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      Analysis of the genomic landscape of yolk sac tumors reveals mechanisms of evolution and chemoresistance

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          Abstract

          Yolk sac tumors (YSTs) are a major histological subtype of malignant ovarian germ cell tumors with a relatively poor prognosis. The molecular basis of this disease has not been thoroughly characterized at the genomic level. Here we perform whole-exome and RNA sequencing on 41 clinical tumor samples from 30 YST patients, with distinct responses to cisplatin-based chemotherapy. We show that microsatellite instability status and mutational signatures are informative of chemoresistance. We identify somatic driver candidates, including significantly mutated genes KRAS and KIT and copy-number alteration drivers, including deleted ARID1A and PARK2, and amplified ZNF217, CDKN1B, and KRAS. YSTs have very infrequent TP53 mutations, whereas the tumors from patients with abnormal gonadal development contain both KRAS and TP53 mutations. We further reveal a role of OVOL2 overexpression in YST resistance to cisplatin. This study lays a critical foundation for understanding key molecular aberrations in YSTs and developing related therapeutic strategies.

          Abstract

          Yolk sac tumours are a type of ovarian germ cell tumours. Here, the authors perform exome and RNA sequencing of clinical samples of 30 patients, characterize the mutational landscape of these rare tumours, and identify molecular features associated with resistance to cisplatin-based therapies.

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          Most cited references64

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

              We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

                Author and article information

                Contributors
                hliang1@mdanderson.org
                yangjiaxin@pumch.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 June 2021
                11 June 2021
                2021
                : 12
                : 3579
                Affiliations
                [1 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, China
                [2 ]Precision Scientific (Beijing) Co., Ltd., Beijing, China
                [3 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Bioinformatics and Computational Biology, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [4 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Systems Biology, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                Author information
                http://orcid.org/0000-0002-7856-1655
                http://orcid.org/0000-0001-7633-286X
                http://orcid.org/0000-0002-1276-1136
                Article
                23681
                10.1038/s41467-021-23681-0
                8196104
                34117242
                12dfd8bb-9d41-40fb-9759-ee77cc12667e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2019
                : 11 May 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer,cancer genomics,computational biology and bioinformatics
                Uncategorized
                cancer, cancer genomics, computational biology and bioinformatics

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