Temporal evolution of cellular heterogeneity during the progression to advanced AR-negative prostate cancer.
Adenocarcinoma
/ genetics
Animals
Carcinoma, Neuroendocrine
/ genetics
Cell Line, Tumor
Disease Progression
Gene Expression Regulation, Neoplastic
Humans
Male
Mice, Inbred C57BL
Mice, Knockout
Mice, Transgenic
N-Myc Proto-Oncogene Protein
/ genetics
Organ Culture Techniques
/ methods
Prognosis
Prostate
/ metabolism
Prostatic Neoplasms
/ genetics
Receptors, Androgen
/ genetics
Retinoblastoma Protein
/ genetics
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
07 06 2021
07 06 2021
Historique:
received:
20
09
2020
accepted:
11
05
2021
entrez:
8
6
2021
pubmed:
9
6
2021
medline:
29
6
2021
Statut:
epublish
Résumé
Despite advances in the development of highly effective androgen receptor (AR)-directed therapies for the treatment of men with advanced prostate cancer, acquired resistance to such therapies frequently ensues. A significant subset of patients with resistant disease develop AR-negative tumors that lose their luminal identity and display neuroendocrine features (neuroendocrine prostate cancer (NEPC)). The cellular heterogeneity and the molecular evolution during the progression from AR-positive adenocarcinoma to AR-negative NEPC has yet to be characterized. Utilizing a new genetically engineered mouse model, we have characterized the synergy between Rb1 loss and MYCN (encodes N-Myc) overexpression which results in the formation of AR-negative, poorly differentiated tumors with high metastatic potential. Single-cell-based approaches revealed striking temporal changes to the transcriptome and chromatin accessibility which have identified the emergence of distinct cell populations, marked by differential expression of Ascl1 and Pou2f3, during the transition to NEPC. Moreover, global DNA methylation and the N-Myc cistrome are redirected following Rb1 loss. Altogether, our data provide insight into the progression of prostate adenocarcinoma to NEPC.
Identifiants
pubmed: 34099734
doi: 10.1038/s41467-021-23780-y
pii: 10.1038/s41467-021-23780-y
pmc: PMC8185096
doi:
Substances chimiques
N-Myc Proto-Oncogene Protein
0
Receptors, Androgen
0
Retinoblastoma Protein
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
3372Subventions
Organisme : NCI NIH HHS
ID : P50 CA211024
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA230913
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA203702
Pays : United States
Références
Aparicio, A. et al. Neuroendocrine prostate cancer xenografts with large-cell and small-cell features derived from a single patient’s tumor: morphological, immunohistochemical, and gene expression profiles. Prostate 71, 846–856 (2011).
pubmed: 21456067
doi: 10.1002/pros.21301
Beltran, H. et al. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat. Med. 22, 298–305 (2016).
pubmed: 26855148
pmcid: 4777652
doi: 10.1038/nm.4045
Tzelepi, V. et al. Modeling a lethal prostate cancer variant with small-cell carcinoma features. Clin. Cancer Res. 18, 666–677 (2012).
pubmed: 22156612
doi: 10.1158/1078-0432.CCR-11-1867
Rickman, D. S., Beltran, H., Demichelis, F. & Rubin, M. A. Biology and evolution of poorly differentiated neuroendocrine tumors. Nat. Med. 23, 1–10 (2017).
pubmed: 28586335
doi: 10.1038/nm.4341
Beltran, H. et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets. Cancer Discov. 1, 487–495 (2011).
pubmed: 22389870
pmcid: 3290518
doi: 10.1158/2159-8290.CD-11-0130
Berger, A. et al. N-Myc-mediated epigenetic reprogramming drives lineage plasticity in advanced prostate cancer. J. Clin. Invest. 130, 3924–3940 (2019).
doi: 10.1172/JCI127961
Dardenne, E. et al. N-Myc induces an EZH2-mediated transcriptional program driving neuroendocrine prostate cancer. Cancer Cell 30, 563–577 (2016).
pubmed: 27728805
pmcid: 5540451
doi: 10.1016/j.ccell.2016.09.005
Lee, J. K. et al. N-Myc drives neuroendocrine prostate cancer initiated from human prostate epithelial cells. Cancer Cell 29, 536–547 (2016).
pubmed: 27050099
pmcid: 4829466
doi: 10.1016/j.ccell.2016.03.001
Aparicio, A. M. et al. Combined tumor suppressor defects characterize clinically defined aggressive variant prostate cancers. Clin. Cancer Res. 22, 1520–1530 (2016).
pubmed: 26546618
doi: 10.1158/1078-0432.CCR-15-1259
Ku, S. Y. et al. Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 355, 78–83 (2017).
pubmed: 28059767
pmcid: 5367887
doi: 10.1126/science.aah4199
Martin, P. et al. Prostate epithelial Pten/TP53 loss leads to transformation of multipotential progenitors and epithelial to mesenchymal transition. Am. J. Pathol. 179, 422–435 (2011).
pubmed: 21703421
pmcid: 3123810
doi: 10.1016/j.ajpath.2011.03.035
Mu, P. et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science 355, 84–88 (2017).
pubmed: 28059768
pmcid: 5247742
doi: 10.1126/science.aah4307
Tan, H. L. et al. Rb loss is characteristic of prostatic small cell neuroendocrine carcinoma. Clin. Cancer Res. 20, 890–903 (2014).
pubmed: 24323898
doi: 10.1158/1078-0432.CCR-13-1982
Zhou, Z. et al. Synergy of p53 and Rb deficiency in a conditional mouse model for metastatic prostate cancer. Cancer Res. 66, 7889–7898 (2006).
pubmed: 16912162
doi: 10.1158/0008-5472.CAN-06-0486
Zou, M. et al. Transdifferentiation as a mechanism of treatment resistance in a mouse model of castration-resistant prostate cancer. Cancer Discov. 7, 736–749 (2017).
pubmed: 28411207
pmcid: 5501744
doi: 10.1158/2159-8290.CD-16-1174
Wu, N. et al. A mouse model of MYCN-driven retinoblastoma reveals MYCN-independent tumor reemergence. J. Clin. Invest. 127, 888–898 (2017).
pubmed: 28165337
pmcid: 5330763
doi: 10.1172/JCI88508
Weiss, W. A., Aldape, K., Mohapatra, G., Feuerstein, B. G. & Bishop, J. M. Targeted expression of MYCN causes neuroblastoma in transgenic mice. EMBO J. 16, 2985–2995 (1997).
pubmed: 9214616
pmcid: 1169917
doi: 10.1093/emboj/16.11.2985
Balanis, N. G. et al. Pan-cancer convergence to a small-cell neuroendocrine phenotype that shares susceptibilities with hematological malignancies. Cancer Cell 36, 17–34 e17 (2019).
pubmed: 31287989
pmcid: 6703903
doi: 10.1016/j.ccell.2019.06.005
Rosenbaum, J. N. et al. INSM1: a novel immunohistochemical and molecular marker for neuroendocrine and neuroepithelial neoplasms. Am. J. Clin. Pathol. 144, 579–591 (2015).
pubmed: 26386079
doi: 10.1309/AJCPGZWXXBSNL4VD
Agoff, S. N. et al. Thyroid transcription factor-1 is expressed in extrapulmonary small cell carcinomas but not in other extrapulmonary neuroendocrine tumors. Mod. Pathol. 13, 238–242 (2000).
pubmed: 10757334
doi: 10.1038/modpathol.3880044
Dubchak, I. et al. Active conservation of noncoding sequences revealed by three-way species comparisons. Genome Res. 10, 1304–1306 (2000).
pubmed: 10984448
pmcid: 310906
doi: 10.1101/gr.142200
Frazer, K. A., Pachter, L., Poliakov, A., Rubin, E. M. & Dubchak, I. VISTA: computational tools for comparative genomics. Nucleic Acids Res. 32, W273–W279 (2004).
pubmed: 15215394
pmcid: 441596
doi: 10.1093/nar/gkh458
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
pubmed: 22588877
doi: 10.1158/2159-8290.CD-12-0095
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
pubmed: 23550210
pmcid: 4160307
doi: 10.1126/scisignal.2004088
Smith, B. A. et al. A human adult stem cell signature marks aggressive variants across epithelial cancers. Cell Rep. 24, 3353–3366 e3355 (2018).
pubmed: 30232014
pmcid: 6382070
doi: 10.1016/j.celrep.2018.08.062
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
pubmed: 24658644
pmcid: 4122333
doi: 10.1038/nbt.2859
Korenjak, M., Anderssen, E., Ramaswamy, S., Whetstine, J. R. & Dyson, N. J. RBF binding to both canonical E2F targets and noncanonical targets depends on functional dE2F/dDP complexes. Mol. Cell Biol. 32, 4375–4387 (2012).
pubmed: 22927638
pmcid: 3486151
doi: 10.1128/MCB.00536-12
Lin, P. C. et al. Epigenomic alterations in localized and advanced prostate cancer. Neoplasia 15, 373–383 (2013).
pubmed: 23555183
pmcid: 3612910
doi: 10.1593/neo.122146
Onder, T. T. et al. Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. Cancer Res. 68, 3645–3654 (2008).
pubmed: 18483246
doi: 10.1158/0008-5472.CAN-07-2938
Ireland, A. S. et al. MYC Drives temporal evolution of small cell lung cancer subtypes by reprogramming neuroendocrine fate. Cancer Cell https://doi.org/10.1016/j.ccell.2020.05.001 (2020).
Althoff, K. et al. A Cre-conditional MYCN-driven neuroblastoma mouse model as an improved tool for preclinical studies. Oncogene 34, 3357–3368 (2015).
pubmed: 25174395
doi: 10.1038/onc.2014.269
Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).
pubmed: 26797458
pmcid: 4793718
doi: 10.1038/nprot.2016.006
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
pubmed: 22383036
pmcid: 3334321
doi: 10.1038/nprot.2012.016
Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
pubmed: 25260700
doi: 10.1093/bioinformatics/btu638
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet 43, 491–498 (2011).
pubmed: 21478889
pmcid: 3083463
doi: 10.1038/ng.806
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinforma. 43, 11 10 11–11 10 33 (2013).
Engstrom, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).
pubmed: 24185836
pmcid: 4018468
doi: 10.1038/nmeth.2722
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685
pmcid: 2938201
doi: 10.1093/nar/gkq603
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
pubmed: 20513432
pmcid: 2898526
doi: 10.1016/j.molcel.2010.05.004
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
pubmed: 27079975
pmcid: 4987876
doi: 10.1093/nar/gkw257
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
doi: 10.1073/pnas.0506580102
pmcid: 1239896
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
pubmed: 30787437
pmcid: 6434952
doi: 10.1038/s41586-019-0969-x
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
pmcid: 4508757
doi: 10.1016/j.cell.2015.05.047
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
pubmed: 28825705
pmcid: 5764547
doi: 10.1038/nmeth.4402
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
pubmed: 30914743
pmcid: 6435756
doi: 10.1038/s41598-019-41695-z
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
pubmed: 30089906
pmcid: 6130801
doi: 10.1038/s41586-018-0414-6
Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).
pubmed: 21412275
doi: 10.1038/nprot.2010.190
Akalin, A. et al. Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia. PLoS Genet. 8, e1002781 (2012).
pubmed: 22737091
pmcid: 3380828
doi: 10.1371/journal.pgen.1002781
Garrett-Bakelman, F. E. et al. Enhanced reduced representation bisulfite sequencing for assessment of DNA methylation at base pair resolution. J. Vis. Exp. e52246, https://doi.org/10.3791/52246 (2015).
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).
pubmed: 21493656
pmcid: 3102221
doi: 10.1093/bioinformatics/btr167
Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).
pubmed: 23034086
pmcid: 3491415
doi: 10.1186/gb-2012-13-10-r87
Ittmann, M. et al. Animal models of human prostate cancer: the consensus report of the New York meeting of the Mouse Models of Human Cancers Consortium Prostate Pathology Committee. Cancer Res. 73, 2718–2736 (2013).
pubmed: 23610450
pmcid: 3644021
doi: 10.1158/0008-5472.CAN-12-4213