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Copy number variants as modifiers of breast cancer risk for BRCA1/BRCA2 pathogenic variant carriers.

Christopher Hakkaart | John F Pearson | Louise Marquart | Joe Dennis | George A R Wiggins | Daniel R Barnes | Bridget A Robinson | Peter D Mace | Kristiina Aittomäki | Irene L Andrulis | Banu K Arun | Jacopo Azzollini | Judith Balmaña | Rosa B Barkardottir | Sami Belhadj | Lieke Berger | Marinus J Blok | Susanne E Boonen | Julika Borde | Angela R Bradbury | Joan Brunet | Saundra S Buys | Maria A Caligo | Ian Campbell | Wendy K Chung | Kathleen B M Claes | GEMO Study Collaborators | EMBRACE Collaborators | Marie-Agnès Collonge-Rame | Jackie Cook | Casey Cosgrove | Fergus J Couch | Mary B Daly | Sita Dandiker | Rosemarie Davidson | Miguel de la Hoya | Robin de Putter | Capucine Delnatte | Mallika Dhawan | Orland Diez | Yuan Chun Ding | Susan M Domchek | Alan Donaldson | Jacqueline Eason | Douglas F Easton | Hans Ehrencrona | Christoph Engel | D Gareth Evans | Ulrike Faust | Lidia Feliubadaló | Florentia Fostira | Eitan Friedman | Megan Frone | Debra Frost | Judy Garber | Simon A Gayther | Andrea Gehrig | Paul Gesta | Andrew K Godwin | David E Goldgar | Mark H Greene | Eric Hahnen | Christopher R Hake | Ute Hamann | Thomas V O Hansen | Jan Hauke | Julia Hentschel | Natalie Herold | Ellen Honisch | Peter J Hulick | Evgeny N Imyanitov | SWE-BRCA Investigators | kConFab Investigators | HEBON Investigators | Claudine Isaacs | Louise Izatt | Angel Izquierdo | Anna Jakubowska | Paul A James | Ramunas Janavicius | Esther M John | Vijai Joseph | Beth Y Karlan | Zoe Kemp | Judy Kirk | Irene Konstantopoulou | Marco Koudijs | Ava Kwong | Yael Laitman | Fiona Lalloo | Christine Lasset | Charlotte Lautrup | Conxi Lazaro | Clémentine Legrand | Goska Leslie | Fabienne Lesueur | Phuong L Mai | Siranoush Manoukian | Véronique Mari | John W M Martens | Lesley McGuffog | Noura Mebirouk | Alfons Meindl | Austin Miller | Marco Montagna | Lidia Moserle | Emmanuelle Mouret-Fourme | Hannah Musgrave | Sophie Nambot | Katherine L Nathanson | Susan L Neuhausen | Heli Nevanlinna | Joanne Ngeow Yuen Yie | Tu Nguyen-Dumont | Liene Nikitina-Zake | Kenneth Offit | Edith Olah | Olufunmilayo I Olopade | Ana Osorio | Claus-Eric Ott | Sue K Park | Michael T Parsons | Inge Sokilde Pedersen | Ana Peixoto | Pedro Perez-Segura | Paolo Peterlongo | Timea Pocza | Paolo Radice | Juliane Ramser | Johanna Rantala | Gustavo C Rodriguez | Karina Rønlund | Efraim H Rosenberg | Maria Rossing | Rita K Schmutzler | Payal D Shah | Saba Sharif | Priyanka Sharma | Lucy E Side | Jacques Simard | Christian F Singer | Katie Snape | Doris Steinemann | Dominique Stoppa-Lyonnet | Christian Sutter | Yen Yen Tan | Manuel R Teixeira | Soo Hwang Teo | Mads Thomassen | Darcy L Thull | Marc Tischkowitz | Amanda E Toland | Alison H Trainer | Vishakha Tripathi | Nadine Tung | Klaartje van Engelen | Elizabeth J van Rensburg | Ana Vega | Alessandra Viel | Lisa Walker | Jeffrey N Weitzel | Marike R Wevers | Georgia Chenevix-Trench | Amanda B Spurdle | Antonis C Antoniou | Logan C Walker
Communications biology | 2022

The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.

Pubmed ID: 36203093 RIS Download

Associated grants

  • Agency: NCI NIH HHS, United States
    Id: U10 CA180868
  • Agency: Wellcome Trust, United Kingdom
    Id: 203477/Z/16/Z
  • Agency: NCI NIH HHS, United States
    Id: P30 CA008748
  • Agency: NIGMS NIH HHS, United States
    Id: P20 GM130423
  • Agency: NCI NIH HHS, United States
    Id: UG1 CA233191
  • Agency: NCI NIH HHS, United States
    Id: U10 CA180822
  • Agency: NCI NIH HHS, United States
    Id: UG1 CA233290
  • Agency: NCI NIH HHS, United States
    Id: UG1 CA189867

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This is a list of tools and resources that we have found mentioned in this publication.


ATCC (tool)

RRID:SCR_001672

Global nonprofit biological resource center (BRC) and research organization that provides biological products, technical services and educational programs to private industry, government and academic organizations. Its mission is to acquire, authenticate, preserve, develop and distribute biological materials, information, technology, intellectual property and standards for the advancement and application of scientific knowledge. The primary purpose of ATCC is to use its resources and experience as a BRC to become the world leader in standard biological reference materials management, intellectual property resource management and translational research as applied to biomaterial development, standardization and certification. ATCC characterizes cell lines, bacteria, viruses, fungi and protozoa, as well as develops and evaluates assays and techniques for validating research resources and preserving and distributing biological materials to the public and private sector research communities.

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GATK (tool)

RRID:SCR_001876

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

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Addgene (tool)

RRID:SCR_002037

Non-profit plasmid repository dedicated to helping scientists around the world share high-quality plasmids. Facilitates archiving and distributing DNA-based research reagents and associated data to scientists worldwide. Repository contains over 65,000 plasmids, including special collections on CRISPR, fluorescent proteins, and ready-to-use viral preparations. There is no cost for scientists to deposit plasmids, which saves time and money associated with shipping plasmids themselves. All plasmids are fully sequenced for validation and sequencing data is openly available. We handle the appropriate Material Transfer Agreements (MTA) with institutions, facilitating open exchange and offering intellectual property and liability protection for depositing scientists. Furthermore, we curate free educational resources for the scientific community including a blog, eBooks, video protocols, and detailed molecular biology resources.

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PennCNV (tool)

RRID:SCR_002518

A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

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LUMPY (tool)

RRID:SCR_003253

Software package as probabilistic framework for structural variant discovery. Capable of integrating any number of SV detection signals including those generated from read alignments or prior evidence. Simplified wrapper for standard analyses, LUMPY Express, can also be executed.

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ClinVar (tool)

RRID:SCR_006169

Archive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.

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CellProfiler Image Analysis Software (tool)

RRID:SCR_007358

Software tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.

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Thermo Fisher Scientific (tool)

RRID:SCR_008452

Commercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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CNVnator (tool)

RRID:SCR_010821

An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing.

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GenomeStudio (tool)

RRID:SCR_010973

Visualize and analyze data generated by all of Illumina''s platforms.

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Abcam (tool)

RRID:SCR_012931

A commercial antibody supplier which supplies primary and secondary antibodies, biochemicals, proteins, peptides, lysates, immunoassays and other kits.

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MCF-7 (tool)

RRID:CVCL_0031

Cell line MCF-7 is a Cancer cell line with a species of origin Homo sapiens (Human)

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