Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The Akt/mTOR and MNK/eIF4E pathways rewire the prostate cancer translatome to secrete HGF, SPP1 and BGN and recruit suppressive myeloid cells

Nature Cancer volume 4, pages 1102–1121 (2023)Cite this article

Subjects

Abstract

Cancer is highly infiltrated by myeloid-derived suppressor cells (MDSCs). Currently available immunotherapies do not completely eradicate MDSCs. Through a genome-wide analysis of the translatome of prostate cancers driven by different genetic alterations, we demonstrate that prostate cancer rewires its secretome at the translational level to recruit MDSCs. Among different secreted proteins released by prostate tumor cells, we identified Hgf, Spp1 and Bgn as the key factors that regulate MDSC migration. Mechanistically, we found that the coordinated loss of Pdcd4 and activation of the MNK/eIF4E pathways regulate the mRNAs translation of Hgf, Spp1 and Bgn. MDSC infiltration and tumor growth were dampened in prostate cancer treated with the MNK1/2 inhibitor eFT508 and/or the AKT inhibitor ipatasertib, either alone or in combination with a clinically available MDSC-targeting immunotherapy. This work provides a therapeutic strategy that combines translation inhibition with available immunotherapies to restore immune surveillance in prostate cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genome-wide analysis of the translatome is exploited to identify the extracellular interactome of prostate cancer - PMN-MDSCs.
Fig. 2: HGF, SPP1 and BGN are the most expressed, translationally regulated secreted factors in Pten-null-driven prostate cancer.
Fig. 3: HGF, SPP1 and BGN induce PMN-MDSC migration and a T-cell suppressive function.
Fig. 4: PDCD4 and phospho-eIF4E control the translation of Hgf, Spp1 and Bgn.
Fig. 5: eFT508 treatment inhibits tumor growth and PMN-MDSC infiltration in Ptenpc−/−;Trp53pc−/− prostate cancer.
Fig. 6: Dual eFT508 and ipatasertib treatment dampens tumor growth, tumor-infiltrating PMN-MDSCs and increases CD8+ T cells in Ptenpc−/−Trp53pc−/− prostate cancer.
Fig. 7: eFT508 or ipatasertib enhances the antitumor immune response of AZD5069 in Ptenpc−/−;TMPRSS2/Ergpc+/+ prostate cancer.
Fig. 8: BGN, SPP1 and HGF are highly expressed in human prostate cancer and correlate with p-eIF4E and CD33 density.

Similar content being viewed by others

Data availability

RNA-seq data that support the findings of this study have been deposited in ArrayExpress under accession code E-MTAB-9624 (RNA-seq on total RNA of wild-type, Ptenpc−/−, Ptenpc−/−;P53pc −/−) and Gene Expression Omnibus (GEO) under accession code GSE202910 (RNA-seq on total RNA of Pten pc−/−; TMPRSS2/Ergpc+/+; Pten pc−/−; CDCP1pc +/+; Ptenpc−/−; Timp1−/−; RNA-seq on polysomal RNA of wild-type, Ptenpc−/−, Ptenpc−/−;P53pc −/−, Pten pc−/−; TMPRSS2/Ergpc+/+; Pten pc−/−; CDCP1pc +/+; Ptenpc−/−; Timp1−/−).

The data published in the Array Express are the results of the RNA-seq on total RNA of the same samples for which the results of the RNA-seq on polysomal RNA are published in GEO and they were processed at the same time.

GEO accession code GSE202907 contains data for RNA-seq on total and polysomal RNA of undifferentiated bone marrow, PMN-MDSCs (CD11b+/Ly6Ghigh/Ly6Clow) and M-MDSCs (CD11b+/Ly6Gneg/Ly6Clow).

The datasets used in this study were Uniprot, https://www.uniprot.org/; the Human Protein Atlas, https://www.proteinatlas.org/; STRING, https://string-db.org/; and DAVID v.6.8, https://david.ncifcrf.gov/.

The human prostate cancer transcriptomic data were derived from the TCGA Research Network at http://cancergenome.nih.gov/ and elsewhere25.

Source Data for Figs. 1–8 and Extended Data Figs. 2–10 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

All packages used for the bioinformatics analysis are described in Methods.

References

  1. Kwon, E. D. et al. Ipilimumab versus placebo after radiotherapy in patients with metastatic castration-resistant prostate cancer that had progressed after docetaxel chemotherapy (CA184-043): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol. 15, 700–712 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. de Bono, J. S. et al. Prostate carcinogenesis: inflammatory storms. Nat. Rev. Cancer 20, 455–469 (2020).

    Article  PubMed  Google Scholar 

  3. Feng, S. et al. Myeloid-derived suppressor cells inhibit T cell activation through nitrating LCK in mouse cancers. Proc. Natl Acad. Sci. USA 115, 10094–10099 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Veglia, F., Sanseviero, E. & Gabrilovich, D. I. Myeloid-derived suppressor cells in the era of increasing myeloid cell diversity. Nat. Rev. Immunol. 21, 485–498 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Di Mitri, D. et al. Tumour-infiltrating Gr-1+ myeloid cells antagonize senescence in cancer. Nature 515, 134–137 (2014).

    Article  PubMed  Google Scholar 

  6. Calcinotto, A. et al. IL-23 secreted by myeloid cells drives castration-resistant prostate cancer. Nature 559, 363–369 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lu, X. et al. Effective combinatorial immunotherapy for castration-resistant prostate cancer. Nature 543, 728–732 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Porembka, M. R. et al. Pancreatic adenocarcinoma induces bone marrow mobilization of myeloid-derived suppressor cells which promote primary tumor growth. Cancer Immunol. Immunother. 61, 1373–1385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang, L. et al. Increased myeloid-derived suppressor cells in gastric cancer correlate with cancer stage and plasma S100A8/A9 proinflammatory proteins. J. Immunol. 190, 794–804 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Donkor, M. K. et al. Mammary tumor heterogeneity in the expansion of myeloid-derived suppressor cells. Int. Immunopharmacol. 9, 937–948 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Leibowitz-Amit, R. et al. Clinical variables associated with PSA response to abiraterone acetate in patients with metastatic castration-resistant prostate cancer. Ann. Oncol. 25, 657–662 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lorente, D. et al. Baseline neutrophil-lymphocyte ratio (NLR) is associated with survival and response to treatment with second-line chemotherapy for advanced prostate cancer independent of baseline steroid use. Ann. Oncol. 26, 750–755 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Templeton, A. J. et al. Simple prognostic score for metastatic castration-resistant prostate cancer with incorporation of neutrophil-to-lymphocyte ratio. Cancer 120, 3346–3352 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Kaur, H. B. et al. Association of tumor-infiltrating T-cell density with molecular subtype, racial ancestry and clinical outcomes in prostate cancer. Mod. Pathol. 31, 1539–1552 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Soest, R. J. et al. Neutrophil-to-lymphocyte ratio as a prognostic biomarker for men with metastatic castration-resistant prostate cancer receiving first-line chemotherapy: data from two randomized phase III trials. Ann. Oncol. 26, 743–749 (2015).

    Article  PubMed  Google Scholar 

  16. Sharma, J. et al. Elevated IL-8, TNF-α, and MCP-1 in men with metastatic prostate cancer starting androgen-deprivation therapy (ADT) are associated with shorter time to castration-resistance and overall survival. Prostate 74, 820–828 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Chi, N., Tan, Z., Ma, K., Bao, L. & Yun, Z. Increased circulating myeloid-derived suppressor cells correlate with cancer stages, interleukin-8 and -6 in prostate cancer. Int. J. Clin. Exp. Med. 7, 3181–3192 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. Lopez-Bujanda, Z. A. et al. Castration-mediated IL-8 promotes myeloid infiltration and prostate cancer progression. Nat. Cancer 2, 803–818 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nicholls, D. J. et al. Pharmacological characterization of AZD5069, a slowly reversible CXC chemokine receptor 2 antagonist. J. Pharmacol. Exp. Ther. 353, 340–350 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Dominguez, C., McCampbell, K. K., David, J. M. & Palena, C. Neutralization of IL-8 decreases tumor PMN-MDSCs and reduces mesenchymalization of claudin-low triple-negative breast cancer. JCI Insight https://doi.org/10.1172/jci.insight.94296 (2017).

  21. Silvera, D., Formenti, S. C. & Schneider, R. J. Translational control in cancer. Nat. Rev. Cancer 10, 254–266 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Piccirillo, C. A., Bjur, E., Topisirovic, I., Sonenberg, N. & Larsson, O. Translational control of immune responses: from transcripts to translatomes. Nat. Immunol. 15, 503–511 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Jamaspishvili, T. et al. Clinical implications of PTEN loss in prostate cancer. Nat. Rev. Urol. 15, 222–234 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. The Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

    Article  PubMed Central  Google Scholar 

  25. Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hsieh, A. C. et al. The translational landscape of mTOR signalling steers cancer initiation and metastasis. Nature 485, 55–61 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bhat, M. et al. Targeting the translation machinery in cancer. Nat. Rev. Drug Discov. 14, 261–278 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Chu, J., Cargnello, M., Topisirovic, I. & Pelletier, J. Translation initiation factors: reprogramming protein synthesis in cancer. Trends Cell Biol. 26, 918–933 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Furic, L. et al. eIF4E phosphorylation promotes tumorigenesis and is associated with prostate cancer progression. Proc. Natl Acad. Sci. USA 107, 14134–14139 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xu, Y. et al. Translation control of the immune checkpoint in cancer and its therapeutic targeting. Nat. Med. 25, 301–311 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Knight, J. R. P. et al. MNK inhibition sensitizes KRAS-mutant colorectal cancer to mTORC1 inhibition by reducing eIF4E Phosphorylation and c-MYC expression. Cancer Discov. 11, 1228–1247 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Huang, F. et al. Inhibiting the MNK1/2-eIF4E axis impairs melanoma phenotype switching and potentiates antitumor immune responses. J. Clin. Invest. https://doi.org/10.1172/JCI140752 (2021).

  33. Robichaud, N. et al. Phosphorylation of eIF4E promotes EMT and metastasis via translational control of SNAIL and MMP-3. Oncogene 34, 2032–2042 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Robichaud, N., Sonenberg, N., Ruggero, D. & Schneider, R. J. Translational control in cancer. Cold Spring Harb. Perspect. Biol. https://doi.org/10.1101/cshperspect.a032896 (2019).

  35. Schmid, T. et al. Translation inhibitor Pdcd4 is targeted for degradation during tumor promotion. Cancer Res. 68, 1254–1260 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Parsyan, A. et al. mRNA helicases: the tacticians of translational control. Nat. Rev. Mol. Cell Biol. 12, 235–245 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Sheth, S. et al. Resveratrol reduces prostate cancer growth and metastasis by inhibiting the Akt/MicroRNA-21 pathway. PLoS ONE 7, e51655 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tomlins, S. A. et al. Integrative molecular concept modeling of prostate cancer progression. Nat. Genet. 39, 41–51 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Sinha, A. et al. The proteogenomic landscape of curable prostate cancer. Cancer Cell 35, 414–427 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Fabbri, L., Chakraborty, A., Robert, C. & Vagner, S. The plasticity of mRNA translation during cancer progression and therapy resistance. Nat. Rev. Cancer 21, 558–577 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Rajasekhar, V. K. et al. Oncogenic Ras and Akt signaling contribute to glioblastoma formation by differential recruitment of existing mRNAs to polysomes. Mol. Cell 12, 889–901 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Chen, Y. et al. ETS factors reprogram the androgen receptor cistrome and prime prostate tumorigenesis in response to PTEN loss. Nat. Med. 19, 1023–1029 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen, Z. et al. Crucial role of p53-dependent cellular senescence in suppression of Pten-deficient tumorigenesis. Nature 436, 725–730 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Alajati, A. et al. CDCP1 overexpression drives prostate cancer progression and can be targeted in vivo. J. Clin. Invest. 130, 2435–2450 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Guccini, I. et al. Senescence reprogramming by TIMP1 deficiency promotes prostate cancer metastasis. Cancer Cell 39, 68–82 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  PubMed  Google Scholar 

  48. Rozen, F. et al. Bidirectional RNA helicase activity of eucaryotic translation initiation factors 4A and 4F. Mol. Cell. Biol. 10, 1134–1144 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Cerezo, M. et al. Translational control of tumor immune escape via the eIF4F-STAT1-PD-L1 axis in melanoma. Nat. Med. 24, 1877–1886 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Fu, J. et al. HGF/c-MET pathway in cancer: from molecular characterization to clinical evidence. Oncogene 40, 4625–4651 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Appunni, S. et al. Biglycan: an emerging small leucine-rich proteoglycan (SLRP) marker and its clinicopathological significance. Mol. Cell. Biochem. 476, 3935–3950 (2021).

    Article  CAS  PubMed  Google Scholar 

  52. Zhao, H. et al. The role of osteopontin in the progression of solid organ tumour. Cell Death Dis. 9, 356 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bolis, M. et al. Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression. Nat. Commun. 12, 7033 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Reich, S. H. et al. Structure-based design of pyridone-aminal eFT508 targeting dysregulated translation by selective mitogen-activated protein kinase interacting kinases 1 and 2 (MNK1/2) inhibition. J. Med. Chem. 61, 3516–3540 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Lin, J. et al. Targeting activated Akt with GDC-0068, a novel selective Akt inhibitor that is efficacious in multiple tumor models. Clin. Cancer Res. 19, 1760–1772 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. de Bono, J. S. et al. Randomized phase II study evaluating Akt blockade with ipatasertib, in combination with abiraterone, in patients with metastatic prostate cancer with and without PTEN loss. Clin. Cancer Res. 25, 928–936 (2019).

    Article  PubMed  Google Scholar 

  57. Sweeney, C. et al. Ipatasertib plus abiraterone and prednisolone in metastatic castration-resistant prostate cancer (IPATential150): a multicentre, randomised, double-blind, phase 3 trial. Lancet 398, 131–142 (2021).

    Article  CAS  PubMed  Google Scholar 

  58. de Wit, R. et al. Baseline neutrophil-to-lymphocyte ratio as a predictive and prognostic biomarker in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel versus abiraterone or enzalutamide in the CARD study. ESMO Open 6, 100241 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Rebello, R. J. et al. Prostate cancer. Nat. Rev. Dis. Primers 7, 9 (2021).

    Article  PubMed  Google Scholar 

  60. Wei, S. C., Duffy, C. R. & Allison, J. P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov. 8, 1069–1086 (2018).

    Article  PubMed  Google Scholar 

  61. Rescigno, P. & de Bono, J. S. Immunotherapy for lethal prostate cancer. Nat. Rev. Urol. 16, 69–70 (2019).

    Article  PubMed  Google Scholar 

  62. Aguilar-Valles, A. et al. Translational control of depression-like behavior via phosphorylation of eukaryotic translation initiation factor 4E. Nat. Commun. 9, 2459 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Genuth, N. R. & Barna, M. Heterogeneity and specialized functions of translation machinery: from genes to organisms. Nat. Rev. Genet. 19, 431–452 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hilliard, A. et al. Translational regulation of autoimmune inflammation and lymphoma genesis by programmed cell death 4. J. Immunol. 177, 8095–8102 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. Dorrello, N. V. et al. S6K1- and βTRCP-mediated degradation of PDCD4 promotes protein translation and cell growth. Science 314, 467–471 (2006).

    Article  CAS  PubMed  Google Scholar 

  66. Asangani, I. A. et al. MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene 27, 2128–2136 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Sheedy, F. J. et al. Negative regulation of TLR4 via targeting of the proinflammatory tumor suppressor PDCD4 by the microRNA miR-21. Nat. Immunol. 11, 141–147 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Cho, H. et al. RapidCaP, a novel GEM model for metastatic prostate cancer analysis and therapy, reveals myc as a driver of Pten-mutant metastasis. Cancer Discov. 4, 318–333 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Marigo, I. et al. Tumor-induced tolerance and immune suppression depend on the C/EBPβ transcription factor. Immunity 32, 790–802 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Pernigoni, N. et al. Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis. Science 374, 216–224 (2021).

    Article  CAS  PubMed  Google Scholar 

  71. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  72. Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Oertlin, C. et al. Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res. 47, e70 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Franceschini, A. et al. STRING v9.1: protein–protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).

    Article  CAS  PubMed  Google Scholar 

  76. Swaim, C. D., Scott, A. F., Canadeo, L. A. & Huibregtse, J. M. Extracellular ISG15 signals cytokine secretion through the LFA-1 integrin receptor. Mol Cell 68, 581–590 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Sadik, C. D., Miyabe, Y., Sezin, T. & Luster, A. D. The critical role of C5a as an initiator of neutrophil-mediated autoimmune inflammation of the joint and skin. Semin. Immunol. 37, 21–29 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. Lauria, F. et al. SMN-primed ribosomes modulate the translation of transcripts related to spinal muscular atrophy. Nat. Cell Biol. 22, 1239–1251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Tebaldi, T. et al. Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells. BMC Genomics 13, 220 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Lauria, F. et al. riboWaltz: optimization of ribosome P-site positioning in ribosome profiling data. PLoS Comput. Biol. 14, e1006169 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  82. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-ΔΔC(T)) method. Methods 25, 402–408 (2001).

    Article  CAS  PubMed  Google Scholar 

  83. Welti, J. et al. Targeting bromodomain and extra-terminal (BET) family proteins in castration-resistant prostate cancer (CRPC). Clin. Cancer Res. 24, 3149–3162 (2018).

    Article  CAS  PubMed  Google Scholar 

  84. Gil, V. et al. HER3 is an actionable target in advanced prostate cancer. Cancer Res. 81, 6207–6218 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhong, Q. et al. Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity. Sci Rep. 6, 24146 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Antonarakis, E. S. et al. An immunohistochemical signature comprising PTEN, MYC, and Ki67 predicts progression in prostate cancer patients receiving adjuvant docetaxel after prostatectomy. Cancer 118, 6063–6071 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge all the members of the A.A. laboratory for scientific discussions and critical reading. We acknowledge all the patients who participated in the study protocols. I.G. was, in part, funded by a SAKK Translational Urogenital Cancer Meeting Award. This work was supported by ERC consolidator (CoG683136) grant, Prostate Cancer Foundation (PCF Challenge Award 19CHAL08), Swiss Card-Onco-Grant of Alfred and Annemarie von Sick, Horten Foundation, Prix Robert Wenner, Ligue Suisse contre le Cancer and ISREC Foundation (to A.A.).

Author information

Author notes

  1. Adele Ponzoni

    Present address: Ima Biotech, Lille, France

  2. Michela Mirenda

    Present address: Evotec, Toulouse, France

  3. Mariantonietta D’Ambrosio

    Present address: Imperial College London, London, UK

  4. Ilaria Guccini

    Present address: Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland

  5. Ajinkya Revandkar

    Present address: Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, MA, USA

  6. Abdullah Alajati

    Present address: Department of Urology, Universitätklinikum Bonn, Bonn, Germany

  7. Nahjme Parhizgari

    Present address: Biosun Pharmed, Kordan, Iran

Authors and Affiliations

  1. Institute of Oncology Research, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland

    Daniela Brina, Adele Ponzoni, Martina Troiani, Bianca Calì, Emiliano Pasquini, Giuseppe Attanasio, Simone Mosole, Michela Mirenda, Mariantonietta D’Ambrosio, Manuel Colucci, Ilaria Guccini, Ajinkya Revandkar, Abdullah Alajati, Nahjme Parhizgari, Aurora Valdata, Martino Maddalena, Arianna Calcinotto, Andrea Rinaldi & Andrea Alimonti

  2. Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland

    Martina Troiani, Marco Bolis & Andrea Alimonti

  3. Yale Cancer Center and Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA

    Toma Tebaldi

  4. Institute of Biophysics, CNR Unit at Trento, Povo, Italy

    Deborah Donzel, Fabio Lauria & Gabriella Viero

  5. Bioinformatics Core Unit, Swiss Institute of Bioinformatics, Bellinzona, Switzerland

    Marco Bolis

  6. Computational Oncology Unit, Department of Oncology, Istituto di Richerche Farmacologiche ‘Mario Negri’ IRCCS, Milano, Italy

    Marco Bolis

  7. IMED Oncology AstraZeneca, Li Ka Shing Centre, Cambridge, UK

    Simon Barry

  8. Department of Pathology and Molecular Pathology, University Hospital Zurich (USZ), Zurich, Switzerland

    Jan Hendrik Rüschoff & Holger Moch

  9. Veneto Institute of Oncology, IOV-IRCCS, Padua, Italy

    Marianna Sabbadin & Matteo Fassan

  10. Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK

    Semini Sumanasuriya, Mateus Crespo, Adam Sharp, Wei Yuan & Johann de Bono

  11. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA

    Mathew Grinu, Alexandra Boyle, Cynthia Miller & Lloyd Trotman

  12. 2cSysBioMed, TI, Contra, Switzerland

    Nicolas Delaleu

  13. Department of Medicine (DIMED), Surgical Pathology Unit, University of Padua, Padua, Italy

    Matteo Fassan

  14. The Royal Marsden Hospital, London, UK

    Johann de Bono

  15. Department of Medicine, Venetian Institute of Molecular Medicine, University of Padova, Padova, Italy

    Andrea Alimonti

  16. Department of Health Sciences and Technology, Eidgenössische Technische Hochschule (ETH) Zürich, Zurich, Switzerland

    Andrea Alimonti

Authors
  1. Daniela Brina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adele Ponzoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martina Troiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bianca Calì
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emiliano Pasquini
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Giuseppe Attanasio
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Simone Mosole
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michela Mirenda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mariantonietta D’Ambrosio
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Manuel Colucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ilaria Guccini
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ajinkya Revandkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Abdullah Alajati
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Toma Tebaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Deborah Donzel
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Fabio Lauria
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Nahjme Parhizgari
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Aurora Valdata
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Martino Maddalena
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Arianna Calcinotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Marco Bolis
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Andrea Rinaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Simon Barry
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jan Hendrik Rüschoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Marianna Sabbadin
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Semini Sumanasuriya
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Mateus Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Adam Sharp
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Wei Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Mathew Grinu
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Alexandra Boyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Cynthia Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Lloyd Trotman
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Nicolas Delaleu
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Matteo Fassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Holger Moch
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Gabriella Viero
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Johann de Bono
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Andrea Alimonti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.A. and D.B. conceived and designed the project. D.B., A.P., M.M. and N.P. performed experiments. M.T. performed the bioinformatic analysis. B.C. contributed to data discussion and revision of the manuscript. E.P. and G.A. took care of genotyping the animals, performed the in vivo treatments and tumor measurement. S.M. performed the immunohistochemical experiments. M.D. and A.A. developed the Ptenpc−/−; CDCP1pc+/+ mouse model. M.C. analyzed the Incucyte-derived data. I.G. and A.Revandkar developed the Ptenpc−/−;Timp1−/− mouse model. T.T. performed the bioinformatic analysis related to the 5′ UTR length and folding energy. D.D., F.L. and G.V. performed and analyzed the Ribo-seq. A.V. performed the in vivo treatments and tumor measurement. M.M. revised the manuscript. A.C. performed preliminary immunophenotyping experiments. M.B. supervised M.T. A. Rinaldi checked the quality of the RNA. S.B. provided the AZD5069 compound. J.H.R. and H.M. provided the human tissue microarray of the cohort 1. M.S. and M.F. provided the human tissue microarray of the cohort 2. S.S., M.C., W.Y., A.S. and J.d.B. selected and provided human CRPC and patient-derived xenograft samples. M.G., A.B., C.M. and L.T. provided the Pten−/−; Trp53−/− RapidCap cell line. N.D. performed preliminary bioinformatic analysis. D.B. and A.A. interpreted the data and wrote the paper.

Corresponding author

Correspondence to Andrea Alimonti.

Ethics declarations

Competing interests

S.B. is affiliated with IMED Oncology AstraZeneca, Li Ka Shing Centre, Cambridge, UK and provided the AZD5069 compound. Johann de Bono has served on Advisory Boards for Roche and AstraZeneca and he is an employee of the ICR, which has received funding or other support for his research work from AstraZeneca and which has a commercial interest in PI3K/AKT pathway inhibitors (no personal income). Johann de Bono and Andrea Alimonti are principal investigators of the NCT03177187 trial which was partially supported by AstraZeneca and Astellas Pharma. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Tracy McGaha, Ping Mu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Polysome profiling analysis in Pten-null-driven prostate cancer.

a, Polysome profiles of wild-type prostate, Ptenpc−/−, Ptenpc−/−;TMPRSS2/Ergpc+/+, Ptenpc−/−;CDCP1pc+/+, Ptenpc−/−;Timp1−/− and Ptenpc−/−;Trp53pc−/− prostate cancer. RNA-seq was performed on polysome-bound RNAs and total RNA derived from the prostate of three mice for each genetic background for a total of 18 samples. b, PCA plots of total (T) and polysomal RNA (P) fractions for each analyzed genetic background (n = 3 mice for each genetic background for a total of 18 samples). c, PCA plots of total and polysomal RNA fractions for each genetic background analyzed, corrected for the batch effect (n = 3 mice for each genetic background for a total of 18 samples). d, Scatter plots of fold-changes for polysome-associated and total mRNA levels for the comparisons between the indicated genetic backgrounds and wild-type prostates showing mRNAs with upregulated translation efficiency (red), downregulated translation efficiency, buffering up (pink), buffering down (blue) (n = 3 mice for each genetic background for a total of 18 samples). Details are provided in Supplementary Table 1 and Supplementary Table 2.

Extended Data Fig. 2 Polysome profiling analysis in Pten-null-driven prostate cancer and bone marrow-derived MDSCs.

a, Schematic representation of the polysome profiling analysis performed by selecting mRNAs changes in common among the five different genetic backgrounds analyzed (Ptenpc−/−, Ptenpc−/−; TMPRSS2/Ergpc+/+, Ptenpc−/−;CDCP1pc+/+, Ptenpc−/−;Timp1−/− and Ptenpc−/−;Trp53pc−/− prostate cancer) and wild-type prostate. Using this approach, 1072 polysomes-bound mRNAs were upregulated in the polysomal RNA pool (threshold Log2 FC > 1.0; FDR < 0.05), 776 total mRNA were upregulated in the total RNA pool (threshold Log2 FC > 1.0; FDR < 0.05) and 247 mRNAs were found translationally up-regulated (threshold for polysomal mRNA expression Log2 FC ≥ 1.0; FDR < 0.05: threshold for translation efficiency Log2 FC ≥ 0.5; FDR < 0.1) in each genetic background compared to wild-type prostate. b, Gene Ontology biological processes enriched among the upregulated mRNAs in the polysomes-bound pool (upper panel) and in the total RNA pool (lower panel) in Ptenpc−/−, Ptenpc−/−;TMPRSS2/Ergpc+/+; Ptenpc−/−;CDCP1pc+/+, Ptenpc−/−;Timp1−/− and Ptenpc−/−;Trp53pc−/− prostate cancer compared to wild-type prostate, determined by DAVID software (n = 3 mice for each genetic background for a total of 18 samples). Log10 adjusted p-values by using the linear step-up method of Benjamini is reported. c, Scheme of the differentiation protocol of bone marrow-derived MDSCs; FACS plot of the gating strategy of bone marrow-derived MDSCs, sorted in CD11b+/Ly6Ghigh/Ly6Clow PMN-MDSCs and CD11b+/Ly6Gneg/Ly6Chigh M-MDSCs after 5 days of differentiation with 40 ng/ml GM-CSF and 40 ng/ml IL-6 in RPMI plus 10% FBS medium (top); polysome profiles of undifferentiated bone marrow (middle) CD11b+/Ly6Ghigh/Ly6Clow PMN-MDSCs (bottom left) and CD11b+/Ly6Gneg/Ly6Chigh M-MDSCs (bottom right). RNA-seq was performed on polysome-bound RNAs and total RNA derived from three biological replicates.

Source data

Extended Data Fig. 3 BGN, SPP1 and HGF are upregulated in Pten null-driven prostate cancer compared to wild-type prostate.

a, Graphs showing the CPM of Bgn, Spp1 and Hgf in wild-type prostate and Ptenpc−/− prostate cancer determined by Ribo-seq analysis (left panel). Data are presented as mean values +/− SEM of n = 3 mice for each genotype. P values were computed by one-tailed quasi-likelihood F-test and are indicated at the top of the graph. Ribosome occupancy in wild-type prostate (grey) and Ptenpc−/− prostate cancer (red) determined by Ribo-seq analysis (right panel). Each profile represents the mean normalized coverage among n = 3 mice for each genotype. The structure of the transcript, showing the boundaries of CDS and UTR regions, is outlined below each profile. b, Western blot showing the protein levels of BGN, SPP1 and HGF in wild-type prostates, Ptenpc−/− and Ptenpc−/−;Trp53pc−/− prostate cancers. Densitometry values normalized to the respective loading control are indicated for each band. See quantification for the indicated number of mice in Ext Data Fig. 3c. c, Densitometric analysis of BGN, SPP1 and HGF in wild-type prostates, Ptenpc−/− and Ptenpc−/−;Trp53pc−/− prostate cancers (from the left, BGN n = 17, n = 7, n = 14; SPP1 n = 10, n = 8, n = 12; HGF n = 11, n = 6, n = 6). Data are mean ± SD. d, Percentage of tumor-infiltrating CD45+/CD11b+/ Ly6Ghigh/Ly6Clow cells (PMN-MDSCs) in wild-type, Ptenpc−/− and Ptenpc−/−;Trp53pc−/− prostate cancers (from the left, n = 5, n = 4, n = 5 derived from the analysis of Fig. 1a. Data are mean ± SD. Statistical analysis between all groups in (c) and (d): (ordinary one-way ANOVA followed by Tukey’s multiple comparisons test).

Source data

Extended Data Fig. 4 TLR2, CD44 and MET expression correlates with the PMN-MDSCs signature in human prostate cancer and CRPC.

a, Western blot showing the protein levels of BGN, SPP1 and HGF in epithelial (EpCAM+ cells), immune (CD45+ cells) and stromal fraction (EpCAM-, CD45- cells) of Ptenpc−/−;Trp53pc−/− prostate cancer. Densitometry values normalized to the respective loading control are indicated for each band. The experiment was repeated two independent times with similar results. b, Mean fluorescence intensity (MFI) of the indicated receptors in tumor-infiltrating immune cells subsets and EpCAM+ cells in Ptenpc−/−;Trp53pc−/− prostate cancers. At least n = 3 biologically independent samples. Data are mean ± SD. c, FACS plots of TLR2, CD44 and MET receptor expression in bone marrow-derived MDSCs; green signal: MDSCs stained with fluorescence minus one control, violet signal: MDSCs stained with the specific antibody. d, Correlation of TLR2 (left panel), CD44 (middle panel) and MET (right panel) expression with the PMN-MDSCs signature in primary prostate cancer and CRPC. Pearson correlation and p value are indicated at the top of the graph. TLR2: 95 % confidence interval 0.734- 0.785; CD44: 95 % confidence interval: 0.180-0.295; MET: 95 % confidence interval: 0.156-0.273 e, BGN, SPP1 and HGF protein levels determined in Pten−/−;Trp53−/− (RapidCap)-derived conditioned medium by ELISA assay. n = 3 biologically independent samples. Data are mean ± SD. Statistical analysis (unpaired two-sided Student’s t-test). f, Arg1, Nos2, Vsir and Cd274 mRNA expression levels in bone marrow-derived MDSCs pretreated with recombinant BGN, SPP1 and HGF for 24 hours. n = 2 (Arg1), n = 3 (Nos2), n = 3 (Vsir), n = 2 (Cd274) biologically independent samples. Data are mean ± SD. Statistical analysis (two-way ANOVA followed by Dunnett’s multiple comparisons test). g, Growth curve of scramble and Hgf/Spp1/Bgn triple KD Pten−/−;Trp53−/− (RapidCap) cells. Data are mean ± SEM. The experiment was repeated two independent times with similar results. h, Western blot showing HGF, SPP1 and BGN protein levels in scramble and triple KD Pten−/−;Trp53−/− (RapidCap)- cell lines used for the in vivo experiments. Densitometry values normalized to the respective loading control are indicated for each band. i, Tumor growth of scramble and Hgf/Spp1/Bgn triple KD in Pten−/−;Trp53−/− (RapidCap) - allografts (for all groups, n = 5 in each group). Data are mean ± SEM. Statistical analysis (multiple unpaired student t test). j, Representative IHC of Gr-1 and CD3 in scramble and Hgf/Spp1/Bgn triple KD Pten−/−;Trp53−/− (RapidCap)- allografts. Scale bar 50 μm. (n = 5 mice in each group). k, Representative IHC of Gr-1 and CD3 in vehicle-treated (n = 4 mice) and recombinant Bgn/Spp1/Hgf-treated (n = 6 mice) TRAMP-C1 allografts. Scale bar 50 μm.

Source data

Extended Data Fig. 5 PDCD4 inhibits eIF4F complex formation and cooperates with eFT508 to reduce Hgf, Spp1 and Bgn levels.

a, Western blot showing the protein levels of PTEN, pSer473-AKT, AKT total, pSer235-S6, eIF4E, MNK1 and representative HSP90 in wild-type prostate and Ptenpc−/−;Trp53pc−/− prostate cancer. The experiment was performed once with n = 3 mice for each group. b, Heatmap showing PDCD4 mRNA levels in the indicated genetic background of prostate cancer compared to wild-type prostate (total mRNA expression determined by RNA-seq). c, Western blot showing the levels of Pten and PDCD4 in the indicated settings (top). Cap pull-down assay showing the levels of eIF4G, eIF4A, eIF4E and p-eIF4E in input, cap pull-down and sepharose control beads. Densitometry values of the cap pull-down normalized to the input are indicated for each band (bottom). d, Western blot showing the levels of p-eIF4E and eIF4E after RNA immunoprecipitation with the respective antibody in Pten-sh TC1 prostate cancer cells. e, Western blot showing the levels of HGF, SPP1, BGN, p-eIF4E and representative HSP90 in Pten-sh TC1 cell line upon the indicated concentration of eFT508. f, Western blot showing the levels of HGF, SPP1, BGN, p-eIF4E, PDCD4 and representative HSP90 in Pten-sh TC1 cell line upon 500 nM eFT508 treatment and PDCD4 rescue. g, Polysome profiles of vehicle, 500 nM eFT508-treated, Pdcd4-overexpressing Pten-sh cells and eFT508-treated / Pdcd4-overexpressing Pten-sh cells. h, Distribution of Hgf, Spp1 and Bgn mRNA levels in the fractions derived from the sucrose gradient fractionation in Pten-sh TC1 cells, determined by qRT-PCR (n = 5 independent experiments for Hgf and Spp1; n = 4 independent experiments for Bgn; n = 3 independent experiments for Actinb). The percentages of Hgf, Spp1 and Bgn mRNA distributed in each fraction are shown. v = vehicle; e = eFT508; p = pdcd4; e + p = eFT508 + pdcd4. Data are mean ± SD. Statistical analysis between all groups (ordinary two-way ANOVA followed by Tukey’s multiple comparisons test). i, Western blot showing the levels of PDCD4 and p-eIF4E in human PC3 prostate cancer cell line. j, Translation efficiency (polysomal mRNA expression/ total mRNA expression) of HGF, SPP1, BGN, ISG15 and PDGFB upon 500 nM eFT508 treatment and PDCD4 rescue in human PC3 prostate cancer cell line (n = 3 independent experiments). Data are mean ± SD. Statistical analysis between all groups: (RM one-way ANOVA followed by Tukey’s multiple comparisons test). Densitometry values normalized to the housekeeping are indicated for each band in (a) and (e-f) and (i). The experiment was repeated at least two independent times with similar results in (c-f) and (i).

Source data

Extended Data Fig. 6 Prostate-specific Pdcd4 rescue inhibits tumor-infiltrating PMN-MDSCs and its loss is associated with decreased disease-free survival in human prostate cancer.

a, Growth curve of control vector (lenti ORF) and PDCD4 -overexpressing (lenti PDCD4) Pten−/−;Trp53−/− (RapidCap) prostate cancer cells, determined by the Incucyte system. Data are mean ± SEM. The experiment was repeated two independent times with similar results. b, Tumor growth of C57BL6 mice injected with 2.5 × 106 control vector or PDCD4 -overexpressing Pten−/−;Trp53−/− (RapidCap) prostate cancer cells (lenti ORF n = 10; lenti Pdcd4 n = 7 mice). Data are mean ± SEM. Statistical analysis: (two way ANOVA followed by Šídák’s multiple comparisons test). c, Western blot showing the protein levels of PDCD4, SPP1, HGF, BGN and representative HSP90 in control vector and PDCD4 -overexpressing Pten−/−;Trp53−/− (RapidCap) murine prostate cancer cells. Densitometry values normalized to the housekeeping are indicated for each band. The experiment was repeated two independent times with similar results. d, Representative IHC of Gr-1-positive cells in control vector or PDCD4 -overexpressing Pten−/−;Trp53−/− (RapidCap) allografts. Scale bar 50 μm. (n = 5 mice in each group). e, Representative FACS plot of CD45+/CD11b+/Ly6Ghigh/Ly6Clow cells (PMN-MDSCs) inside the CD45+/CD11b+ population. f, Percentage of tumor-infiltrating CD45+/CD11b+/Ly6Ghigh/Ly6Clow (PMN-MDSCs) in control vector and PDCD4-overexpressing Pten−/−;Trp53−/− (RapidCap) allografts determined by flow cytometric analysis (n = 5 mice in each group). Data are mean ± SD. Statistical analysis (Mann-Whitney test). g, Correlation between PDCD4 mRNA levels and disease-free probability in the indicated human prostate cancer datasets. h, Correlation between PDCD4 mRNA expression levels and Pten deletion/mutation in the human prostate cancer TCGA dataset. Statistical analysis: chi-square test.

Source data

Extended Data Fig. 7 eFT508 inhibits translation of Hgf, Spp1 and Bgn and impairs PMN-MDSCs migration in prostate cancer.

a, Hgf, Spp1 and Bgn mRNA levels in polysomes-bound mRNAs and total mRNAs fraction in prostate cancer of eFT508-treated and vehicle-treated Ptenpc−/−;Trp53pc−/− mice determined by qRT- PCR (n = 3 mice in each group). Data are mean ± SD. Statistical analysis (two-tailed ratio paired t-test). b, Densitometry of BGN, SPP1 and HGF protein expression levels in prostate cancer of eFT508-treated or vehicle-treated Ptenpc−/−;Trp53pc−/− mice (BGN and HGF, n = 3 mice ; SPP1, n = 4 mice). Data are mean ± SD. Statistical analysis (unpaired two-sided Student’s t-test). c, Ifng, Granzyme B (GrzmB), Perforin (Prfn) and FoxP3 mRNA levels in prostate cancer of eFT508-treated compared to vehicle-treated Ptenpc−/−;Trp53pc−/− mice determined by qRT- PCR (Ifng, GrzmB and Prfn, n = 3 mice, Foxp3 n = 4 mice). Data are mean ± SD. Statistical analysis (two-tailed ratio paired t-test). d, Number of migrated MDSCs tested in a transwell migration assay: MDSCs, previously exposed to 10% FBS, vehicle, 100 nM or 500 nM eFT508-treated Ptenpc−/−;Trp53pc−/− (RapidCap)-derived conditioned media for 24 hours, were allowed to migrate through a 5 μm-transwell to the bottom well for 6 hours toward Pten−/−;Trp53−/− (RapidCap)-derived conditioned media. The number of migrated cells was determined by flow cytometric analysis. Experiment in technical replicates performed twice with similar results. e, Number of migrated MDSCs tested in a transwell migration assay: MDSCs were allowed to migrate through a 5 μm-transwell to the bottom well for 6 hours toward 0.1% FBS media, vehicle, 100 nM or 500 nM eFT508-treated Pten−/−;Trp53−/− (RapidCap)-derived conditioned media. The number of migrated cells was determined by flow cytometric analysis. Experiment in technical replicates performed twice with similar results.

Source data

Extended Data Fig. 8 eFT508 restores T cell activation in the Ptenpc−/−;Trp53pc−/− mouse model.

a, Representative FACS plots of the CD45+/CD3 population and CD45+/CD3+/CD8+ cells upon isotype control and anti-CD8 depleting antibody. b, Representative FACS plots of the CD45+/CD11b+ population and CD45+/CD11b+/Ly6Ghigh/Ly6Clow (PMN-MDSCs) upon isotype control and anti-Ly6G depleting antibody. c, Ifng, GrzmB and Foxp3 mRNA levels in eFT508-treated and vehicle-treated Pten−/−;Trp53−/− (RapidCap) allografts determined by qRT- PCR (vehicle group, n = 4; eFT508 group, n = 4, n = 5, n = 5 mice, respectively). Data are mean ± SD. Statistical analysis (unpaired two-sided Student’s t-test). d, Growth curve analysis of Pten−/−;Trp53−/− (RapidCap) murine prostate cancer cells, LnCap and PC3 human prostate cancer cell line treated with vehicle or 1 μM, 2 μM, 5 μM eFT508, determined by the Incucyte system. Data are mean ± SEM. The experiment was repeated two independent times with similar results.

Source data

Extended Data Fig. 9 AKT inhibition increases PDCD4 levels and cooperates with eFT508 to reduce HGF, SPP1 and BGN protein levels in Pten−/−;Trp53−/− prostate cancer cells.

a, Western blot analysis showing the protein levels of PDCD4 and phospho-S6 in Pten−/−;Trp53−/− (RapidCap) and PC3 prostate cancer cell line upon treatment with the indicated concentration of ipatasertib. Densitometry values normalized to the loading control are indicated at the bottom for each band. The experiment was repeated two independent times with similar results. b, Western blot analysis showing the protein levels of PDCD4, p-S6, p-eIF4E, p-4EBP1, HSP90 (upper panel) and HGF, SPP1, BGN and representative HSP90 (lower panel) in Pten−/−;Trp53−/− (RapidCap) murine prostate cancer cell line upon treatment with vehicle, 500 nM eFT508, 500 nM ipatasertib or the dual treatment. Densitometry values normalized to the loading control are indicated at the bottom for each band. The experiment was repeated two independent times with similar results. c, Western blot analysis showing the protein levels of PDCD4, p-S6, p-eIF4E, p-4EBP1, HSP90 (upper panel) and HGF, SPP1, BGN and representative HSP90 (lower panel) in PC3 human prostate cancer cell line upon treatment with vehicle, 500 nM eFT508, 500 nM ipatasertib or the dual treatment. Densitometry values normalized to the loading control are indicated for each band. The experiment was repeated two independent times with similar results. d, Transwell migration assay performed with bone marrow-derived MDSCs tested for the capability to migrate toward PCa medium conditioned with the indicated treatments. n = 3 biological replicates. The experiment was repeated two independent times with similar results. Data are mean ± SD. Statistical analysis between all groups (ordinary one-way ANOVA followed by Tukey’s multiple comparisons test). e, Representative FACS plot of the gating strategy for the quantification of CD45+/CD11b+/Ly6Ghigh/Ly6Clow (PMN-MDSCs) (left) and CD45+/CD3+/CD8+ cells (right) in Ptenpc−/−;Trp53pc−/− prostate tumors.

Source data

Extended Data Fig. 10 BGN, SPP1 and HGF are highly expressed in CRPC and correlate with p-eIF4E protein levels.

a, Representative FACS plots of CD45+/CD11b+ population and Ly6Ghigh/Ly6Clow cells (PMN-MDSCs) inside the CD45+/CD11b+ population in AZD5069-treated and vehicle-treated Ptenpc−/−;Tmprss2/Ergpc+/+. b, Western blot analysis showing the protein levels of HGF, SPP1 and BGN in Ptenpc−/−;Tmprss2/Ergpc+/+ prostate tumors upon treatment with vehicle or AZD5069. Densitometry values normalized to the housekeeping are indicated for each band. The experiment was performed once. c, Western blot analysis showing the protein levels of CXCL5 in Ptenpc−/−;Tmprss2/Ergpc+/+ prostate tumors upon treatment with vehicle or eFT508. Densitometry values normalized to the housekeeping are indicated for each band. The experiment was repeated two independent times with similar results. d, Heatmap depicting the mRNA levels of CXCL-chemokines in prostate tumors of the indicated genotypes compared to wild-type prostates (total mRNA expression determined by RNA-seq; n = 3 mice for each genetic background). e, Representative IHC of BGN, SPP1, HGF and p-eIF4E showing negative (upper panel) and positive (lower panel) cases in CRPC in cohort 1. Scale Bar 50 μm. f, Correlation between the co-expression of ≥ 2 ligands and p-eIF4E in CRPC in cohort 1 (n = 101). Two-sided Fisher’s exact test. g, Western blot showing the protein levels of BGN, SPP1 and HGF and representative HSP90 in CRPC patient-derived xenografts. (n = 4). h, Correlation between plasma HGF levels (pg/ml), determined by ELISA assay, and neutrophil-to-lymphocyte ratio (NLR) in CRPC patients. Statistical analysis: simple linear regression.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–3

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 2

Unprocessed western blots.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 6

Statistical Source Data.

Source Data Fig. 6

Unprocessed western blots.

Source Data Fig. 7

Statistical Source Data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 8

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 3

Statistical Source Data.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Statistical Source Data.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 7

Statistical Source Data.

Source Data Extended Data Fig. 8

Statistical Source Data.

Source Data Extended Data Fig. 9

Statistical Source Data.

Source Data Extended Data Fig. 9

Unprocessed western blots.

Source Data Extended Data Fig. 10

Statistical Source Data.

Source Data Extended Data Fig. 10

Unprocessed western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brina, D., Ponzoni, A., Troiani, M. et al. The Akt/mTOR and MNK/eIF4E pathways rewire the prostate cancer translatome to secrete HGF, SPP1 and BGN and recruit suppressive myeloid cells. Nat Cancer 4, 1102–1121 (2023). https://doi.org/10.1038/s43018-023-00594-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00594-z

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer