactive gliosis compared to homozygous APOEε4 carriers with AD, suggesting that FN1 might be a downstream driver of APOEε4 -mediated AD-related pathology and cognitive decline. To validate our findings, we used zebrafish models with loss-of-function (LOF) mutations in fn1b - the ortholog for human FN1 . We found that fibronectin LOF reduced gliosis, enhanced gliovascular remodeling and potentiated the microglial response, suggesting that pathological accumulation of FN1 could impair toxic protein clearance, which is ameliorated with FN1 LOF. Our study suggests vascular deposition of FN1 is related to the pathogenicity of APOEε4 , LOF variants in FN1 may reduce APOEε4 -related AD risk, providing novel clues to potential therapeutic interventions targeting the ECM to mitigate AD risk.
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5
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Gunasekaran TI, Reyes-Dumeyer D, Faber KM, Goate A, Boeve B, Cruchaga C, Pericak-Vance M, Haines JL, Rosenberg R, Tsuang D, Mejia DR, Medrano M, Lantigua RA, Sweet RA, Bennett DA, Wilson RS, Foroud T, Vardarajan BN, Mayeux R. Identification of Rare Damaging Missense and Loss of Function Variants in GWAS Loci Using Genome Sequencing Data from Two Cohorts of Familial Late-Onset Alzheimer's Disease. medRxiv 2023:2023.12.18.23300145. [PMID: 38196599 PMCID: PMC10775337 DOI: 10.1101/2023.12.18.23300145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
PURPOSE Few rare pathogenic variants have been identified in the 70+ genetic loci from genome wide association studies of late-onset Alzheimer's disease (AD), limiting research on underlying mechanisms, risk assessment, and genetic counseling. METHODS Using genome sequencing data from 197 families in The National Institute on Aging Alzheimer's Disease Family Based Study (AD-FBS), and 214 families in The Estudio Familiar de la Influencia Genética en Alzheimer (EFIGA), we characterized rare coding variants predicted to highly damaging missense or loss of function variants (LoF) within known GWAS loci. RESULTS Eight coding and one LoF variant segregated in 10 (5.1%) AD-FBS families and 16 coding and two LoF variants segregated in 18 (8.4%) EFIGA families. ABCA7 and AKAP9 contained the most damaging variants. In 51 (25.9%) of the AD-FBS and in 26 (12.1%) of the EFIGA families, APOE-ε4 was the only variant segregating with familial AD (fAD). Neither APOE-ε4 nor missense or LoF variants were found in 44.1% of the AD-FBS and 62.1% of the EFIGA families. CONCLUSIONS Although rare variants were found in both family groups, many families had no gene variant segregating within the family, indicating that the genetic basis for AD has yet to be fully defined.
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Khani F, Hooper WF, Wang X, Chu TR, Shah M, Winterkorn L, Sigouros M, Conteduca V, Pisapia D, Wobker S, Walker S, Graff JN, Robinson B, Mosquera JM, Sboner A, Elemento O, Robine N, Beltran H. Evolution of structural rearrangements in prostate cancer intracranial metastases. NPJ Precis Oncol 2023; 7:91. [PMID: 37704749 PMCID: PMC10499931 DOI: 10.1038/s41698-023-00435-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/08/2023] [Indexed: 09/15/2023] Open
Abstract
Intracranial metastases in prostate cancer are uncommon but clinically aggressive. A detailed molecular characterization of prostate cancer intracranial metastases would improve our understanding of their pathogenesis and the search for new treatment strategies. We evaluated the clinical and molecular characteristics of 36 patients with metastatic prostate cancer to either the dura or brain parenchyma. We performed whole genome sequencing (WGS) of 10 intracranial prostate cancer metastases, as well as WGS of primary prostate tumors from men who later developed metastatic disease (n = 6) and nonbrain prostate cancer metastases (n = 36). This first whole genome sequencing study of prostate intracranial metastases led to several new insights. First, there was a higher diversity of complex structural alterations in prostate cancer intracranial metastases compared to primary tumor tissues. Chromothripsis and chromoplexy events seemed to dominate, yet there were few enrichments of specific categories of structural variants compared with non-brain metastases. Second, aberrations involving the AR gene, including AR enhancer gain were observed in 7/10 (70%) of intracranial metastases, as well as recurrent loss of function aberrations involving TP53 in 8/10 (80%), RB1 in 2/10 (20%), BRCA2 in 2/10 (20%), and activation of the PI3K/AKT/PTEN pathway in 8/10 (80%). These alterations were frequently present in tumor tissues from other sites of disease obtained concurrently or sequentially from the same individuals. Third, clonality analysis points to genomic factors and evolutionary bottlenecks that contribute to metastatic spread in patients with prostate cancer. These results describe the aggressive molecular features underlying intracranial metastasis that may inform future diagnostic and treatment approaches.
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Affiliation(s)
- Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Xiaofei Wang
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | | | | | - Michael Sigouros
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Vincenza Conteduca
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medical and Surgical Sciences, Unit of Medical Oncology and Biomolecular Therapy, University of Foggia, Policlinico Riuniti, Foggia, Italy
| | - David Pisapia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sara Wobker
- Department of Pathology and Laboratory Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Sydney Walker
- Department of Medical Oncology, Oregon Health Sciences University, Portland, OR, USA
| | - Julie N Graff
- Department of Medical Oncology, Oregon Health Sciences University, Portland, OR, USA
| | - Brian Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Juan Miguel Mosquera
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Andrea Sboner
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Olivier Elemento
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | | | - Himisha Beltran
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
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7
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Shiraishi Y, Koya J, Chiba K, Okada A, Arai Y, Saito Y, Shibata T, Kataoka K. Precise characterization of somatic complex structural variations from tumor/control paired long-read sequencing data with nanomonsv. Nucleic Acids Res 2023; 51:e74. [PMID: 37336583 PMCID: PMC10415145 DOI: 10.1093/nar/gkad526] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/23/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023] Open
Abstract
We present our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Using tumor/control paired long-read sequencing data from three cancer and their matched lymphoblastoid lines, we demonstrate that Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. In addition, we have developed a workflow to classify mobile element insertions while elucidating their in-depth properties, such as 5' truncations, internal inversions, as well as source sites for 3' transductions. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to a better understanding of mutational processes and functional consequences of somatic SVs.
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Affiliation(s)
- Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Junji Koya
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Kenichi Chiba
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ai Okada
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Yasuhito Arai
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Yuki Saito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
- Laboratory of Molecular Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Keisuke Kataoka
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Hematology, Keio University School of Medicine, Tokyo, Japan
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8
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Das S, Biswas NK, Basu A. Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data. Nucleic Acids Res 2023; 51:e75. [PMID: 37378434 PMCID: PMC10415152 DOI: 10.1093/nar/gkad539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/16/2023] [Accepted: 06/27/2023] [Indexed: 06/29/2023] Open
Abstract
High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.
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Affiliation(s)
- Subrata Das
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
| | - Nidhan K Biswas
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
| | - Analabha Basu
- National Institute of Biomedical Genomics, Kalyani, 741251, West Bengal, India
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9
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Shapiro JA, Gaonkar KS, Spielman SJ, Savonen CL, Bethell CJ, Jin R, Rathi KS, Zhu Y, Egolf LE, Farrow BK, Miller DP, Yang Y, Koganti T, Noureen N, Koptyra MP, Duong N, Santi M, Kim J, Robins S, Storm PB, Mack SC, Lilly JV, Xie HM, Jain P, Raman P, Rood BR, Lulla RR, Nazarian J, Kraya AA, Vaksman Z, Heath AP, Kline C, Scolaro L, Viaene AN, Huang X, Way GP, Foltz SM, Zhang B, Poetsch AR, Mueller S, Ennis BM, Prados M, Diskin SJ, Zheng S, Guo Y, Kannan S, Waanders AJ, Margol AS, Kim MC, Hanson D, Van Kuren N, Wong J, Kaufman RS, Coleman N, Blackden C, Cole KA, Mason JL, Madsen PJ, Koschmann CJ, Stewart DR, Wafula E, Brown MA, Resnick AC, Greene CS, Rokita JL, Taroni JN. OpenPBTA: The Open Pediatric Brain Tumor Atlas. Cell Genom 2023; 3:100340. [PMID: 37492101 PMCID: PMC10363844 DOI: 10.1016/j.xgen.2023.100340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/28/2023] [Accepted: 05/04/2023] [Indexed: 07/27/2023]
Abstract
Pediatric brain and spinal cancers are collectively the leading disease-related cause of death in children; thus, we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Children's Brain Tumor Network (CBTN) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to establish OpenPBTA, an open collaborative project with over 40 scalable analysis modules that genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas and TP53 loss as a significant marker for poor overall survival in ependymomas and H3 K28-mutant diffuse midline gliomas. Already being actively applied to other pediatric cancers and PNOC molecular tumor board decision-making, OpenPBTA is an invaluable resource to the pediatric oncology community.
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Affiliation(s)
- Joshua A. Shapiro
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
| | - Krutika S. Gaonkar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Stephanie J. Spielman
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
- Rowan University, Glassboro, NJ 08028, USA
| | - Candace L. Savonen
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
| | - Chante J. Bethell
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
| | - Run Jin
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Komal S. Rathi
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yuankun Zhu
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Laura E. Egolf
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Bailey K. Farrow
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Daniel P. Miller
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yang Yang
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA
| | - Tejaswi Koganti
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nighat Noureen
- Greehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA
| | - Mateusz P. Koptyra
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nhat Duong
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jung Kim
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
| | - Shannon Robins
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Phillip B. Storm
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Stephen C. Mack
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Jena V. Lilly
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Hongbo M. Xie
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Payal Jain
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Pichai Raman
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Brian R. Rood
- Children’s National Research Institute, Washington, DC 20012, USA
- George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
| | - Rishi R. Lulla
- Division of Hematology/Oncology, Hasbro Children’s Hospital, Providence, RI 02903, USA
- Department of Pediatrics, The Warren Alpert School of Brown University, Providence, RI 02912, USA
| | - Javad Nazarian
- Children’s National Research Institute, Washington, DC 20012, USA
- George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
- Department of Pediatrics, University of Zurich, Zurich, Switzerland
| | - Adam A. Kraya
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Zalman Vaksman
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Allison P. Heath
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Cassie Kline
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Laura Scolaro
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Angela N. Viaene
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Xiaoyan Huang
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Gregory P. Way
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Steven M. Foltz
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bo Zhang
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Anna R. Poetsch
- Biotechnology Center, Technical University Dresden, Dresden, Germany
- National Center for Tumor Diseases, Dresden, Germany
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, San Francisco, CA 94115, USA
| | - Brian M. Ennis
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michael Prados
- University of California, San Francisco, San Francisco, CA 94115, USA
| | - Sharon J. Diskin
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Siyuan Zheng
- Greehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA
| | - Yiran Guo
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Shrivats Kannan
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Angela J. Waanders
- Division of Hematology, Oncology, Neuro-Oncology, and Stem Cell Transplant, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ashley S. Margol
- Division of Hematology and Oncology, Children’s Hospital of Los Angeles, Los Angeles, CA 90027, USA
- Department of Pediatrics, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - Meen Chul Kim
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Derek Hanson
- Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA
- Hackensack University Medical Center, Hackensack, NJ 07601, USA
| | - Nicholas Van Kuren
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jessica Wong
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Rebecca S. Kaufman
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Noel Coleman
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Christopher Blackden
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kristina A. Cole
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer L. Mason
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Peter J. Madsen
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Carl J. Koschmann
- Department of Pediatrics, University of Michigan Health, Ann Arbor, MI 48105, USA
- Pediatric Hematology Oncology, Mott Children’s Hospital, Ann Arbor, MI 48109, USA
| | - Douglas R. Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
| | - Eric Wafula
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Miguel A. Brown
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Adam C. Resnick
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Casey S. Greene
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jo Lynne Rokita
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jaclyn N. Taroni
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
| | - Children’s Brain Tumor Network
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Rowan University, Glassboro, NJ 08028, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA
- Greehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
- Children’s National Research Institute, Washington, DC 20012, USA
- George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
- Division of Hematology/Oncology, Hasbro Children’s Hospital, Providence, RI 02903, USA
- Department of Pediatrics, The Warren Alpert School of Brown University, Providence, RI 02912, USA
- Department of Pediatrics, University of Zurich, Zurich, Switzerland
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Biotechnology Center, Technical University Dresden, Dresden, Germany
- National Center for Tumor Diseases, Dresden, Germany
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, San Francisco, CA 94115, USA
- University of California, San Francisco, San Francisco, CA 94115, USA
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Hematology, Oncology, Neuro-Oncology, and Stem Cell Transplant, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Hematology and Oncology, Children’s Hospital of Los Angeles, Los Angeles, CA 90027, USA
- Department of Pediatrics, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
- Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA
- Hackensack University Medical Center, Hackensack, NJ 07601, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Michigan Health, Ann Arbor, MI 48105, USA
- Pediatric Hematology Oncology, Mott Children’s Hospital, Ann Arbor, MI 48109, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pacific Pediatric Neuro-Oncology Consortium
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Bioinformatics and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Rowan University, Glassboro, NJ 08028, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA
- Greehey Children’s Cancer Research Institute, UT Health San Antonio, San Antonio, TX 78229, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
- Children’s National Research Institute, Washington, DC 20012, USA
- George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
- Division of Hematology/Oncology, Hasbro Children’s Hospital, Providence, RI 02903, USA
- Department of Pediatrics, The Warren Alpert School of Brown University, Providence, RI 02912, USA
- Department of Pediatrics, University of Zurich, Zurich, Switzerland
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Biotechnology Center, Technical University Dresden, Dresden, Germany
- National Center for Tumor Diseases, Dresden, Germany
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, San Francisco, CA 94115, USA
- University of California, San Francisco, San Francisco, CA 94115, USA
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Hematology, Oncology, Neuro-Oncology, and Stem Cell Transplant, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Hematology and Oncology, Children’s Hospital of Los Angeles, Los Angeles, CA 90027, USA
- Department of Pediatrics, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
- Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA
- Hackensack University Medical Center, Hackensack, NJ 07601, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Michigan Health, Ann Arbor, MI 48105, USA
- Pediatric Hematology Oncology, Mott Children’s Hospital, Ann Arbor, MI 48109, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Vardarajan BN, Reyes‐Dumeyer D, Piriz AL, Lantigua RA, Medrano M, Rivera D, Jiménez‐Velázquez IZ, Martin E, Pericak‐Vance MA, Bush W, Farrer L, Haines JL, Wang L, Leung YY, Schellenberg G, Kukull W, De Jager P, Bennett DA, Schneider JA, Mayeux R. Progranulin mutations in clinical and neuropathological Alzheimer's disease. Alzheimers Dement 2022; 18:2458-2467. [PMID: 35258170 PMCID: PMC9360185 DOI: 10.1002/alz.12567] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 09/07/2021] [Accepted: 12/10/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Progranulin (GRN) mutations occur in frontotemporal lobar degeneration (FTLD) and in Alzheimer's disease (AD), often with TDP-43 pathology. METHODS We determined the frequency of rs5848 and rare, pathogenic GRN mutations in two autopsy and one family cohort. We compared Braak stage, β-amyloid load, hyperphosphorylated tau (PHFtau) tangle density and TDP-43 pathology in GRN carriers and non-carriers. RESULTS Pathogenic GRN mutations were more frequent in all cohorts compared to the Genome Aggregation Database (gnomAD), but there was no evidence for association with AD. Pathogenic GRN carriers had significantly higher PHFtau tangle density adjusting for age, sex and APOE ε4 genotype. AD patients with rs5848 had higher frequencies of hippocampal sclerosis and TDP-43 deposits. Twenty-two rare, pathogenic GRN variants were observed in the family cohort. DISCUSSION GRN mutations in clinical and neuropathological AD increase the burden of tau-related brain pathology but show no specific association with β-amyloid load or AD.
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Affiliation(s)
- Badri N. Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- The Gertrude H. Sergievsky CenterCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia University and the New York Presbyterian HospitalNew YorkNew YorkUSA
| | - Dolly Reyes‐Dumeyer
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- The Gertrude H. Sergievsky CenterCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia University and the New York Presbyterian HospitalNew YorkNew YorkUSA
| | - Angel L. Piriz
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- The Gertrude H. Sergievsky CenterCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Rafael A. Lantigua
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of MedicineCollege of Physicians and SurgeonsColumbia University, and the New York Presbyterian HospitalNew YorkNew YorkUSA
| | - Martin Medrano
- School of MedicinePontificia Universidad Catolica Madre y Maestra (PUCMM)SantiagoDominican Republic
| | - Diones Rivera
- Department of NeurologyCEDIMAT, Plaza de la SaludSanto DomingoDominican Republic
- School of MedicineUniversidad Pedro Henriquez Urena (UNPHU)Santo DomingoDominican Republic
| | | | - Eden Martin
- The John P. Hussman Institute for Human Genomicsand Dr. John T. Macdonald Foundation Department of Human GeneticsMiamiFloridaUSA
| | - Margaret A. Pericak‐Vance
- The John P. Hussman Institute for Human Genomicsand Dr. John T. Macdonald Foundation Department of Human GeneticsMiamiFloridaUSA
| | - William Bush
- Department of Biostatistics and EpidemiologyCase Western Reserve UniversityClevelandOhioUSA
| | - Lindsay Farrer
- Boston University School of MedicineBostonMassachusettsUSA
| | - Jonathan L. Haines
- Department of Biostatistics and EpidemiologyCase Western Reserve UniversityClevelandOhioUSA
| | - Li‐San Wang
- School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Yuk Yee Leung
- School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | | | - Walter Kukull
- Department of EpidemiologySchool of Public HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Philip De Jager
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia University and the New York Presbyterian HospitalNew YorkNew YorkUSA
| | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Julie A. Schneider
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | | | - Richard Mayeux
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- The Gertrude H. Sergievsky CenterCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia University and the New York Presbyterian HospitalNew YorkNew YorkUSA
- Department of EpidemiologySchool of Public HealthUniversity of WashingtonSeattleWashingtonUSA
- Department of PsychiatryCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
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11
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Cuppen E, Elemento O, Rosenquist R, Nikic S, IJzerman M, Zaleski ID, Frederix G, Levin LÅ, Mullighan CG, Buettner R, Pugh TJ, Grimmond S, Caldas C, Andre F, Custers I, Campo E, van Snellenberg H, Schuh A, Nakagawa H, von Kalle C, Haferlach T, Fröhling S, Jobanputra V. Implementation of Whole-Genome and Transcriptome Sequencing Into Clinical Cancer Care. JCO Precis Oncol 2022; 6:e2200245. [PMID: 36480778 PMCID: PMC10166391 DOI: 10.1200/po.22.00245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/30/2022] [Accepted: 09/21/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The combination of whole-genome and transcriptome sequencing (WGTS) is expected to transform diagnosis and treatment for patients with cancer. WGTS is a comprehensive precision diagnostic test that is starting to replace the standard of care for oncology molecular testing in health care systems around the world; however, the implementation and widescale adoption of this best-in-class testing is lacking. METHODS Here, we address the barriers in integrating WGTS for cancer diagnostics and treatment selection and answer questions regarding utility in different cancer types, cost-effectiveness and affordability, and other practical considerations for WGTS implementation. RESULTS We review the current studies implementing WGTS in health care systems and provide a synopsis of the clinical evidence and insights into practical considerations for WGTS implementation. We reflect on regulatory, costs, reimbursement, and incidental findings aspects of this test. CONCLUSION WGTS is an appropriate comprehensive clinical test for many tumor types and can replace multiple, cascade testing approaches currently performed. Decreasing sequencing cost, increasing number of clinically relevant aberrations and discovery of more complex biomarkers of treatment response, should pave the way for health care systems and laboratories in implementing WGTS into clinical practice, to transform diagnosis and treatment for patients with cancer.
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Affiliation(s)
- Edwin Cuppen
- Hartwig Medical Foundation, Amsterdam, the Netherlands
- Center for Molecular Medicine and Oncode Institute, University Medical Center, Utrecht, the Netherlands
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Solna, Sweden
| | - Svetlana Nikic
- Illumina Productos de España, S.L.U., Plaza Pablo Ruiz Picasso, Madrid, Spain
| | - Maarten IJzerman
- Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, the Netherlands
- Centre for Cancer Research, University of Melbourne, Melbourne, Australia
| | - Isabelle Durand Zaleski
- Université de Paris, CRESS, INSERM, INRA, URCEco, AP-HP, Hôpital de l'Hôtel Dieu, Paris, France
| | - Geert Frederix
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, the Netherlands
| | - Lars-Åke Levin
- Department of Health, Medicine and Caring Sciences (HMV), Linköping University, Linköping, Sweden
| | | | | | - Trevor J. Pugh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Sean Grimmond
- Centre for Cancer Research, University of Melbourne, Melbourne, Australia
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute and Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | | | | | - Elias Campo
- Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red, Cáncer (CIBERONC), Madrid, Spain
- Hematopathology Unit, Hospital Clínic of Barcelona, Barcelona, Spain
- University of Barcelona, Barcelona, Spain
| | | | - Anna Schuh
- University of Oxford, Oxford, United Kingdom
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Christof von Kalle
- Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Clinical Study Center, Berlin, Germany
| | | | - Stefan Fröhling
- Division of Translational Medical Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Vaidehi Jobanputra
- New York Genome Center; Department of Pathology, Columbia University Irving Medical Center, New York, NY
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12
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Zheng T. TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion. Front Genet 2022; 13:981269. [DOI: 10.3389/fgene.2022.981269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/17/2022] [Indexed: 11/23/2022] Open
Abstract
Mutation detecting is a routine work for sequencing data analysis and the trading of existing tools often involves the combinations of signals on a set of overlapped sequencing reads. However, the subclonal mutations, which are reported to contribute to tumor recurrence and metastasis, are sometimes eliminated by existing signals. When the clonal proportion decreases, signals often present ambiguous, while complicated interactions among signals break the IID assumption for most of the machine learning models. Although the mutation callers could lower the thresholds, false positives are significantly introduced. The main aim here was to detect the subclonal mutations with high specificity from the scenario of ambiguous sample purities or clonal proportions. We proposed a novel machine learning approach for filtering false positive calls to accurately detect mutations with wide spectrum subclonal proportion. We have carried out a series of experiments on both simulated and real datasets, and compared to several state-of-art approaches, including freebayes, MuTect2, Sentieon and SiNVICT. The results demonstrated that the proposed method adapts well to different diluted sequencing signals and can significantly reduce the false positive when detecting subclonal mutations. The codes have been uploaded at https://github.com/TrinaZ/TL-fpFilter for academic usage only.
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13
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Muñoz-Barrera A, Rubio-Rodríguez LA, Díaz-de Usera A, Jáspez D, Lorenzo-Salazar JM, González-Montelongo R, García-Olivares V, Flores C. From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research. Life (Basel) 2022; 12:1939. [PMID: 36431075 PMCID: PMC9695713 DOI: 10.3390/life12111939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.
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Affiliation(s)
- Adrián Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Ana Díaz-de Usera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - David Jáspez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - José M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Rafaela González-Montelongo
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Víctor García-Olivares
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Fernando de Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain
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14
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Espejo Valle-Inclan J, Besselink NJ, de Bruijn E, Cameron DL, Ebler J, Kutzera J, van Lieshout S, Marschall T, Nelen M, Priestley P, Renkens I, Roemer MG, van Roosmalen MJ, Wenger AM, Ylstra B, Fijneman RJ, Kloosterman WP, Cuppen E. A multi-platform reference for somatic structural variation detection. Cell Genom 2022; 2:100139. [PMID: 36778136 PMCID: PMC9903816 DOI: 10.1016/j.xgen.2022.100139] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 05/06/2021] [Accepted: 05/06/2022] [Indexed: 10/18/2022]
Abstract
Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.
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Affiliation(s)
| | - Nicolle J.M. Besselink
- Center for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands
| | | | - Daniel L. Cameron
- Hartwig Medical Foundation, Amsterdam, the Netherlands,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Joachim Kutzera
- Center for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands
| | | | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Marcel Nelen
- Department of Human Genetics, Radboud UMC, Nijmegen, the Netherlands
| | | | - Ivo Renkens
- Center for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands
| | - Margaretha G.M. Roemer
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | | | | | - Bauke Ylstra
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Remond J.A. Fijneman
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Wigard P. Kloosterman
- Center for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands,Corresponding author
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, UMC Utrecht, Utrecht, the Netherlands,Hartwig Medical Foundation, Amsterdam, the Netherlands,Corresponding author
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15
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Ozerov M, Noreikiene K, Kahar S, Huss M, Huusko A, Kõiv T, Sepp M, López M, Gårdmark A, Gross R, Vasemägi A. Whole-genome sequencing illuminates multifaceted targets of selection to humic substances in Eurasian perch. Mol Ecol 2022; 31:2367-2383. [PMID: 35202502 PMCID: PMC9314028 DOI: 10.1111/mec.16409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 10/06/2021] [Revised: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 11/30/2022]
Abstract
Extreme environments are inhospitable to the majority of species, but some organisms are able to survive in such hostile conditions due to evolutionary adaptations. For example, modern bony fishes have colonized various aquatic environments, including perpetually dark, hypoxic, hypersaline and toxic habitats. Eurasian perch (Perca fluviatilis) is among the few fish species of northern latitudes that is able to live in very acidic humic lakes. Such lakes represent almost "nocturnal" environments; they contain high levels of dissolved organic matter, which in addition to creating a challenging visual environment, also affects a large number of other habitat parameters and biotic interactions. To reveal the genomic targets of humic-associated selection, we performed whole-genome sequencing of perch originating from 16 humic and 16 clear-water lakes in northern Europe. We identified over 800,000 SNPs, of which >10,000 were identified as potential candidates under selection (associated with >3,000 genes) using multiple outlier approaches. Our findings suggest that adaptation to the humic environment may involve hundreds of regions scattered across the genome. Putative signals of adaptation were detected in genes and gene families with diverse functions, including organism development and ion transportation. The observed excess of variants under selection in regulatory regions highlights the importance of adaptive evolution via regulatory elements, rather than via protein sequence modification. Our study demonstrates the power of whole-genome analysis to illuminate multifaceted nature of humic adaptation and provides the foundation for further investigation of causal mutations underlying phenotypic traits of ecological and evolutionary importance.
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Affiliation(s)
- Mikhail Ozerov
- Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, 17893, Drottningholm, Sweden.,Department of Biology, University of Turku, 20014, Turku, Finland.,Biodiversity Unit, University of Turku, 20014, Turku, Finland
| | - Kristina Noreikiene
- Chair of Aquaculture, Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 46, 51006, Tartu, Estonia
| | - Siim Kahar
- Chair of Aquaculture, Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 46, 51006, Tartu, Estonia
| | - Magnus Huss
- Swedish University of Agricultural Sciences, Department of Aquatic Resources, 74242, Öregrund, Sweden
| | - Ari Huusko
- Natural resources Institute Finland (Luke), 88300, Paltamo, Finland
| | - Toomas Kõiv
- Chair of Hydrobiology and Fishery, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006, Tartu, Estonia
| | - Margot Sepp
- Chair of Hydrobiology and Fishery, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006, Tartu, Estonia
| | - María López
- Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, 17893, Drottningholm, Sweden
| | - Anna Gårdmark
- Swedish University of Agricultural Sciences, Department of Aquatic Resources, 74242, Öregrund, Sweden
| | - Riho Gross
- Chair of Aquaculture, Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 46, 51006, Tartu, Estonia
| | - Anti Vasemägi
- Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, 17893, Drottningholm, Sweden.,Chair of Aquaculture, Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 46, 51006, Tartu, Estonia
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Yamamoto T, Sato Y, Yasuda S, Shikamura M, Tamura T, Takenaka C, Takasu N, Nomura M, Dohi H, Takahashi M, Mandai M, Kanemura Y, Nakamura M, Okano H, Kawamata S. OUP accepted manuscript. Stem Cells Transl Med 2022; 11:527-538. [PMID: 35445254 PMCID: PMC9154342 DOI: 10.1093/stcltm/szac014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/13/2022] [Indexed: 11/15/2022] Open
Abstract
Cell therapy using induced pluripotent stem cell (iPSC) derivatives may result in abnormal tissue generation because the cells undergo numerous cycles of mitosis before clinical application, potentially increasing the accumulation of genetic abnormalities. Therefore, genetic tests may predict abnormal tissue formation after transplantation. Here, we administered iPSC derivatives with or without single-nucleotide variants (SNVs) and deletions in cancer-related genes with various genomic copy number variant (CNV) profiles into immunodeficient mice and examined the relationships between mutations and abnormal tissue formation after transplantation. No positive correlations were found between the presence of SNVs/deletions and the formation of abnormal tissues; the overall predictivity was 29%. However, a copy number higher than 3 was correlated, with an overall predictivity of 86%. Furthermore, we found CNV hotspots at 14q32.33 and 17q12 loci. Thus, CNV analysis may predict abnormal tissue formation after transplantation of iPSC derivatives and reduce the number of tumorigenicity tests.
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Affiliation(s)
- Takako Yamamoto
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Yoji Sato
- Division of Cell-Based Therapeutic Products, National Institute of Health Sciences, Kawasaki, Japan
| | - Satoshi Yasuda
- Division of Cell-Based Therapeutic Products, National Institute of Health Sciences, Kawasaki, Japan
| | - Masayuki Shikamura
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Takashi Tamura
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Chiemi Takenaka
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | | | | | | | | | | | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Shin Kawamata
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
- Riken BDR, Kobe, Japan
- Corresponding author: Shin Kawamata, Minatojima-minamimachi 1-5-4, Chuo-ku Kobe, 650-0047 Japan.
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17
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Wojtaszewska M, Stępień R, Woźna A, Piernik M, Sztromwasser P, Dąbrowski M, Gniot M, Szymański S, Socha M, Kasprzak P, Matkowski R, Zawadzki P. Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach. Mol Diagn Ther 2021. [PMID: 34932189 DOI: 10.1007/s40291-021-00571-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2021] [Indexed: 11/02/2022]
Abstract
BACKGROUND AND OBJECTIVE Human epidermal growth factor receptor 2 (HER2) protein overexpression is one of the most significant biomarkers for breast cancer diagnostics, treatment prediction, and prognostics. The high accessibility of HER2 inhibitors in routine clinical practice directly translates into the diagnostic need for precise and robust marker identification. Even though multigene next-generation sequencing methodologies have slowly taken over the field of single-biomarker molecular tests, the copy number alterations such as amplification of the HER2-coding ERBB2 gene are hard to validate on next-generation sequencing platforms as they are characterized by chromosomal structural heterogeneity, polysomy, and genomic context of ploidy. In our study, we tested the approach of using whole genome sequencing instead of next-generation sequencing panels to determine HER2 status in the clinical set-up. METHODS We used a large dataset of 876 patients with breast cancer whole genomes with curated clinical data and an additional set of 551 patients' external genomic data. We used the decision-tree-based algorithm for optimization of the diagnostic tool for HER2 status assessment by whole genome sequencing. RESULTS The most efficient approach to assess HER2 status in whole genome sequencing data was the ploidy-corrected copy number, utilizing ERBB2 copy number and mean tumor ploidy. The classifier achieved sensitivity of 91.18% and specificity of 98.69% on the internal validation dataset and 89.86% and 96.06% on the external data, which is similar to other next-generation sequencing methods, currently tested in the clinic. CONCLUSIONS We provide evidence that the HER2 status may be reliably determined by whole genome sequencing and is applicable across different laboratory protocols and pipelines. We suggest using the ploidy-corrected copy number for diagnostic purposes.
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18
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Robine N, Varmus H. New York's Polyethnic-1000: a regional initiative to understand how diverse ancestries influence the risk, progression, and treatment of cancers. Trends Cancer 2021; 8:269-272. [PMID: 34895873 DOI: 10.1016/j.trecan.2021.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/09/2021] [Indexed: 12/22/2022]
Abstract
Research consortia can help to repair deficiencies in knowledge about the influence of inherited genetic diversity on disease. The New York Genome Center (NYGC) recently established Polyethnic-1000 (P-1000), a multi-institutional collaboration to study hereditary factors affecting several types of cancer. Here, we describe its rationale, organization, development, current activities, and prospects.
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Affiliation(s)
| | - Harold Varmus
- New York Genome Center, New York, NY, USA; Weill-Cornell Medicine, New York, NY, USA.
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19
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Lou RN, Therkildsen NO. Batch effects in population genomic studies with low-coverage whole genome sequencing data: Causes, detection and mitigation. Mol Ecol Resour 2021; 22:1678-1692. [PMID: 34825778 DOI: 10.1111/1755-0998.13559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/05/2021] [Accepted: 11/11/2021] [Indexed: 01/04/2023]
Abstract
Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a "batch-effect-naive" bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four-channel vs. two-channel), sequencing run, read type (single-end vs. paired-end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.
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20
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Kline J, Vardarajan B, Abhyankar A, Kytömaa S, Levin B, Sobreira N, Tang A, Thomas-Wilson A, Zhang R, Jobanputra V. Embryonic lethal genetic variants and chromosomally normal pregnancy loss. Fertil Steril 2021; 116:1351-1358. [PMID: 34756330 DOI: 10.1016/j.fertnstert.2021.06.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/16/2021] [Accepted: 06/23/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To examine whether rare damaging genetic variants are associated with chromosomally normal pregnancy loss and estimate the magnitude of the association. DESIGN Case-control. SETTING Cases were derived from a consecutive series of karyotyped losses at one New Jersey hospital. Controls were derived from the National Database for Autism Research. PATIENT(S) Cases comprised 19 chromosomally normal loss conceptus-parent trios. Controls comprised 547 unaffected siblings of autism case-parent trios. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) The rate of damaging variants in the exome (loss of function and missense-damaging) and the proportions of probands with at least one such variant among cases vs. controls. RESULTS The proportions of probands with at least one rare damaging variant were 36.8% among cases and 22.9% among controls (odds ratio, 2.0; 99% confidence interval, 0.5-7.3). No case had a variant in a known fetal anomaly gene. The proportion with variants in possibly embryonic lethal genes increased in case probands (odds ratio, 14.5; 99% confidence interval, 1.5-89.7); variants occurred in BAZ1A, FBN2, and TIMP2. CONCLUSION(S) Rare genetic variants in the conceptus may be a cause of chromosomally normal pregnancy loss. A larger sample is needed to estimate the magnitude of the association with precision and identify relevant biologic pathways.
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Affiliation(s)
- Jennie Kline
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York; Gertrude H. Sergievsky Center, Columbia University, New York, New York.
| | - Badri Vardarajan
- Gertrude H. Sergievsky Center, Columbia University, New York, New York
| | | | - Sonja Kytömaa
- Boston University School of Medicine, Boston, Massachusetts
| | - Bruce Levin
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
| | - Nara Sobreira
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Andrew Tang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | | | - Ruiwei Zhang
- Life Sciences Practice, Charles River Associates, New York, New York
| | - Vaidehi Jobanputra
- Department of Pathology and Cell Biology, Columbia University, New York, New York; New York Genome Center, New York, New York
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21
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Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021; 23:6361045. [PMID: 34471925 PMCID: PMC8769701 DOI: 10.1093/bib/bbab343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 05/04/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.
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Affiliation(s)
| | | | | | - Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
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22
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Musunuri R, Arora K, Corvelo A, Shah M, Shelton J, Zody MC, Narzisi G. Somatic variant analysis of linked-reads sequencing data with Lancet. Bioinformatics 2021; 37:1918-1919. [PMID: 33241313 DOI: 10.1093/bioinformatics/btaa888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/03/2020] [Accepted: 10/02/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY We present a new version of the popular somatic variant caller, Lancet, that supports the analysis of linked-reads sequencing data. By seamlessly integrating barcodes and haplotype read assignments within the colored De Bruijn graph local-assembly framework, Lancet computes a barcode-aware coverage and identifies variants that disagree with the local haplotype structure. AVAILABILITY AND IMPLEMENTATION Lancet is implemented in C++ and available for academic and non-commercial research purposes as an open-source package at https://github.com/nygenome/lancet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rajeeva Musunuri
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - Kanika Arora
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - André Corvelo
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - Minita Shah
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - Jennifer Shelton
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - Michael C Zody
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
| | - Giuseppe Narzisi
- Computational Biology Lab, New York Genome Center, New York, NY 10013, USA
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23
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Qi H, Ma M, Lai D, Tao SC. Phage display: an ideal platform for coupling protein to nucleic acid. Acta Biochim Biophys Sin (Shanghai) 2021; 53:389-399. [PMID: 33537750 DOI: 10.1093/abbs/gmab006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Indexed: 01/07/2023] Open
Abstract
Display technology, especially phage display technology, has been widely applied in many fields. The theoretical core of display technology is the physical linkage between the protein/peptide on the surface of a phage and the coding DNA sequence inside the same phage. Starting from phage-displayed peptide/protein/antibody libraries and taking advantage of the ever-growing power of next-generation sequencing (NGS) for DNA sequencing/decoding, rich protein-related information can easily be obtained in a high-throughput way. Based on this information, many scientific and clinical questions can be readily addressed. In the past few years, aided by the development of NGS, droplet technology, and massive oligonucleotide synthesis, we have witnessed and continue to witness large advances of phage display technology, in both technology development and application. The aim of this review is to summarize and discuss these recent advances.
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Affiliation(s)
- Huan Qi
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mingliang Ma
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Danyun Lai
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sheng-ce Tao
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China
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24
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De-Kayne R, Frei D, Greenway R, Mendes SL, Retel C, Feulner PGD. Sequencing platform shifts provide opportunities but pose challenges for combining genomic data sets. Mol Ecol Resour 2020; 21:653-660. [PMID: 33314612 DOI: 10.1111/1755-0998.13309] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/23/2020] [Accepted: 12/07/2020] [Indexed: 12/31/2022]
Abstract
Technological advances in DNA sequencing over the last decade now permit the production and curation of large genomic data sets in an increasing number of nonmodel species. Additionally, these new data provide the opportunity for combining data sets, resulting in larger studies with a broader taxonomic range. Whilst the development of new sequencing platforms has been beneficial, resulting in a higher throughput of data at a lower per-base cost, shifts in sequencing technology can also pose challenges for those wishing to combine new sequencing data with data sequenced on older platforms. Here, we outline the types of studies where the use of curated data might be beneficial, and highlight potential biases that might be introduced by combining data from different sequencing platforms. As an example of the challenges associated with combining data across sequencing platforms, we focus on the impact of the shift in Illumina's base calling technology from a four-channel system to a two-channel system. We caution that when data are combined from these two systems, erroneous guanine base calls that result from the two-channel chemistry can make their way through a bioinformatic pipeline, eventually leading to inaccurate and potentially misleading conclusions. We also suggest solutions for dealing with such potential artefacts, which make samples sequenced on different sequencing platforms appear more differentiated from one another than they really are. Finally, we stress the importance of archiving tissue samples and the associated sequences for the continued reproducibility and reusability of sequencing data in the face of ever-changing sequencing platform technology.
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Affiliation(s)
- Rishi De-Kayne
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - David Frei
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Ryan Greenway
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
| | - Sofia L Mendes
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Cas Retel
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
| | - Philine G D Feulner
- Department of Fish Ecology and Evolution, Centre of Ecology, Evolution and Biogeochemistry, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.,Division of Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
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25
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Zeng Z, Fu J, Cibulskis C, Jhaveri A, Gumbs C, Das B, Sanchez-Espiridion B, Janssens S, Taing L, Wang J, Lindsay J, Vilimas T, Zhang J, Tokheim C, Sahu A, Jiang P, Yan C, Duose DY, Cerami E, Chen L, Cohen D, Chen Q, Enos R, Huang X, Lee JJ, Liu Y, Neuberg DS, Nguyen C, Patterson C, Sarkar S, Shukla S, Tang M, Tsuji J, Uduman M, Wang X, Weirather JL, Yu J, Yu J, Zhang J, Zhang J, Meerzaman D, Thurin M, Futreal A, Karlovich C, Gabriel SB, Wistuba II, Liu XS, Wu CJ. Cross-Site Concordance Evaluation of Tumor DNA and RNA Sequencing Platforms for the CIMAC-CIDC Network. Clin Cancer Res 2020; 27:5049-5061. [PMID: 33323402 DOI: 10.1158/1078-0432.ccr-20-3251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/24/2020] [Accepted: 12/08/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Whole-exome (WES) and RNA sequencing (RNA-seq) are key components of cancer immunogenomic analyses. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the Cancer Immune Monitoring and Analysis Centers (CIMAC) and the Cancer Immunologic Data Commons (CIDC) conducted a systematic harmonization study. EXPERIMENTAL DESIGN DNA and RNA were centrally extracted from fresh frozen and formalin-fixed paraffin-embedded non-small cell lung carcinoma tumors and distributed to three centers for WES and RNA-seq profiling. In addition, two 10-plex HapMap cell line pools with known mutations were used to evaluate the accuracy of the WES platforms. RESULTS The WES platforms achieved high precision (> 0.98) and recall (> 0.87) on the HapMap pools when evaluated on loci using > 50× common coverage. Nonsynonymous mutations clustered by tumor sample, achieving an index of specific agreement above 0.67 among replicates, centers, and sample processing. A DV200 > 24% for RNA, as a putative presequencing RNA quality control (QC) metric, was found to be a reliable threshold for generating consistent expression readouts in RNA-seq and NanoString data. MedTIN > 30 was likewise assessed as a reliable RNA-seq QC metric, above which samples from the same tumor across replicates, centers, and sample processing runs could be robustly clustered and HLA typing, immune infiltration, and immune repertoire inference could be performed. CONCLUSIONS The CIMAC collaborating laboratory platforms effectively generated consistent WES and RNA-seq data and enable robust cross-trial comparisons and meta-analyses of highly complex immuno-oncology biomarker data across the NCI CIMAC-CIDC Network.
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Affiliation(s)
- Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, China
| | | | - Aashna Jhaveri
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Curtis Gumbs
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Biswajit Das
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Beatriz Sanchez-Espiridion
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Len Taing
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, China
| | - James Lindsay
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Tomas Vilimas
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Avinash Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peng Jiang
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Chunhua Yan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | - Dzifa Yawa Duose
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Li Chen
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - David Cohen
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Qingrong Chen
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | | | - Xin Huang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jack J Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Donna S Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Cu Nguyen
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | | | - Sharmistha Sarkar
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sachet Shukla
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ming Tang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Junko Tsuji
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Mohamed Uduman
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Xiaoman Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jason L Weirather
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.,Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jijun Yu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Joyce Yu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology and Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jiexin Zhang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daoud Meerzaman
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | - Magdalena Thurin
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland
| | - Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chris Karlovich
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | | | - Ignacio Ivan Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts. .,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Catherine J Wu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts. .,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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26
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Gong T, Hayes VM, Chan EKF. Shiny-SoSV: A web-based performance calculator for somatic structural variant detection. PLoS One 2020; 15:e0238108. [PMID: 32853264 DOI: 10.1371/journal.pone.0238108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/10/2020] [Indexed: 11/19/2022] Open
Abstract
Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user’s guide and example use-cases.
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