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      Genetic changes associated with relapse in favorable histology Wilms tumor: A Children’s Oncology Group AREN03B2 study

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          Summary

          Over the last decade, sequencing of primary tumors has clarified the genetic underpinnings of Wilms tumor but has not affected therapy, outcome, or toxicity. We now sharpen our focus on relapse samples from the umbrella AREN03B2 study. We show that over 40% of relapse samples contain mutations in SIX1 or genes of the MYCN network, drivers of progenitor proliferation. Not previously seen in large studies of primary Wilms tumors, DIS3 and TERT are now identified as recurrently mutated. The analysis of primary-relapse tumor pairs suggests that 11p15 loss of heterozygosity (and other copy number changes) and mutations in WT1 and MLLT1 typically occur early, but mutations in SIX1, MYCN, and WTX are late developments in some individuals. Most strikingly, 75% of relapse samples had gain of 1q, providing strong conceptual support for studying circulating tumor DNA in clinical trials to better detect 1q gain earlier and monitor response.

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          Highlights

          • Methods for measuring 1q gain, seen in 75% of relapse samples, merit optimization

          • Drivers of progenitor proliferation ( SIX1 and MYCN) affect tumorigenesis and relapse

          • Mutations in DIS3 and in the TERT promoter are newly identified

          • Chromosomal gains and losses often occur prior to mutations in Wilms tumor

          Abstract

          Wilms tumors have many potential driver mutations. Gadd et al. focus on samples from relapse tumors revealing that over 40% contain mutations in SIX1 or in MYCN network genes, drivers of progenitor proliferation, and that 75% have 1q gain, supporting optimization of new tools for identifying 1q gain.

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          Most cited references81

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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              Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

              The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                25 May 2022
                21 June 2022
                25 May 2022
                : 3
                : 6
                : 100644
                Affiliations
                [1 ]Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago and Robert H. Lurie Cancer Center, Northwestern University, 225 East Chicago Avenue, Box 17, Chicago, IL 60611, USA
                [2 ]Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
                [3 ]Division of Biostatistics, University of Southern California, Los Angeles, CA 90007, USA
                [4 ]Department of Pediatrics, IWK Health Centre and Dalhousie University, Halifax, NS B3K 6R8, Canada
                [5 ]Department of Pediatric Oncology, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center and Harvard Medical School, Boston, MA 02215, USA
                [6 ]Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [7 ]Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [8 ]Institute for Genomic Medicine and Biopathology Center, Nationwide Children’s Hospital, Departments of Pathology and Pediatrics, Ohio State University, Columbus, OH 43205, USA
                [9 ]Biospecimen Research Group, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
                Author notes
                []Corresponding author eperlman@ 123456luriechildrens.org
                [10]

                Lead contact

                Article
                S2666-3791(22)00169-0 100644
                10.1016/j.xcrm.2022.100644
                9244995
                35617957
                6a00ded2-d2e2-4c3e-881d-0c00149c0480
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 5 January 2022
                : 23 March 2022
                : 4 May 2022
                Categories
                Article

                : wilms tumor,relapse,six1,mycn,dis3,tert,1q gain
                : wilms tumor, relapse, six1, mycn, dis3, tert, 1q gain

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