Prostate cancer castrate resistant progression usage of non-canonical androgen receptor signaling and ketone body fuel.
Androgen Antagonists
/ pharmacology
Animals
Cell Line, Tumor
Disease Progression
Fatty Acids
/ metabolism
Gene Expression Regulation, Neoplastic
/ drug effects
Humans
Ketone Bodies
/ metabolism
Male
Mice
Neoplasm Transplantation
Prostatic Neoplasms, Castration-Resistant
/ metabolism
Receptors, Androgen
/ metabolism
Signal Transduction
/ drug effects
Journal
Oncogene
ISSN: 1476-5594
Titre abrégé: Oncogene
Pays: England
ID NLM: 8711562
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
15
01
2021
accepted:
06
09
2021
revised:
25
08
2021
pubmed:
30
9
2021
medline:
30
12
2021
entrez:
29
9
2021
Statut:
ppublish
Résumé
Prostate cancer (PCa) that progresses after androgen deprivation therapy (ADT) remains incurable. The underlying mechanisms that account for the ultimate emergence of resistance to ADT, progressing to castrate-resistant prostate cancer (CRPC), include those that reactivate androgen receptor (AR), or those that are entirely independent or cooperate with androgen signaling to underlie PCa progression. The intricacy of metabolic pathways associated with PCa progression spurred us to develop a metabolism-centric analysis to assess the metabolic shift occurring in PCa that progresses with low AR expression. We used PCa patient-derived xenografts (PDXs) to assess the metabolic changes after castration of tumor-bearing mice and subsequently confirmed main findings in human donor tumor that progressed after ADT. We found that relapsed tumors had a significant increase in fatty acids and ketone body (KB) content compared with baseline. We confirmed that critical ketolytic enzymes (ACAT1, OXCT1, BDH1) were dysregulated after castrate-resistant progression. Further, these enzymes are increased in the human donor tissue after progressing to ADT. In an in silico approach, increased ACAT1, OXCT1, BDH1 expression was also observed for a subset of PCa patients that relapsed with low AR and ERG (ETS-related gene) expression. Further, expression of these factors was also associated with decreased time to biochemical relapse and decreased progression-free survival. Our studies reveal the key metabolites fueling castration resistant progression in the context of a partial or complete loss of AR dependence.
Identifiants
pubmed: 34584218
doi: 10.1038/s41388-021-02008-9
pii: 10.1038/s41388-021-02008-9
pmc: PMC8566229
doi:
Substances chimiques
AR protein, human
0
Androgen Antagonists
0
Fatty Acids
0
Ketone Bodies
0
Receptors, Androgen
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
6284-6298Subventions
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Informations de copyright
© 2021. The Author(s).
Références
Watson PA, Arora VK, Sawyers CL. Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer. Nat Rev Cancer. 2015;15:701–11.
pubmed: 26563462
pmcid: 4771416
doi: 10.1038/nrc4016
Nagarajan A, Malvi P, Wajapeyee N. Oncogene-directed alterations in cancer cell metabolism. Trends Cancer. 2016;2:365–77.
pubmed: 27822561
pmcid: 5096652
doi: 10.1016/j.trecan.2016.06.002
Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–33.
doi: 10.1126/science.1160809
Devic S. Warburg effect - a consequence or the cause of carcinogenesis? J Cancer. 2016;7:817–22.
pubmed: 27162540
pmcid: 4860798
doi: 10.7150/jca.14274
Jaworski DM, Namboodiri AM, Moffett JR. Acetate as a metabolic and epigenetic modifier of cancer therapy. J Cell Biochem. 2016;117:574–88.
pubmed: 26251955
doi: 10.1002/jcb.25305
Sullivan LB, Gui DY, Hosios AM, Bush LN, Freinkman E, Vander, Heiden MG. Supporting aspartate biosynthesis Is an essential function of respiration in proliferating cells. Cell. 2015;162:552–63.
pubmed: 26232225
pmcid: 4522278
doi: 10.1016/j.cell.2015.07.017
Antico Arciuch VG, Gueron G, Cotignola J, Vázquez ES. Altered signaling pathways in prostate cancer drive metabolic fate. Int J Sci Res. 2017;6:614–9.
Goveia J, Pircher A, Conradi L-C, Kalucka J, Lagani V, Dewerchin M, et al. Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities. EMBO Mol Med. 2016;8:1134–42.
pubmed: 27601137
pmcid: 5048364
doi: 10.15252/emmm.201606798
Yoshii Y, Furukawa T, Saga T, Fujibayashi Y. Acetate/acetyl-CoA metabolism associated with cancer fatty acid synthesis: overview and application. Cancer Lett. 2015;356:211–6. (2 Pt A).
pubmed: 24569091
doi: 10.1016/j.canlet.2014.02.019
Saraon P, Trudel D, Kron K, Dmitromanolakis A, Trachtenberg J, Bapat B, et al. Evaluation and prognostic significance of ACAT1 as a marker of prostate cancer progression. Prostate. 2014;74:372–80.
pubmed: 24311408
doi: 10.1002/pros.22758
Wan X, Corn PG, Yang J, Palanisamy N, Starbuck MW, Efstathiou E, et al. Prostate cancer cell-stromal cell crosstalk via FGFR1 mediates antitumor activity of dovitinib in bone metastases. Sci Transl Med. 2014;6:252ra122.
pubmed: 25186177
pmcid: 4407499
doi: 10.1126/scitranslmed.3009332
Varkaris A, Corn PG, Parikh NU, Efstathiou E, Song JH, Lee YC, et al. Integrating murine and clinical trials with cabozantinib to understand roles of MET and VEGFR2 as targets for growth inhibition of prostate cancer. Clin Cancer Res. 2016;22:107–21.
pubmed: 26272062
doi: 10.1158/1078-0432.CCR-15-0235
Brenner JC, Ateeq B, Li Y, Yocum AK, Cao Q, Asangani IA, et al. Mechanistic rationale for inhibition of poly(ADP-ribose) polymerase in ETS gene fusion-positive prostate cancer. Cancer Cell. 2011;19:664–78.
pubmed: 21575865
pmcid: 3113473
doi: 10.1016/j.ccr.2011.04.010
Li ZG, Mathew P, Yang J, Starbuck MW, Zurita AJ, Liu J, et al. Androgen receptor-negative human prostate cancer cells induce osteogenesis in mice through FGF9-mediated mechanisms. J Clin Invest. 2008;118:2697–710.
pubmed: 18618013
pmcid: 2447924
doi: 10.1172/JCI33637C1
Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu YM, Cao X, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra21.
doi: 10.1126/scitranslmed.3003161
Palanisamy N, Yang J, Shepherd PDA, Li-Ning-Tapia EM, Labanca E, Manyam G, et al. The MD Anderson prostate cancer patient-derived xenograft series (MDA PCa PDX) captures the molecular landscape of prostate cancer and facilitates marker-driven therapy development. Clin Cancer Res. 2020;26:4933–46.
pubmed: 32576626
pmcid: 7501166
doi: 10.1158/1078-0432.CCR-20-0479
Abida W, Armenia J, Gopalan A, Brennan R, Walsh M, Barron D, et al. Prospective genomic profiling of prostate cancer across disease states reveals germline and somatic alterations that may affect clinical decision making. JCO Precis Oncol. 2017;2017:PO.17.00029.
Navone NM, van Weerden WM, Vessella RL, Williams ED, Wang Y, Isaacs JT, et al. Movember GAP1 PDX project: an international collection of serially transplantable prostate cancer patient-derived xenograft (PDX) models. Prostate. 2018;78:1262–82.
pubmed: 30073676
doi: 10.1002/pros.23701
Zhang S, Xie C. The role of OXCT1 in the pathogenesis of cancer as a rate-limiting enzyme of ketone body metabolism. Life Sci. 2017;183:110–5.
pubmed: 28684065
doi: 10.1016/j.lfs.2017.07.003
Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. Cell. 2016;165:535–50.
pubmed: 27104977
doi: 10.1016/j.cell.2016.03.014
Cai C, Wang H, He HH, Chen S, He L, Ma F, et al. ERG induces androgen receptor-mediated regulation of SOX9 in prostate cancer. J Clin Invest. 2013;123:1109–22.
pubmed: 23426182
pmcid: 3582143
doi: 10.1172/JCI66666
Saraon P, Cretu D, Musrap N, Karagiannis GS, Batruch I, Drabovich AP, et al. Quantitative proteomics reveals that enzymes of the ketogenic pathway are associated with prostate cancer progression. Mol Cell Proteom. 2013;12:1589–601.
doi: 10.1074/mcp.M112.023887
Lima AR, Bastos Mde L, Carvalho M, Guedes de Pinho P. Biomarker discovery in human prostate cancer: an update in metabolomics studies. Transl Oncol. 2016;9:357–70.
pubmed: 27567960
pmcid: 5006818
doi: 10.1016/j.tranon.2016.05.004
Tomlins SA, Laxman B, Dhanasekaran SM, Helgeson BE, Cao X, Morris DS, et al. Distinct classes of chromosomal rearrangements create oncogenic ETS gene fusions in prostate cancer. Nature. 2007;448:595–9.
pubmed: 17671502
doi: 10.1038/nature06024
Zhang W, Liu B, Wu W, Li L, Broom BM, Basourakos SP, et al. Targeting the MYCN-PARP-DNA damage response pathway in neuroendocrine prostate cancer. Clin Cancer Res. 2018;24:696–707.
pubmed: 29138344
doi: 10.1158/1078-0432.CCR-17-1872
Li L, Chang W, Yang G, Ren C, Park S, Karantanos T, et al. Targeting poly(ADP-ribose) polymerase and the c-Myb-regulated DNA damage response pathway in castration-resistant prostate cancer. Sci Signal. 2014;7:ra47.
pubmed: 24847116
pmcid: 4135429
Salameh A, Lee AK, Cardo-Vila M, Nunes DN, Efstathiou E, Staquicini FI, et al. PRUNE2 is a human prostate cancer suppressor regulated by the intronic long noncoding RNA PCA3. Proc Natl Acad Sci USA. 2015;112:8403–8.
pubmed: 26080435
pmcid: 4500257
doi: 10.1073/pnas.1507882112
Zhang Y, Zheng D, Zhou T, Song H, Hulsurkar M, Su N, et al. Androgen deprivation promotes neuroendocrine differentiation and angiogenesis through CREB-EZH2-TSP1 pathway in prostate cancers. Nat Commun. 2018;9:4080.
pubmed: 30287808
pmcid: 6172226
doi: 10.1038/s41467-018-06177-2
Tzelepi V, Zhang J, Lu JF, Kleb B, Wu G, Wan X, et al. Modeling a lethal prostate cancer variant with small-cell carcinoma features. Clin Cancer Res. 2012;18:666–77.
pubmed: 22156612
doi: 10.1158/1078-0432.CCR-11-1867
Labanca E, Vazquez ES, Corn PG, Roberts JM, Wang F, Logothetis CJ, et al. Fibroblast growth factors signaling in bone metastasis. Endocr Relat Cancer. 2020;27:R255–R65.
pubmed: 32369771
pmcid: 7274538
doi: 10.1530/ERC-19-0472
Bluemn EG, Coleman IM, Lucas JM, Coleman RT, Hernandez-Lopez S, Tharakan R, et al. Androgen receptor pathway-independent prostate cancer is sustained through FGF signaling. Cancer Cell. 2017;32:474–89.e6.
pubmed: 29017058
pmcid: 5750052
doi: 10.1016/j.ccell.2017.09.003
Newman JC, Verdin E. Ketone bodies as signaling metabolites. Trends Endocrinol Metab. 2014;25:42–52.
pubmed: 24140022
doi: 10.1016/j.tem.2013.09.002
Martinez-Outschoorn UE, Lin Z, Whitaker-Menezes D, Howell A, Lisanti MP, Sotgia F. Ketone bodies and two-compartment tumor metabolism: stromal ketone production fuels mitochondrial biogenesis in epithelial cancer cells. Cell Cycle. 2012;11:3956–63.
pubmed: 23082721
pmcid: 3507491
doi: 10.4161/cc.22136
Rodrigues LM, Uribe-Lewis S, Madhu B, Honess DJ, Stubbs M, Griffiths JR. The action of β-hydroxybutyrate on the growth, metabolism and global histone H3 acetylation of spontaneous mouse mammary tumours: evidence of a β-hydroxybutyrate paradox. Cancer Metab. 2017;5:4.
pubmed: 28261475
pmcid: 5331634
doi: 10.1186/s40170-017-0166-z
Mierziak J, Burgberger M, Wojtasik W. 3-hydroxybutyrate as a metabolite and a signal molecule regulating processes of living organisms. Biomolecules. 2021;11:402.
pubmed: 33803253
pmcid: 8000602
doi: 10.3390/biom11030402
Chriett S, Dąbek A, Wojtala M, Vidal H, Balcerczyk A, Pirola L. Prominent action of butyrate over β-hydroxybutyrate as histone deacetylase inhibitor, transcriptional modulator and anti-inflammatory molecule. Sci Rep. 2019;9:742.
pubmed: 30679586
pmcid: 6346118
doi: 10.1038/s41598-018-36941-9
Puchalska P, Crawford PA. Multi-dimensional roles of ketone bodies in fuel metabolism, signaling, and therapeutics. Cell Metab. 2017;25:262–84.
pubmed: 28178565
pmcid: 5313038
doi: 10.1016/j.cmet.2016.12.022
Thumelin S, Forestier M, Girard J, Pegorier JP. Developmental changes in mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase gene expression in rat liver, intestine and kidney. Biochem J. 1993;292:493–6.
pubmed: 8099282
pmcid: 1134236
doi: 10.1042/bj2920493
Zhang D, Yang H, Kong X, Wang K, Mao X, Yan X, et al. Proteomics analysis reveals diabetic kidney as a ketogenic organ in type 2 diabetes. Am J Physiol Endocrinol Metab. 2011;300:E287–95.
pubmed: 20959534
doi: 10.1152/ajpendo.00308.2010
Garcia-Bermudez J, Birsoy K. Drugging ACAT1 for cancer therapy. Mol Cell. 2016;64:856–7.
pubmed: 27912096
doi: 10.1016/j.molcel.2016.11.023
Morscher RJ, Aminzadeh-Gohari S, Feichtinger RG, Mayr JA, Lang R, Neureiter D, et al. Inhibition of neuroblastoma tumor growth by ketogenic diet and/or calorie restriction in a CD1-Nu mouse model. PLoS One. 2015;10:e0129802–e.
pubmed: 26053068
pmcid: 4459995
doi: 10.1371/journal.pone.0129802
Shukla SK, Gebregiworgis T, Purohit V, Chaika NV, Gunda V, Radhakrishnan P, et al. Metabolic reprogramming induced by ketone bodies diminishes pancreatic cancer cachexia. Cancer Metab. 2014;2:18.
pubmed: 25228990
pmcid: 4165433
doi: 10.1186/2049-3002-2-18
Poff AM, Ari C, Seyfried TN, D’Agostino DP. The ketogenic diet and hyperbaric oxygen therapy prolong survival in mice with systemic metastatic cancer. PLoS One. 2013;8:e65522–e.
pubmed: 23755243
pmcid: 3673985
doi: 10.1371/journal.pone.0065522
Allen BG, Bhatia SK, Buatti JM, Brandt KE, Lindholm KE, Button AM, et al. Ketogenic diets enhance oxidative stress and radio-chemo-therapy responses in lung cancer xenografts. Clin Cancer Res. 2013;19:3905–13.
pubmed: 23743570
pmcid: 3954599
doi: 10.1158/1078-0432.CCR-12-0287
Klement RJ, Sweeney RA. Impact of a ketogenic diet intervention during radiotherapy on body composition: I. Initial clinical experience with six prospectively studied patients. BMC Res Notes. 2016;9:143.
pubmed: 26946138
pmcid: 4779584
doi: 10.1186/s13104-016-1959-9
Zhang J, Jia P-P, Liu Q-L, Cong M-H, Gao Y, Shi H-P, et al. Low ketolytic enzyme levels in tumors predict ketogenic diet responses in cancer cell lines in vitro and in vivo. J Lipid Res. 2018;59:625–34.
pubmed: 29414764
pmcid: 5880499
doi: 10.1194/jlr.M082040
Huang D, Li T, Wang L, Zhang L, Yan R, Li K, et al. Hepatocellular carcinoma redirects to ketolysis for progression under nutrition deprivation stress. Cell Res. 2016;26:1112–30.
pubmed: 27644987
pmcid: 5113304
doi: 10.1038/cr.2016.109
Faria M, Shepherd P, Pan Y, Chatterjee SS, Navone N, Gustafsson J-Å, et al. The estrogen receptor variants β2 and β5 induce stem cell characteristics and chemotherapy resistance in prostate cancer through activation of hypoxic signaling. Oncotarget. 2018;9:36273–88.
pubmed: 30555629
pmcid: 6284737
doi: 10.18632/oncotarget.26345
Kolde R pheatmap: Pretty heatmaps. R package version 1.0.12. https://cran.r-project.org/web/packages/pheatmap/index.html . 2019.
Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem. 2009;81:6656–67.
pubmed: 19624122
doi: 10.1021/ac901536h
Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform. 2010;2:9.
pubmed: 20955607
pmcid: 2984397
doi: 10.1186/1758-2946-2-9
Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci. 2003;100:9440.
pubmed: 12883005
pmcid: 170937
doi: 10.1073/pnas.1530509100
Yang J, Fizazi K, Peleg S, Sikes CR, Raymond AK, Jamal N, et al. Prostate cancer cells induce osteoblast differentiation through a Cbfa1-dependent pathway. Cancer Res. 2001;61:5652–9.
pubmed: 11454720
Efstathiou E, Titus M, Wen S, Hoang A, Karlou M, Ashe R, et al. Molecular characterization of enzalutamide-treated bone metastatic castration-resistant prostate cancer. Eur Urol. 2015;67:53–60.
pubmed: 24882673
doi: 10.1016/j.eururo.2014.05.005
Blighe K, Rana S, Lewis M Enhanced Volcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.6.0, https://github.com/kevinblighe/EnhancedVolcano . 2020.
Wickham H ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org . 2016.
Hakimi AA, Reznik E, Lee CH, Creighton CJ, Brannon AR, Luna A, et al. An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell. 2016;29:104–16.
pubmed: 26766592
pmcid: 4809063
doi: 10.1016/j.ccell.2015.12.004
Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.
pubmed: 24071849
pmcid: 3919969
doi: 10.1038/ng.2764
Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675–8.
pubmed: 32444850
pmcid: 7386072
doi: 10.1038/s41587-020-0546-8
Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173:400–16.e11.
pubmed: 29625055
pmcid: 6066282
doi: 10.1016/j.cell.2018.02.052
Ross-Adams H, Lamb AD, Dunning MJ, Halim S, Lindberg J, Massie CM, et al. Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study. EBioMedicine. 2015;2:1133–44.
pubmed: 26501111
pmcid: 4588396
doi: 10.1016/j.ebiom.2015.07.017
Abida W, Cyrta J, Heller G, Prandi D, Armenia J, Coleman I, et al. Genomic correlates of clinical outcome in advanced prostate cancer. Proc Natl Acad Sci USA. 2019;116:11428–36.
pubmed: 31061129
pmcid: 6561293
doi: 10.1073/pnas.1902651116
Kassambara A, Kosinski M, Biecek P survminer: Drawing survival curves using “ggplot2” 2019 [Available from: https://rpkgs.datanovia.com/survminer/ ].
Budczies J, Klauschen F, Sinn BV, Győrffy B, Schmitt WD, Darb-Esfahani S, et al. Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization. PLoS One. 2012;7:e51862.
pubmed: 23251644
pmcid: 3522617
doi: 10.1371/journal.pone.0051862