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Abstract
Ectopic Cushing syndrome is a rare clinical disorder resulting from excessive adrenocorticotrophic
hormone (ACTH) produced by non-pituitary neoplasms, mainly neuroendocrine neoplasms
(NENs) of the lung, pancreas, and gastrointestinal tract, and other less common sites.
The genetic background of ACTH-producing NENs has not been well studied. Inspired
by an index case of ACTH-producing pancreatic NEN carrying a gene fusion, we postulated
that ACTH-producing NENs might be enriched for gene fusions. We herein examined 21
ACTH-secreting NENs of the pancreas (10), lung (9), thymus (1), and kidney (1) using
targeted RNA sequencing. The tumors were classified according to the most recent WHO
classification as NET-G1/typical carcinoid (
n = 4), NETG-2/atypical carcinoid (
n = 14), and NET-G3 (
n = 3). Overall, targeted RNA sequencing was successful in 11 cases (4 of 10 pancreatic
tumors, 5 of 9 pulmonary tumors, and in the one renal and one thymic tumor). All four
successfully tested pancreatic tumors revealed a gene fusion: two had a
EWSR1::BEND2 and one case each had a
KMT2A::BCOR and a
TFG::ADGRG7 fusion, respectively.
EWSR1 rearrangements were confirmed in both tumors with a
EWSR1::BEND2 by FISH. Gene fusions were mutually exclusive with
ATRX,
DAXX, and
MEN1 mutations (the most frequently mutated genes in NETs) in all four cases. Using RNA-based
variant assessment (
n = 16) or via the TSO500 panel (
n = 5), no pathogenic BCOR mutations were detected in any of the cases. Taken together,
gene fusions were detected in 4/4 (100%) pancreatic versus 0/7 (0%) non-pancreatic
tumors, respectively. These results suggest a potential role for gene fusions in triggering
the ACTH production in pancreatic NENs presenting with ectopic Cushing syndrome. While
the exact mechanisms responsible for the ectopic ACTH secretion are beyond the scope
of this study, overexpressed fusion proteins might be involved in promoter-mediated
overexpression of pre-ACTH precursors in analogy to the mechanisms postulated for
EWSR1::CREB1-mediated paraneoplastic phenomena in certain mesenchymal neoplasms. The genetic background
of the ACTH-producing non-pancreatic NENs remains to be further studied.
To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1
The genomes of 102 primary pancreatic neuroendocrine tumours have been sequenced, revealing mutations in genes with functions such as chromatin remodelling, DNA damage repair, mTOR activation and telomere maintenance, and a greater-than-expected contribution from germ line mutations.
The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples ( n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS . In addition, we confirmed the transforming potential of two novel fusions, RRBP1 - RAF1 and RASGRP1 - ATP1A1 , in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.
Publisher:
Springer Berlin Heidelberg
(Berlin/Heidelberg
)
ISSN
(Print):
0945-6317
ISSN
(Electronic):
1432-2307
Publication date
(Electronic):
24
January
2023
Publication date PMC-release: 24
January
2023
Publication date
(Print):
2023
Volume: 482
Issue: 3
Pages: 507-516
Affiliations
[1
]GRID grid.411668.c, ISNI 0000 0000 9935 6525, Institute of Pathology, University Hospital Erlangen, Friedrich Alexander University
of Erlangen-Nuremberg & Comprehensive Cancer Center, , European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), ; Erlangen, Germany
[2
]GRID grid.6936.a, ISNI 0000000123222966, Institute of Pathology, , Technical University Munich, ; Munich, Germany
[3
]GRID grid.6936.a, ISNI 0000000123222966, Department of Internal Medicine 2, , Technical University Munich, ; Munich, Germany
[4
]GRID grid.5330.5, ISNI 0000 0001 2107 3311, Department of Medicine 1, Division of Endocrinology, Comprehensive Cancer Center,
, Erlangen University Hospital, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN),
Friedrich Alexander University of Erlangen-Nuremberg, ; Erlangen, Germany
[5
]GRID grid.18147.3b, ISNI 0000000121724807, Unit of Pathology, Department of Medicine and Surgery, , University of Insubria, ; Varese, Italy
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History
Date
received
: 17
October
2022
Date
revision received
: 1
December
2022
Date
accepted
: 18
December
2022
Funding
Funded by: Universitätsklinikum Erlangen (8546)
Open Access
:
Open Access funding enabled and organized by Projekt DEAL.
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