Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

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Abstract

Neuroendocrine carcinomas (NEC) are tumors expressing markers of neuronal differentiation that can arise at different anatomic sites but have strong histological and clinical similarities. Here we report the chromatin landscapes of a range of human NECs and show convergence to the activation of a common epigenetic program. With a particular focus on treatment emergent neuroendocrine prostate cancer (NEPC), we analyze cell lines, patient-derived xenograft (PDX) models and human clinical samples to show the existence of two distinct NEPC subtypes based on the expression of the neuronal transcription factors ASCL1 and NEUROD1. While in cell lines and PDX models these subtypes are mutually exclusive, single-cell analysis of human clinical samples exhibits a more complex tumor structure with subtypes coexisting as separate sub-populations within the same tumor. These tumor sub-populations differ genetically and epigenetically contributing to intra- and inter-tumoral heterogeneity in human metastases. Overall, our results provide a deeper understanding of the shared clinicopathological characteristics shown by NECs. Furthermore, the intratumoral heterogeneity of human NEPCs suggests the requirement of simultaneous targeting of coexisting tumor populations as a therapeutic strategy.

Abstract

Neuroendocrine carcinomas (NECs) arise from different anatomic sites, but have similar histological and clinical features. Here, the authors show that the epigenetic landscape of a range of NECs converges towards a common epigenetic state, while distinct subtypes occur within neuroendocrine prostate cancer contributing to intratumor heterogeneity in clinical samples.

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Most cited references 61

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

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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Is Open Access

GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Author and article information

Contributors

Paloma Cejas:

ORCID: http://orcid.org/0000-0002-2090-7748

paloma_cejas@dfci.harvard.edu

Myles Brown:

ORCID: http://orcid.org/0000-0002-8213-1658

myles_brown@dfci.harvard.edu

Henry W. Long:

ORCID: http://orcid.org/0000-0001-6849-6629

henry_long@dfci.harvard.edu

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): 1 October 2021

Publication date PMC-release: 1 October 2021

Publication date Collection: 2021

Volume: 12

Electronic Location Identifier: 5775

Affiliations

[1 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA

[2 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Center for Functional Cancer Epigenetics, , Dana-Farber Cancer Institute, ; Boston, MA USA

[3 ]GRID grid.81821.32, ISNI 0000 0000 8970 9163, Translational Oncology Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ) and CIBERONC, , La Paz University Hospital, ; Madrid, Spain

[4 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA

[5 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Urology, , University of Washington, ; Seattle, WA USA

[6 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Divisions of Human Biology and Clinical Research, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA

[7 ]GRID grid.5477.1, ISNI 0000000120346234, Department of Pathology, University Medical Center Utrecht, , Utrecht University, ; Utrecht, The Netherlands

[8 ]GRID grid.509540.d, ISNI 0000 0004 6880 3010, Department of Endocrinology, , Amsterdam UMC, ; Amsterdam, The Netherlands

[9 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Oncologic Pathology, , Dana-Farber Cancer Institute and Harvard Medical School, ; Boston, MA USA

[10 ]GRID grid.413734.6, ISNI 0000 0000 8499 1112, Weill Cornell Medical Center, Department of Pathology and Laboratory Medicine, , New York Presbyterian Hospital, ; New York, NY USA

[11 ]GRID grid.270301.7, ISNI 0000 0001 2292 6283, Whitehead Institute for Biomedical Research, ; 455 Main Street, Cambridge, MA 02142 USA

[12 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Beth Israel Deaconess Medical Center and PSMAR-IMIM Lab. Harvard Medical School, ; Boston, Massachusetts USA

[13 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Massachusetts General Hospital Cancer Center, ; Boston, MA USA

[14 ]Nancy B. and Jake L. Hamon Center for Therapeutic Oncology Research, Dallas, TX USA

[15 ]GRID grid.267313.2, ISNI 0000 0000 9482 7121, Harold C. Simmons Comprehensive Cancer Center, , University of Texas Southwestern Medical Center, ; Dallas, TX USA

[16 ]GRID grid.267313.2, ISNI 0000 0000 9482 7121, Department of Internal Medicine, , University of Texas Southwestern Medical Center, ; Dallas, TX USA

[17 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Department of Data Science, , Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, ; Boston, MA USA

[18 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Division of Clinical Research, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA

[19 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Pathology, , University of Washington, ; Seattle, WA USA

Author information

Paloma Cejas http://orcid.org/0000-0002-2090-7748

Alba Font-Tello http://orcid.org/0000-0002-7501-2888

Xintao Qiu http://orcid.org/0000-0002-8560-7017

Alok K. Tewari http://orcid.org/0000-0003-2617-7499

Lodewijk Brosens http://orcid.org/0000-0003-1341-8994

Koen M. A. Dreijerink http://orcid.org/0000-0002-3140-3502

Sarah Abou Alaiwi http://orcid.org/0000-0002-7073-3432

Himisha Beltran http://orcid.org/0000-0003-3259-2226

Francesca Khani http://orcid.org/0000-0001-7238-8604

Eliezer M. Van Allen http://orcid.org/0000-0002-0201-4444

Joaquim Bellmunt http://orcid.org/0000-0003-2328-3421

Peter S. Nelson http://orcid.org/0000-0002-5451-5726

James DeCaprio http://orcid.org/0000-0002-0896-167X

X. Shirley Liu http://orcid.org/0000-0003-4736-7339

Matthew Freedman http://orcid.org/0000-0002-0151-1238

Eva Corey http://orcid.org/0000-0002-9244-3807

Myles Brown http://orcid.org/0000-0002-8213-1658

Henry W. Long http://orcid.org/0000-0001-6849-6629

Article

Publisher ID: 26042

DOI: 10.1038/s41467-021-26042-z

PMC ID: 8486778

PubMed ID: 34599169

SO-VID: f368c9f8-f9a5-4f0a-8a85-f97a8438dd4d

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 : 30 October 2020

Date accepted : 7 September 2021

Funding

Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);

Award ID: 2P01CA163227-06A1

Award Recipient : Henry W. Long

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ScienceOpen disciplines: Uncategorized

Keywords: cancer genomics,neuroendocrine cancer,epigenetics

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ScienceOpen disciplines: Uncategorized

Keywords: cancer genomics, neuroendocrine cancer, epigenetics

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Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

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Cancer Metabolism

Most cited references 61

Gene Ontology: tool for the unification of biology

STAR: ultrafast universal RNA-seq aligner.

GSVA: gene set variation analysis for microarray and RNA-Seq data

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Cited by 39

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