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.

  • Resource
  • Published:

Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers

Nature Cancer volume 5, pages 1427–1447 (2024)Cite this article

Subjects

A Publisher Correction to this article was published on 01 October 2024

This article has been updated

Abstract

Prostate cancer (PCa) exhibits significant geoethnic disparities as reflected by distinct variations in the cancer genome and disease progression. Here, we perform a comprehensive proteogenomic characterization of localized high-risk PCa utilizing paired tumors and nearby tissues from 125 Chinese male patients, with the primary objectives of identifying potential biomarkers, unraveling critical oncogenic events and delineating molecular subtypes with poor prognosis. Our integrated analysis highlights the utility of GOLM1 as a noninvasive serum biomarker. Phosphoproteomics analysis reveals the crucial role of Ser331 phosphorylation on FOXA1 in regulating FOXA1-AR-dependent cistrome. Notably, our proteomic profiling identifies three distinct subtypes, with metabolic immune-desert tumors (S-III) emerging as a particularly aggressive subtype linked to poor prognosis and BCAT2 catabolism-driven PCa progression. In summary, our study provides a comprehensive resource detailing the unique proteomic and phosphoproteomic characteristics of PCa molecular pathogenesis and offering valuable insights for the development of diagnostic and therapeutic strategies.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Proteogenomic landscape of high-risk Chinese PCa.
Fig. 2: Integrated multiomic analyses of PCa.
Fig. 3: Tumor-NAT comparisons reveal tumorigenic changes and biomarker candidates.
Fig. 4: Phosphorylation of FOXA1-Ser331 is required for the activation of AR signaling.
Fig. 5: PCa proteomic subtypes and their molecular characterizations.
Fig. 6: BCAA catabolism potentiates PCa progression.

Similar content being viewed by others

Data availability

MS data have been deposited in ProteomeXchange with the primary accession code PXD049446. The genomic and transcriptomic data have been deposited in the Genome Sequence Archive database under accession code PRJCA013924. RNA-seq, Cut&Tag-seq and ATAC-seq data of C4-2 cells generated during this study have been deposited in the Gene Expression Omnibus database under accession code GSE232125. All raw datasets generated in this study have been deposited in the article repository on Figshare at https://doi.org/10.6084/m9.figshare.25706982 (ref. 100). The datasets derived from TCGA are available at the cBioPortal website (www.cbioportal.org/). CPGEA sequencing data are available in the Biological Project Library (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA001124). The AR pathway, AKT1_mTOR pathway and TP53 pathway signatures are derived from MSigDB H: hallmark. The WNT pathway signature was from MSigDB C2 CP. The E2F targets signature was from MSigDB C3 TFT. The RB pathway signature was from MSigDB C6: oncogenic signature. The Nelson_response_to_androgen_up and Wang_response_to_androgen_up signatures were from MSigDB C2 CGP. The MAPK pathway signature was from https://doi.org/10.1038/s41698-018-0051-4 (ref. 99) and Sun_AR_signature was from https://doi.org/10.1038/s41586-020-2135-x (ref. 7). All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All data analysis was conducted using published software packages, as detailed and referenced in the manuscript. No new code was generated for this study.

Change history

References

  1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  2. Rebbeck, T. R. Prostate cancer disparities by race and ethnicity: from nucleotide to neighborhood. Cold Spring Harb. Perspect Med. 8, e030387 (2018).

    Article  Google Scholar 

  3. Kimura, T. East meets West: ethnic differences in prostate cancer epidemiology between East Asians and Caucasians. Chin. J. Cancer 31, 421–429 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Culp, M. B., Soerjomataram, I., Efstathiou, J. A., Bray, F. & Jemal, A. Recent global patterns in prostate cancer incidence and mortality rates. Eur. Urol. 77, 38–52 (2020).

    Article  PubMed  Google Scholar 

  5. Abeshouse, A. et al. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

    Article  CAS  Google Scholar 

  6. Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Li, J. et al. A genomic and epigenomic atlas of prostate cancer in Asian populations. Nature 580, 93–99 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  9. Adams, E. J. et al. FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes. Nature 571, 408–412 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Parolia, A. et al. Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer. Nature 571, 413–418 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Popiolek, M. et al. Natural history of early, localized prostate cancer. JAMA 291, 2713–2719 (2004).

    Article  Google Scholar 

  12. Jemal, A. et al. Prostate cancer incidence rates 2 years after the US preventive services task force recommendations against screening. JAMA Oncol. 2, 1657–1660 (2016).

    Article  PubMed  Google Scholar 

  13. Mottet, N. et al. EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer—2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 79, 243–262 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Schröder, F. H. et al. Screening and prostate-cancer mortality in a randomized European study. N. Engl. J. Med. 360, 1320–1328 (2009).

    Article  PubMed  Google Scholar 

  15. Eisenberger, M. & Partin, A. Progress toward identifying aggressive prostate cancer. N. Engl. J. Med. 351, 180–181 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Hamdy, F. C. et al. Fifteen-year outcomes after monitoring, surgery, or radiotherapy for prostate cancer. N. Engl. J. Med. 388, 1547–1558 (2023).

    Article  PubMed  Google Scholar 

  17. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mun, D.-G. et al. Proteogenomic characterization of human early-onset gastric cancer. Cancer Cell 35, 111–124 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Chen, Y.-J. et al. Proteogenomics of non-smoking lung cancer in east Asia delineates molecular signatures of pathogenesis and progression. Cell 182, 226–244 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Iglesias-Gato, D. et al. The proteome of primary prostate cancer. Eur. Urol. 69, 942–952 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Latonen, L. et al. Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression. Nat. Commun. 9, e1176 (2018).

    Article  Google Scholar 

  22. Iglesias-Gato, D. et al. The proteome of prostate cancer bone metastasis reveals heterogeneity with prognostic implications. Clin. Cancer Res. 24, 5433–5444 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Kim, Y. et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat. Commun. 7, e11906 (2016).

    Article  Google Scholar 

  24. Drake, J. M. et al. Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166, 1041–1054 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ren, S. et al. Whole-genome and transcriptome sequencing of prostate cancer identify new genetic alterations driving disease progression. Eur. Urol. 73, 322–339 (2018).

    Article  CAS  PubMed  Google Scholar 

  27. Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chowdhury, S. et al. Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer. Cell 186, 3476–3498.e3435 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Paschalis, A. et al. Alternative splicing in prostate cancer. Nat. Rev. Clin. Oncol. 15, 663–675 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Bader, D. A. & McGuire, S. E. Tumour metabolism and its unique properties in prostate adenocarcinoma. Nat. Rev. Urol. 17, 214–231 (2020).

    Article  PubMed  Google Scholar 

  32. Mottet, N. et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 71, 618–629 (2017).

    Article  PubMed  Google Scholar 

  33. Chen, R. et al. Percent free prostate-specific antigen is effective to predict prostate biopsy outcome in Chinese men with prostate-specific antigen between 10.1 and 20.0 ng ml(-1). Asian J. Androl. 17, 1017–1021 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Li, C., Tang, Z., Zhang, W., Ye, Z. & Liu, F. GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA. Nucleic Acids Res. 49, 242–246 (2021).

    Article  Google Scholar 

  35. Bakht, M. K. et al. Landscape of prostate-specific membrane antigen heterogeneity and regulation in AR-positive and AR-negative metastatic prostate cancer. Nat. Cancer. 4, 699–715 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lucarelli, G. et al. Spondin-2, a secreted extracellular matrix protein, is a novel diagnostic biomarker for prostate cancer. J. Urol. 190, 2271–2277 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Ochoa, D. et al. The functional landscape of the human phosphoproteome. Nat. Biotechnol. 38, 365–373 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Lupien, M. et al. FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. Cell 132, 958–970 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Pomerantz, M. M. et al. The androgen receptor cistrome is extensively reprogrammed in human prostate tumorigenesis. Nat. Genet. 47, 1346–1351 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Meng, J. et al. Immune response drives outcomes in prostate cancer: implications for immunotherapy. Mol. Oncol. 15, 1358–1375 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Giunchi, F., Fiorentino, M. & Loda, M. The metabolic landscape of prostate cancer. Eur. Urol. Oncol. 2, 28–36 (2019).

    Article  PubMed  Google Scholar 

  43. Röhrig, F. & Schulze, A. The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 16, 732–749 (2016).

    Article  PubMed  Google Scholar 

  44. Neinast, M., Murashige, D. & Arany, Z. Branched chain amino acids. Annu. Rev. Physiol 81, 139–164 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Wang, S. et al. Prostate-specific deletion of the murine Pten tumor suppressor gene leads to metastatic prostate cancer. Cancer Cell 4, 209–221 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. 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 

  47. Li, J.-T. et al. BCAT2-mediated BCAA catabolism is critical for development of pancreatic ductal adenocarcinoma. Nat. Cell Biol. 22, 167–174 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Li, J.-T. et al. Diet high in branched-chain amino acid promotes PDAC development by USP1-mediated BCAT2 stabilization. Natl Sci. Rev. 9, e212 (2021).

    Article  Google Scholar 

  49. 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 

  50. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, e328 (2016).

    Article  Google Scholar 

  52. Sharma, P. et al. Nivolumab plus ipilimumab for metastatic castration-resistant prostate cancer: preliminary analysis of patients in the CheckMate 650 trial. Cancer Cell 38, 489–499 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Sivanand, S. & Vander Heiden, M. G. Emerging roles for branched-chain amino acid metabolism in cancer. Cancer Cell 37, 147–156 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lynch, C. J. & Adams, S. H. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat. Rev. Endocrinol. 10, 723–736 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Takegoshi, K. et al. Branched-chain amino acids prevent hepatic fibrosis and development of hepatocellular carcinoma in a non-alcoholic steatohepatitis mouse model. Oncotarget 8, 18191–18205 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Imanaka, K. et al. Impact of branched-chain amino acid supplementation on survival in patients with advanced hepatocellular carcinoma treated with sorafenib: a multicenter retrospective cohort study. Hepatol. Res. 46, 1002–1010 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Kuroda, H. et al. Effects of branched-chain amino acid-enriched nutrient for patients with hepatocellular carcinoma following radiofrequency ablation: a one-year prospective trial. J. Gastroenterol. Hepatol. 25, 1550–1555 (2010).

    Article  CAS  PubMed  Google Scholar 

  58. Nojiri, S., Fujiwara, K., Shinkai, N., Iio, E. & Joh, T. Effects of branched-chain amino acid supplementation after radiofrequency ablation for hepatocellular carcinoma: a randomized trial. Nutrition 33, 20–27 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Lei, M.-Z. et al. Acetylation promotes BCAT2 degradation to suppress BCAA catabolism and pancreatic cancer growth. Sig. Transduct. Target. Ther. 5, e70 (2020).

    Article  Google Scholar 

  60. Ericksen, R. E. et al. Loss of BCAA catabolism during carcinogenesis enhances mTORC1 activity and promotes tumor development and progression. Cell Metab. 29, 1151–1165 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Vickers, A. J., Vertosick, E. A. & Sjoberg, D. D. Value of a statistical model based on four kallikrein markers in blood, commercially available as 4Kscore, in all reasonable prostate biopsy subgroups. Eur. Urol. 74, 535–536 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Catalona William, J. et al. A multicenter study of [-2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range. J. Urol. 185, 1650–1655 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mendhiratta, N. et al. Magnetic resonance imaging-ultrasound fusion targeted prostate biopsy in a consecutive cohort of men with no previous biopsy: reduction of over detection through improved risk stratification. J. Urol. 194, 1601–1606 (2015).

    Article  PubMed  Google Scholar 

  64. Varambally, S. et al. Golgi protein GOLM1 is a tissue and urine biomarker of prostate cancer. Neoplasia. 10, 1285–1294 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, Q. et al. Androgen receptor regulates a distinct transcription program in androgen-independent prostate cancer. Cell 138, 245–256 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Johnson, J. L. et al. An atlas of substrate specificities for the human serine/threonine kinome. Nature 613, 759–766 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Liu, Z. et al. A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies. Mol. Cell. 81, 4076–4090 (2021).

    Article  CAS  PubMed  Google Scholar 

  70. Xu, J.-Y. et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell 182, 245–261 (2020).

    Article  CAS  PubMed  Google Scholar 

  71. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  72. Monti, S., Tamayo, P., Mesirov, J. & Golub, T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).

    Article  Google Scholar 

  73. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Danica, D. W. The KSEA app: a web-based tool for kinase activity inference from quantitative phosphoproteomics. Bioinformatics 33, 3489–3491 (2017).

    Article  Google Scholar 

  75. Gao, Q. et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell 179, 1240 (2019).

    Article  CAS  PubMed  Google Scholar 

  76. Huang, C. et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 39, 361–379.e316 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell 184, 4348–4371.e4340 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Cao, L. et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184, 5031–5052.e5026 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Krueger, F. Trim Galore: a wrapper tool around Cutadapt and FastQC. v.0.6.7 (2012).

  81. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, e41 (2011).

    Article  Google Scholar 

  84. Andrews, S. FastQC: a quality control tool for high throughput sequence data. v.0.11.9 (2010).

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

    Article  CAS  PubMed  Google Scholar 

  86. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinform. 12, e323 (2011).

    Article  Google Scholar 

  87. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, 5593–5601 (2014).

    Article  Google Scholar 

  88. Haas, B. A.-O. et al. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol. 20, e213 (2019).

    Article  Google Scholar 

  89. Davidson, N. M., Majewski, I. J. & Oshlack, A. JAFFA: high sensitivity transcriptome-focused fusion gene detection. Genome Med. 7, e43 (2015).

    Article  Google Scholar 

  90. Uhrig, S. A.-O. et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 31, 448–460 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion–switching, targeted mass spectrometry–based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Jin, C., McKeehan, K. & Wang, F. Transgenic mouse with high cre recombinase activity in all prostate lobes, seminal vesicle, and ductus deferens. Prostate 57, 160–164 (2003).

    Article  CAS  PubMed  Google Scholar 

  93. Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Yuan, H. et al. SETD2 restricts prostate cancer metastasis by integrating EZH2 and AMPK signaling pathways. Cancer Cell 38, 350–365 (2020).

    Article  CAS  PubMed  Google Scholar 

  95. Park, J.-H. et al. Prostatic intraepithelial neoplasia in genetically engineered mice. Am. J. Pathol. 161, 727–735 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Wang, S. et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc. 8, 2502–2515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Cheng, C. et al. Gremlin1 is a therapeutically targetable FGFR1 ligand that regulates lineage plasticity and castration resistance in prostate cancer. Nat. Cancer 3, 565–580 (2022).

    Article  CAS  PubMed  Google Scholar 

  98. Deng, S. et al. Ectopic JAK-STAT activation enables the transition to a stem-like and multilineage state conferring AR-targeted therapy resistance. Nat. Cancer 3, 1071–1087 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wagle, M. C. et al. A transcriptional MAPK pathway activity score (MPAS) is a clinically relevant biomarker in multiple cancer types. NPJ Precis. Oncol. 2, 7 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Qin, J. et al. Integrative proteomic analysis reveals metabolic vulnerabilities and diagnostic biomarkers in high-risk prostate cancer. Figshare https://doi.org/10.6084/m9.figshare.25706982 (2024).

Download references

Acknowledgements

This study was supported by grants from the National Key Research and Development Program of China (2021YFA1300601 to J.Q., 2018YFA0902700 to J.Q., 2020YFE0202200 to M.T.), the National Natural Science Foundation of China Projects (8245103 to J.Q., 82341012 to J.Q., 22225702 to M.T., 81825018 to J.Q., 82130085 to J.Q., 82372698 to B.D., 82372771 to N.L., 92153302 to M.T., 82203495 to Q.P., 81821005 to M.T. and 32322048 to J.-Y.X.) and the Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch (JCYJ-SHFY-2022-007 to J.Q.). The Shanghai Academic/Technology Research Leader Program (22XD1420900 to M.T.), Guangdong High-level New R&D Institute (2019B090904008 to M.T.) and Guangdong High-level Innovative Research Institute (2021B0909050003 to M.T.). The Shanghai Rising-Star Program (no. 22QA1411100 to J.-Y.X.), the Youth Innovation Promotion Association CAS (no. 2021276 to J.-Y.X.) and the Young Elite Scientists Sponsorship Program by CAST (2022QNRC001 to J.-Y.X.). We also thank the support of the Innovative Research Team of High-Level Local Universities in Shanghai (to W.X., J.Q. and M.T.) and Sanofi scholarship program (to J.-Y.X.). We thank GloriousMed Clinical Laboratory (Shanghai, China) for their contribution to the WES and RNA-seq analyses. The computations in this paper were supported by the Center for High Performance Computing at Shanghai Jiao Tong University.

Author information

Author notes

  1. These authors contributed equally: Baijun Dong, Jun-Yu Xu, Yuqi Huang, Jiacheng Guo, Qun Dong, Yanqing Wang.

  2. These authors jointly supervised this work: Jun-Yu Xu, Jing Li, Wei Xue, Minjia Tan, Jun Qin.

Authors and Affiliations

  1. Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Baijun Dong, Yanqing Wang & Wei Xue

  2. Department of Urology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China

    Baijun Dong

  3. State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Jun-Yu Xu, Yuqi Huang, Mingya Zhang, Linhui Zhai & Minjia Tan

  4. Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Guangdong, China

    Jun-Yu Xu & Minjia Tan

  5. CAS Key Laboratory of Tissue Microenvironment and Tumor, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Jiacheng Guo, Ni Li, Qiang Pan, Hanling Wang & Jun Qin

  6. Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Qun Dong, Bairun Chen, Danqing Shen, Yiming Ma & Jing Li

  7. Department of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China

    Qiuli Liu & Jun Jiang

  8. Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, China

    Linhui Zhai

  9. State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, China

    Jian Zhang

  10. Jinfeng Laboratory, Chongqing, China

    Jun Qin

Authors
  1. Baijun Dong
    View author publications

    Search author on:PubMed Google Scholar

  2. Jun-Yu Xu
    View author publications

    Search author on:PubMed Google Scholar

  3. Yuqi Huang
    View author publications

    Search author on:PubMed Google Scholar

  4. Jiacheng Guo
    View author publications

    Search author on:PubMed Google Scholar

  5. Qun Dong
    View author publications

    Search author on:PubMed Google Scholar

  6. Yanqing Wang
    View author publications

    Search author on:PubMed Google Scholar

  7. Ni Li
    View author publications

    Search author on:PubMed Google Scholar

  8. Qiuli Liu
    View author publications

    Search author on:PubMed Google Scholar

  9. Mingya Zhang
    View author publications

    Search author on:PubMed Google Scholar

  10. Qiang Pan
    View author publications

    Search author on:PubMed Google Scholar

  11. Hanling Wang
    View author publications

    Search author on:PubMed Google Scholar

  12. Jun Jiang
    View author publications

    Search author on:PubMed Google Scholar

  13. Bairun Chen
    View author publications

    Search author on:PubMed Google Scholar

  14. Danqing Shen
    View author publications

    Search author on:PubMed Google Scholar

  15. Yiming Ma
    View author publications

    Search author on:PubMed Google Scholar

  16. Linhui Zhai
    View author publications

    Search author on:PubMed Google Scholar

  17. Jian Zhang
    View author publications

    Search author on:PubMed Google Scholar

  18. Jing Li
    View author publications

    Search author on:PubMed Google Scholar

  19. Wei Xue
    View author publications

    Search author on:PubMed Google Scholar

  20. Minjia Tan
    View author publications

    Search author on:PubMed Google Scholar

  21. Jun Qin
    View author publications

    Search author on:PubMed Google Scholar

Contributions

B.D., J.-Y.X., Y.H., M.T. and J.Q. designed the experiment and prepared the manuscript. B.D., J.-Y.X., Y.H., J.G. and Q.D. performed most of the experiments. Y.H., Q.D., J.L. and N.L. contributed to the computational statistical analysis. Y.W., Q.L. and J.J. performed the TMA and pathology analyses. M.Z., Q.P., H.W., B.C., D.S., Y.M., L.Z. and J.Z. performed a specific subset of the experiments and analyses, which were supervised by J.L., W.X., M.T. and J.Q. All authors approved the final manuscript.

Corresponding authors

Correspondence to Jun-Yu Xu, Jing Li, Wei Xue, Minjia Tan or Jun Qin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks the anonymous reviewers 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 Proteogenomic landscape of high-risk Chinese PCa.

a. The association between histological invasion status and recurrence-free survival in our cohort (Kaplan-Meier analysis, log-rank test, n numbers shown represent patients). b. Distributions of age (n = 125) and plasma PSA (n = 117) levels of patients in our cohort. For the boxplot, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively and whiskers denote 1.5 × interquartile range. c. Left panel: the number of patients in different TNM stages. Middle and right panel: Kaplan-Meier curves of recurrence-free survival of patients in different TNM stages (Kaplan-Meier analysis, log-rank test, n numbers shown represent patients). d. Left panel: the number of patients classified by GS. Right panel: Kaplan-Meier curves of recurrence-free survival of patients with different GS (log-rank test, n numbers shown represent patients). e. Number of identified proteins and phosphorylation sites per sample. f. Distribution of normalized protein intensities (log2 ratio) per sample. g. Correlation among TMT internal references in proteome (left) and phosphoproteome (right). h. Principal component analysis for normalized proteome (left) and phosphoproteome (right) data, dots are colored according the TMT batches. (For panel (e-h), n = 125/120 paired tumor and NAT samples for proteome/phosphoproteome.) i. Distribution of total quantified proteins among all the paired tumor and NAT samples (n = 250) at subcellular level. This figure was created in BioRender.com. j. KEGG pathways enriched in tumor samples with FOXA1 mutation (n = 39) via GSEA (FDR < 0.05). k. Kaplan-Meier curves of recurrence-free survival for patients with or without FOXA1 mutation in our cohort and the CPGEA cohort (log-rank test, n numbers shown represent patients). l. The two-sided Spearman correlation between expression levels of FOXA1 in transcriptome and proteome are calculated for NATs (n = 92) and tumor samples (n = 95).

Source data

Extended Data Fig. 2 Integrated multiomic analyses of Chinese PCa.

a. Genomic amplification and deletion peaks of significantly recurrent somatic CNAs, n = 125 tumor samples. b. Kaplan-Meier curves of recurrence-free survival for patients with or without 13q31.1 amplification (log-rank test, n numbers shown represent patients). c. Cis (diagonal lines) and trans effects of CNAs on phosphoproteins (spearman correlation, n = 120 tumor samples). Genes are ordered by chromosomal location. d. Number of all alternative splicing (AS) events detected in our PCa dataset. e. Kaplan-Meier curves of recurrence-free survival for patients with high or low (median cut point) novel MXE splicing events of SYTL2 (log-rank test). f. Kaplan-Meier curves of recurrence-free survival for patients with high or low (median cut point) annotated/novel SE splicing events of CTNND1 (log-rank test). In panels (e-f), n numbers shown represent patient numbers. g. Left panel: gene-wise mRNA-protein two-sided Spearman correlations in NATs (n = 125). Right panel: gene-wise mRNA-protein two-sided Spearman correlations in tumor samples (n = 125). h. Pathways enriched for genes positively correlated at transcriptome and proteome level (in all 125 paired tumor and NAT samples, FDR < 0.05).

Source data

Extended Data Fig. 3 Tumor-NAT comparisons reveal tumorigenic changes and biomarker candidates.

a. Principal component analysis for normalized transcriptome, proteome data, dots are colored based on sample types. b. Principal component analysis for normalized transcriptome data, dots are colored according to the tumor purity. c. Principal component analysis for normalized proteome data, dots are colored according the tumor purity. (In panels (a-c), n = 125 paired tumor and NAT samples.) d. Top panel: the proteome expression level of the indicated proteins in paired tumors and NAT samples without missing values (two-sided Wilcoxon signed-rank test. KLK, n = 125; AR, n = 105; AMACR, n = 125; NKX3-1, n = 110). For the boxplot, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively and whiskers denote 1.5 × interquartile range. Bottom panel: their associations to PCa recurrence-free survival (Kaplan-Meier analysis, log-rank test, n numbers shown represent tumor samples). e. Venn plot for the upregulated (top panel) or downregulated (bottom panel) genes in transcriptome (DESeq2) and proteome (Wilcoxon signed-rank test). n = 125 paired tumor and NAT samples and the missing values were ignored. f. Expression level of three proteins in serum samples of BPH, primary tumor and metastatic PCa. (two-sided Wilcoxon rank-sum test). g. The two-sided Pearson correlation between serum concentration (ng/ml) and proteome level (log2 intensity of GOLM1 protein) in tumors, the blue shadow represents a confidence interval of 0.95. For panel (f-g), n numbers shown represent tumor samples.

Source data

Extended Data Fig. 4 Phosphorylation of FOXA1-Ser331 is required for the activation of AR signaling.

a. Principal component analysis for normalized phosphoproteome data, dots are colored based on sample types. b. The list of prognosis-related phosphorylation sites (Kaplan-Meier analysis, log-rank test). (In panels (a-b), n = 120 paired tumor and NAT samples. The missing values were ignored.) c. Comparison of the phosphorylation levels of selected FOXA1 phosphorylation sites in paired NATs and tumors in our cohort (two-sided Wilcoxon signed-rank test. FOXA1-S304, n = 75; FOXA1-S307, n = 95; FOXA1-S311, n = 85, n numbers shown represent paired tumor and NAT samples). For the boxplot, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively and whiskers denote 1.5 ×interquartile range. d. Comparison of the phosphorylation levels of selected FOXA1 phosphorylation sites in FOXA1 wild type (n = 82) and mutant (n = 38) tumor samples in our cohort (two-sided Wilcoxon rank-sum test, FOXA1-S304 WT n = 54; FOXA1-S304 mutant n = 21; FOXA1-S307 WT, n = 68; FOXA1-S307 mutant, n = 27; FOXA1-S331 WT, n = 64; FOXA1-S331 mutant, n = 21, n numbers shown represent tumor samples). For the boxplot, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively and whiskers denote 1.5 × interquartile range. e. Kaplan-Meier curves of recurrence-free survival for patients with high or low phosphorylation levels of FOXA1-S304 or -S307 in our cohort (using the best performing threshold, log-rank test, n numbers shown represent tumor samples). f. Comparison of AR signaling activities in patients with high (n = 46) or low (n = 30) phosphorylation level of FOXA1-S331 in our cohort (two-sided Wilcoxon rank-sum test). g. IB analysis in C4-2 and LNCaP cells in the presence of WT or S331A FOXA1. Representative result of three biological replicates is shown. h. Heatmap of the CUT&Tag-seq profiles of FOXA1 peaks in WT and FOXA1S331A C4-2 cells shown in a horizontal window of ± 2 kb from the peak center. i. BETA of activating and repressive function at the AR-dependent and independent sites. The red, blue and black lines represent cumulative fractions of genes that are activated, repressed or unaffected by FOXA1S331A (based on RNA-seq results), respectively. The genes are ranked based on their regulatory potential scores (based on FOXA1 ChIP-seq results). P values were calculated by two-sided Kolmogorov–Smirnov tests.

Source data

Extended Data Fig. 5 Proteomic subtypes of PCa and their molecular characterizations.

a. Consensus clustering results for normalized proteome data (k = 3 to 5). b. Kaplan-Meier curves of recurrence-free survival for patients in different consensus clusters (k = 4 or 5, log-rank test). c. Differentially distributed PCa-associated gene mutations across three proteomic subtypes (one-sided Fisher’s exact test, P = 0.028 for ROBO2, P = 0.006 for NCOR2, P = 0.0008 for APC, P = 0.012 for FOXA1, P = 0.045 for ARID4B). In panels (b-c), n numbers shown represent tumor samples. d. Differentially distributed arm-level gene copy number alterations across three proteomic subtypes (n = 45, 33, 47 tumor samples for S-I, S-II, S-III separately. two-sided Fisher’s exact test, P = 0.027 for loss_13p, P = 0.012 for loss_9p, P = 0.003 for loss_9q). e. GSEA plot for metabolic pathways upregulated in S-III subtypes. f. Comparison of the protein level of FOXA1 across three subtypes in our cohort (two-sided Wilcoxon rank-sum test, n = 33, 25, 37 tumor samples for S-I, S-II, S-III separately). For the boxplot, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively and whiskers denote 1.5 × interquartile range. g. KEGG pathway enrichment results of S-I and S-III subtypes based on phosphoproteome data via GSEA (FDR < 0.05). h. Screening workflow for potential prognostic biomarkers and the number of remaining protein candidates after each filtering step. i. Immunohistochemical staining for FASN, BCAT2, NDUFAB1 and BCKDHB. Representative result of three biological replicates is shown. Scale bar, 100 μm.

Source data

Extended Data Fig. 6 BCAA catabolism potentiates PCa progression.

a. Growth curves of PCa cells with or without BCAT2 KD (n = 7 biological replicates for each group; Data represent the mean ± SEM, the P value was determined by two-way ANOVA followed by multiple comparisons). b. IB analysis of BCAT2 expression in PCa cells. Representative result of three biological replicates is shown. c. Growth curves of LNCaP cells with or without BCAT2 overexpression (n = 7 biological replicates per group; Data represent the mean ± SEM, the P value was determined by two-way ANOVA followed by multiple comparisons). d. IB analysis of BCAT2 in PCa cells as indicated. Representative result of three biological replicates is shown. e. Representative images and quantification of formation efficiency and size of PtenPC−/− organoids with or without BCAT2 overexpression (n = 10 biological replicates for each group, data represent the mean ± SEM; the P value was determined by two-tailed t-test). Scale bar, 500 μm. f-g. Representative image and quantification of the formation efficiency and size of PtenPC−/−; Tp53PC−/− organoids with or without Bcat2 knockdown (n = 15 biological replicates per group, data represent the mean ± SEM; the P value was determined by one-way ANOVA followed by multiple comparisons test). 500 μm. h. H&E and IHC staining for Ki67 in prostate sections from the orthotopic transplantation of PtenPC−/−; Tp53PC−/− organoids and quantification of Ki67+ cells (n = 10 tumors for each group). Data represent the mean ± SEM, the P value was determined by one-way ANOVA followed by multiple comparisons test. Scale bar, 100 μm. i. Relative levels of acetyl-CoA in PtenPC−/−; Tp53PC−/− organoids with or without Bcat2 knockdown (n = 3 biological replicates per group). Data represent the mean ± SEM, the P value was determined by one-way ANOVA followed by multiple comparisons test. j-k. BCAT2 knockdown suppresses mitochondrial respiration levels (OCR) in 22RV-1 (n = 5 biological replicates per group) and PtenPC−/−; Tp53PC−/− organoids (n = 3 biological replicates per group, data represent the mean ± SEM; the P value was determined by two-way ANOVA followed by multiple comparisons test). l. The glycolytic flux (ECAR) analysis in PtenPC−/−; Tp53PC−/− organoids with or without Bcat2 deletion (n = 3 biological replicates for each group). Data represent the mean ± SEM, the P value was determined by two-way ANOVA followed by multiple comparisons test. m. Measurement of 22RV-1 growth in WT and BCAT2-depleted cells with or without nucleobases (NB) and/or BCKA supplementation. Data represent the mean ± SEM; P value was determined by two-way ANOVA followed by multiple comparisons test. (n = 5 biological replicates per group), no significance. n. Measurement of PtenPC−/−; Trp53PC−/− organoids size in WT and BCAT2-depleted cells with or without nucleobases (NB) and/or BCKA supplementation. Data represent the mean ± SEM; P value was determined by one-way ANOVA followed by multiple comparisons test. (n = 16 biological replicates per group). NS, no significance.

Source data

Extended Data Fig. 7 A low-BCAA diet restrains PCa progression as a dietary intervention approach.

a-c. The BCAA concentration (a), body weight (b) and daily food intake (c) in normal or high-BCAA diet treated PtenPC−/− mice (n = 7 mice per group). Data represent the mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test (a and c) and two-way ANOVA followed by multiple comparisons test (b), NS, no significance. d. H&E and IHC staining for Ki67, αSMA and CK8 in prostate sections from the indicated mice and quantification of Ki67 and αSMA positivity (n = 10 tumors each group). Data represent the mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test (up) and two-sided Fisher’s exact test (bottom). Scale bar, 100 μm. e. H&E and AR staining of lymph nodes from 5-month-old PtenPC−/− mice fed an NCD or a high-BCAA diet (n = 15 mice per group; the P value was determined by two-sided Fisher’s exact test). Scale bar, 100 μm. Scale bar, 100 μm. (f-h) The plasma BCAA concentration (f), body weight (g) and daily food intake (h) in normal and a low-BCAA diet treated PtenPC−/−; Tp53PC−/− mice (n = 7 mice per group). Data represent the mean ± SEM. Statistical significance was determined by two-tailed unpaired t-test (f and h) and two-way ANOVA followed by multiple comparisons test (g), NS, no significance. i. Measurement of BCAA and oxidative phosphorylation metabolites in PDXs transplanted into 6-week-old NSG mice fed an NCD or a low-BCAA diet for 7 weeks (n = 3 biological replicates per group).

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data for proteogenomic landscape of high-risk Chinese PCa.

Source Data Fig. 2

Statistical source data for multiomic analyses of localized high-risk PCa.

Source Data Fig. 3

Statistical source data for PCa characteristic and biomarker discovery.

Source Data Fig. 4

Statistical source data and unprocessed images for Fig. 4.

Source Data Fig. 5

Statistical source data for PCa proteomic subtypes and their molecular characterizations.

Source Data Fig. 6

Statistical source data and unprocessed images for Fig. 6.

Source Data Extended Data Fig. 1

Statistical source data for sample size, clinical characteristic distribution and driver genomic mutation of high-risk Chinese PCa cohort.

Source Data Extended Data Fig. 2

Statistical source data for gene-wise correlation analysis between transcriptomic and proteomic data.

Source Data Extended Data Fig. 3

Statistical source data for supplementary analysis of tumor-NAT comparisons.

Source Data Extended Data Fig. 4

Statistical source data and unprocessed images for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Statistical source data and unprocessed images for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Statistical source data and unprocessed images for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Statistical source data and unprocessed images for Extended Data Fig. 7.

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

Dong, B., Xu, JY., Huang, Y. et al. Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers. Nat Cancer 5, 1427–1447 (2024). https://doi.org/10.1038/s43018-024-00820-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43018-024-00820-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research