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      Targeted deep sequencing reveals the genetic heterogeneity in well-differentiated pancreatic neuroendocrine tumors with liver metastasis

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          Abstract

          Background

          Pancreatic neuroendocrine tumor is a rare and heterogeneous entity, and approximately half of the patients harbored liver metastasis when initially diagnosed, whose prognosis is dismal. High-throughput sequencing has largely uncovered the genomic features of pancreatic neuroendocrine tumor, but the genetic alterations in the metastatic cases remain relatively unclear, which we aimed to study.

          Methods

          Pathologically confirmed well-differentiated pancreatic neuroendocrine tumor samples resected in our hospital from 2000 to 2019 were collected. We performed deep sequencing on the exome of 341 tumor-related genes, and compared the differences of genetic alterations between the metastatic and the non-metastatic cases, as well as between the primary and the paired liver metastatic tumors.

          Results

          Sequencing data of 79 samples from 29 pancreatic neuroendocrine tumor patients were included into analysis. A total of 2,471 somatic variants were identified, 75.5% of which were considered as low-abundance. NOTCH1 was the most frequently mutated gene, altered in 26 (53.1%) pancreatic neuroendocrine tumor samples from 18 (62.1%) patients. Compared with the non-metastatic pancreatic neuroendocrine tumors, the metastatic cases were discovered with more single nucleotide variants and copy number variations, indicating the increased genomic instability. In addition, among the paired metastatic cases, the primary and the metastatic lesions shared limited mutated genes.

          Conclusions

          Through the targeted deep sequencing, we identified the intratumor, intraindividual, and interindividual heterogeneity in the pancreatic neuroendocrine tumor patients, particularly in the metastatic cases, bringing potential challenges for the current biopsy strategies in guiding clinical treatments.

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          Most cited references31

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Journal
                Hepatobiliary Surg Nutr
                Hepatobiliary Surg Nutr
                HBSN
                Hepatobiliary Surgery and Nutrition
                AME Publishing Company
                2304-3881
                2304-389X
                19 May 2022
                01 June 2023
                : 12
                : 3
                : 302-313
                Affiliations
                [1 ]deptDepartment of Pancreatic Surgery, Zhongshan Hospital , Fudan University , Shanghai, China;
                [2 ]deptDepartment of Pathology, Zhongshan Hospital , Fudan University , Shanghai, China;
                [3 ]CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences , deptChinese Academy of Sciences , Shanghai, China;
                [4 ]deptInstitute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology , Chinese Academy of Sciences , Shanghai, China;
                [5 ]deptDepartment of General Surgery , Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University , Shanghai, China
                Author notes

                Contributions: (I) Conception and design: W Lou, Z Qiu, D Xie; (II) Administrative support: W Lou, Z Qiu; (III) Provision of study materials or patients: Y Ji, D Wang, D Xie; (IV) Collection and assembly of data: W Zhou, Y Ji, D Wang; (V) Data analysis and interpretation: W Zhou, X Han, Y Ji, D Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                Correspondence to: Wenhui Lou. Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai 200032, China. Email: lou.wenhui@ 123456outlook.com ; Zilong Qiu. Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui District, Shanghai 200031, China. Email: zqiu@ 123456ion.ac.cn .
                Article
                hbsn-12-03-302
                10.21037/hbsn-21-413
                10282677
                37351122
                762ee2b2-31c8-4ae8-96ca-92daf049214b
                2023 Hepatobiliary Surgery and Nutrition. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                : 03 October 2021
                : 10 February 2022
                Categories
                Original Article

                pancreatic neuroendocrine tumor (pnet),liver metastasis,targeted sequencing,genomic alteration,heterogeneity

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