The genomic landscape of 85 advanced neuroendocrine neoplasms reveals subtype-heterogeneity and potential therapeutic targets

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Abstract

Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) form clinically and genetically heterogeneous malignancies, characterized by distinct prognoses based upon primary tumor localization, functionality, grade, proliferation index and diverse outcomes to treatment. Here, we report the mutational landscape of 85 whole-genome sequenced aNEN. This landscape reveals distinct genomic subpopulations of aNEN based on primary localization and differentiation grade; we observe relatively high tumor mutational burdens (TMB) in neuroendocrine carcinoma (average 5.45 somatic mutations per megabase) with TP53, KRAS, RB1, CSMD3, APC, CSMD1, LRATD2, TRRAP and MYC as major drivers versus an overall low TMB in neuroendocrine tumors (1.09). Furthermore, we observe distinct drivers which are enriched in somatic aberrations in pancreatic ( MEN1, ATRX, DAXX, DMD and CREBBP) and midgut-derived neuroendocrine tumors ( CDKN1B). Finally, 49% of aNEN patients reveal potential therapeutic targets based upon actionable (and responsive) somatic aberrations within their genome; potentially directing improvements in aNEN treatment strategies.

Abstract

Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) display heterogeneous clinical and genetic characteristics. Here, the authors investigate the mutational landscape of 85 aNEN by whole genome sequencing and identify distinct subpopulations, tumour mutational burden patterns, drivers and actionable somatic alterations.

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Most cited references 97

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

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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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

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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Signatures of mutational processes in human cancer

Ludmil Alexandrov, Serena Nik-Zainal, David Wedge … (2015)

All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.

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Author and article information

Contributors

Harmen J. G. van de Werken:

ORCID: http://orcid.org/0000-0002-9794-1477

h.vandewerken@erasmusmc.nl

Bianca Mostert:

ORCID: http://orcid.org/0000-0001-5193-0820

b.mostert@erasmusmc.nl

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 29 July 2021

Publication date PMC-release: 29 July 2021

Publication date Collection: 2021

Volume: 12

Electronic Location Identifier: 4612

Affiliations

[1 ]GRID grid.5645.2, ISNI 000000040459992X, Cancer Computational Biology Center, Erasmus MC Cancer Institute, , University Medical Center, ; Rotterdam, the Netherlands

[2 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Urology, Erasmus MC Cancer Institute, , University Medical Center, ; Rotterdam, the Netherlands

[3 ]GRID grid.508717.c, ISNI 0000 0004 0637 3764, Department of Medical Oncology, , Erasmus MC Cancer Institute, ; Rotterdam, the Netherlands

[4 ]GRID grid.7692.a, ISNI 0000000090126352, Center for Molecular Medicine and Oncode Institute, , University Medical Center Utrecht, ; Utrecht, the Netherlands

[5 ]GRID grid.510953.b, Hartwig Medical Foundation, ; Amsterdam, the Netherlands

[6 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Medical Oncology, Cancer Institute, , University of Amsterdam, ; Amsterdam, The Netherlands

[7 ]GRID grid.16872.3a, ISNI 0000 0004 0435 165X, Department of Medical Oncology, , Amsterdam University Medical Centers, Cancer Center Amsterdam, ; Amsterdam, The Netherlands

[8 ]GRID grid.414711.6, ISNI 0000 0004 0477 4812, Department of Internal Medicine, , Maxima Medisch Centrum, ; Veldhoven, The Netherlands

[9 ]GRID grid.7692.a, ISNI 0000000090126352, Department of Endocrine Oncology, , University Medical Center Utrecht, ; Utrecht, The Netherlands

[10 ]GRID grid.511056.6, Center for Personalized Cancer Treatment, ; Rotterdam, the Netherlands

Author information

Job van Riet http://orcid.org/0000-0001-7767-7923

Harmen J. G. van de Werken http://orcid.org/0000-0002-9794-1477

Martijn P. Lolkema http://orcid.org/0000-0003-0466-2928

Bianca Mostert http://orcid.org/0000-0001-5193-0820

Article

Publisher ID: 24812

DOI: 10.1038/s41467-021-24812-3

PMC ID: 8322054

PubMed ID: 34326338

SO-VID: 14916efc-7541-4125-bfd7-678f459086b1

License:

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

Date received : 6 August 2020

Date accepted : 1 July 2021

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ScienceOpen disciplines: Uncategorized

Keywords: next-generation sequencing,neuroendocrine cancer,oncogenes,cancer genomics

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ScienceOpen disciplines: Uncategorized

Keywords: next-generation sequencing, neuroendocrine cancer, oncogenes, cancer genomics

The genomic landscape of 85 advanced neuroendocrine neoplasms reveals subtype-heterogeneity and potential therapeutic targets

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Most cited references 97

Fast and accurate short read alignment with Burrows–Wheeler transform

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Signatures of mutational processes in human cancer

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