. 2021 Oct 1;12(1):5775.

doi: 10.1038/s41467-021-26042-z.

Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

Paloma Cejas^{1

2

3}, Yingtian Xie^{4

5}, Alba Font-Tello^{4

5}, Klothilda Lim^{4

5}, Sudeepa Syamala^{4

5}, Xintao Qiu^{4

5}, Alok K Tewari^{4

5

6}, Neel Shah^{4

5}, Holly M Nguyen⁷, Radhika A Patel⁸, Lisha Brown⁷, Ilsa Coleman⁸, Wenzel M Hackeng⁹, Lodewijk Brosens⁹, Koen M A Dreijerink¹⁰, Leigh Ellis^{6

11}, Sarah Abou Alaiwi⁴, Ji-Heui Seo⁴, Sylvan Baca⁴, Himisha Beltran⁴, Francesca Khani¹², Mark Pomerantz⁴, Alessandra Dall'Agnese¹³, Jett Crowdis^{4

6}, Eliezer M Van Allen^{4

6}, Joaquim Bellmunt¹⁴, Colm Morrisey⁷, Peter S Nelson⁸, James DeCaprio⁴, Anna Farago¹⁵, Nicholas Dyson¹⁵, Benjamin Drapkin^{16

17

18}, X Shirley Liu^{5

19}, Matthew Freedman^{4

5}, Michael C Haffner^{8

20

21}, Eva Corey⁷, Myles Brown^{22

23}, Henry W Long^{24

25}

Affiliations

¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. paloma_cejas@dfci.harvard.edu.
² Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. paloma_cejas@dfci.harvard.edu.
³ Translational Oncology Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ) and CIBERONC, La Paz University Hospital, Madrid, Spain. paloma_cejas@dfci.harvard.edu.
⁴ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
⁵ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Department of Urology, University of Washington, Seattle, WA, USA.
⁸ Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
⁹ Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
¹⁰ Department of Endocrinology, Amsterdam UMC, Amsterdam, The Netherlands.
¹¹ Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
¹² Weill Cornell Medical Center, Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital, New York, NY, USA.
¹³ Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA.
¹⁴ Beth Israel Deaconess Medical Center and PSMAR-IMIM Lab. Harvard Medical School, Boston, Massachusetts, USA.
¹⁵ Massachusetts General Hospital Cancer Center, Boston, MA, USA.
¹⁶ Nancy B. and Jake L. Hamon Center for Therapeutic Oncology Research, Dallas, TX, USA.
¹⁷ Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹⁸ Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹⁹ Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁰ Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
²¹ Department of Pathology, University of Washington, Seattle, WA, USA.
²² Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. myles_brown@dfci.harvard.edu.
²³ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. myles_brown@dfci.harvard.edu.
²⁴ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. henry_long@dfci.harvard.edu.
²⁵ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. henry_long@dfci.harvard.edu.

PMID: 34599169
PMCID: PMC8486778
DOI: 10.1038/s41467-021-26042-z

Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

Paloma Cejas et al. Nat Commun. 2021.

. 2021 Oct 1;12(1):5775.

doi: 10.1038/s41467-021-26042-z.

Authors

Affiliations

¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. paloma_cejas@dfci.harvard.edu.
² Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. paloma_cejas@dfci.harvard.edu.
³ Translational Oncology Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ) and CIBERONC, La Paz University Hospital, Madrid, Spain. paloma_cejas@dfci.harvard.edu.
⁴ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
⁵ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Department of Urology, University of Washington, Seattle, WA, USA.
⁸ Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
⁹ Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
¹⁰ Department of Endocrinology, Amsterdam UMC, Amsterdam, The Netherlands.
¹¹ Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
¹² Weill Cornell Medical Center, Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital, New York, NY, USA.
¹³ Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA.
¹⁴ Beth Israel Deaconess Medical Center and PSMAR-IMIM Lab. Harvard Medical School, Boston, Massachusetts, USA.
¹⁵ Massachusetts General Hospital Cancer Center, Boston, MA, USA.
¹⁶ Nancy B. and Jake L. Hamon Center for Therapeutic Oncology Research, Dallas, TX, USA.
¹⁷ Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹⁸ Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹⁹ Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁰ Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
²¹ Department of Pathology, University of Washington, Seattle, WA, USA.
²² Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. myles_brown@dfci.harvard.edu.
²³ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. myles_brown@dfci.harvard.edu.
²⁴ Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. henry_long@dfci.harvard.edu.
²⁵ Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. henry_long@dfci.harvard.edu.

PMID: 34599169
PMCID: PMC8486778
DOI: 10.1038/s41467-021-26042-z

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. NE carcinomas share a common chromatin state independent of their anatomical origin.**
a Principal component analysis (PCA) of ATAC-seq data of NECs including Merkel cell carcinoma (MCC), neuroendocrine prostate cancer (NEPC), gastrointestinal neuroendocrine carcinoma (GINE), and small-cell lung cancer (SCLC). The plot also includes prostate adenocarcinoma (PDX models and TCGA primary tissues) and lung adenocarcinoma (TCGA primary tissues). b Hierarchical clustering of the pairwise Pearson’s correlation of the ATAC-seq signal across the distinct tumor types. c Heatmap representation of the differential regions between representative ADs and NECs. Each row is a peak location and each column is a sample. Shown above each column are the composite tag density plots for the AD sites (blue) and NE sites (green). d Gene Ontology enrichment using a binomial test showing the pathways enriched in genes with nearby NE-specific accessible regions shown in c. e Top results from motif analysis of the NE-specific accessible regions. f Public ChIP-seq data sets showing the highest overlap with the NE-specific accessible regions as determined by CistromeDB toolkit annotated by tissue type. The TFs are ordered by the top scoring data set of each type. g Expression of NE markers and bHLH TFs across all the NEC samples in our study displayed as a heatmap. Source data are provided as a Source Data file.

**Fig. 2. NEPC shows tumor subtypes based on the differential expression of the transcription factors ASCL1 and NEUROD1.**
a Principal component analysis of ATAC-seq data from NEPC and ADPC PDXs. Samples are color coded by the dominant TF expressed in that sample. b The left side of the heatmap (red) displays the differential ATAC-seq regions identified between NEPC subtypes. There are 12,751 NEUROD1-specific regions (top) and 8950 ASCL1-specific (bottom) ATAC sites. The right side of the heatmap shows the ChIP-seq data at the same sites for ASCL1 (green) and NEUROD1 (blue) for the indicated samples. c Association between differential ATAC-accessible sites and differential gene expression. Each volcano plot depicts RNA-seq log2-fold change (x-axis) and p-value adjusted for multiple hypothesis testing calculated by DESeq2 using a Wald’s test (y-axis). Each dot represents one gene: green indicates a differential ATAC peak is within 50 kB of the gene and orange indicates there is no such peak. Left: ASCL1-specific accessible regions and genes upregulated in ASCL1 subtype; (right) NEUROD1-specific accessible regions and genes upregulated in NEUROD1 subtype. d GSEA pathway analysis of genes enriched in the ASCL1 subtype (green) and the NEUROD1 subtype (blue) (**q-value < 0.001, *q-value < 0.05). e Signal distribution of H3K27ac marked enhancers from representative cases of the ASCL1 subtype (top) and NEUROD1 subtype (bottom). The bars in the lower right of each plot identify the subset of enhancers known as super-enhancers defined by the ROSE algorithm; 693 were identified in LuCaP 93 (ASCL1) and 766 in LuCaP 173.1 (NEUROD1). Super-enhancers nearby selected genes are indicated by the arrows. f Representative IGV tracks at the ASCL1 and NEUROD1 gene loci. ATAC-seq tracks are in red, ASCL1 ChIP-seq in green, NEUROD1 ChIP-seq in blue, and H3K27ac in gray. The loci are marked by subtype-specific super-enhancers with preferential binding of their respective TF. g Circuits of lineage transcription factors specific for the ASCL1 subtype (green) and NEUROD1 subtype (blue). Source data are provided as a Source Data file.

**Fig. 3. Single-cell analysis reveals that NEPC subtypes co-exist in human metastasis and contribute to inter- and intra-tumoral heterogeneity.**
a Plot of ASCL1 and NEUROD1 expression in NEPC tissues from a clinical cohort. TPM: transcripts per million. b Representative immunostaining of FLM3 (ASCL1 staining in the top panel and NEUROD1 staining in the middle panel) showing intratumor heterogeneity. c Hematoxylin and eosin staining of the same field illustrates the distinct histologies for the two subpopulations. d Combined analysis of the scATAC-seq and snRNA-seq in FLM3 (left). Markers specific for normal cell populations enabled assignment of clusters: 1, vascular cells; 2, stromal cells; 3, hepatic cells; 4, monocytes. Accessibility at the top 30 differential ATAC-seq regions between ASCL1 and NEUROD1 subtypes identified by bulk analysis (top right). Analysis of *ASCL1* and *NEUROD1* expression in the snRNA-seq analysis (bottom right). This analysis matches cells with TF expression and the corresponding differential DNA accessibility for each subtype. e tSNE analysis of the combined FLM3 (blue) and FLM5 (black) scATAC-seq data (left). The other three plots show accessibility at *INSM1* promoter (NE marker) and the differential accessibility at *ASCL1* promoter and *NEUROD1* promoter. f Projection of the aggregated scATAC-seq clusters for FLM3 and 5 (light brown dots) within the PCA space defined in Fig. 2a. Source data are provided as a Source Data file.

**Fig. 4. The NEPC subtypes are distinct clones.**
a Genome-wide CNV profiles inferred from the scATAC-seq clusters in FLM3 and FLM5. Black dots are values in 1 MB regions and the red line is the result of running a segmentation algorithm on the data (see “Methods”). Arrows point to differences seen in CNVs across the clusters. b Sample pairwise Pearson’s correlation of the CNV profiles. c Summary heatmap of the scATAC-seq-inferred CNV alterations across all of the patient samples (blue represents losses and red represents gains). d Heatmap of the single-cell CNV analysis of FLM3 where each column is a 2 MB bin tiled across the genome and the rows are individual cells that have been clustered with K-means. Arrows point to CNV differences observed here and in the cluster level analysis. e tSNE plot of FLM3 scATAC-seq data colored by the cluster each cell was partitioned into by the inferred CNV alterations. Those three clusters clearly correspond to NEUROD1 (blue), ASCL1 (green), and normal cells (gray). Source data are provided as a Source Data file.

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Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

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Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

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