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Expressed prognostic biomarkers for primary prostate cancer independent of multifocality and transcriptome heterogeneity


The majority of prostate cancer patients are diagnosed with multiple primary malignant foci. The distinct foci are exceptionally heterogeneous with regard to DNA mutations, but whether this is recapitulated at the transcriptome level remains unknown. In this study, inter- and intrafocal heterogeneity has been assessed by whole-transcriptome sequencing of 87 tissue samples from 23 patients with localized prostate cancer treated with radical prostatectomy. From each patient, multiple samples were taken from one or more malignant foci, in addition to one sample from benign prostate tissue. Transcriptomic profiles of different malignant foci from the same patient showed a similar level of heterogeneity as tumors from different patients. This applies to expression of genes, fusion genes, and somatic mutations. Within-patient pair-wise analyses identified expression patterns linked to ETS status and extraprostatic extension. A set of 62 genes were found with low intrapatient heterogeneity and high interpatient heterogeneity, retaining stable expression profiles across foci within the same patient. Among these, 16 genes are associated with biochemical recurrence in a separately published study and are therefore nominated as biomarkers with prognostic value regardless of which malignant focus is sampled. In conclusion, an extensive heterogeneity in multifocal prostate cancer is confirmed at the gene expression level. Diagnostic biomarkers were identified for ETS positive samples and samples from extraprostatic extensions. Finally, prognostic biomarkers independent of multifocal heterogeneity were found.

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Fig. 1: Principal component analysis shows heterogeneous gene expression levels between different clinicopathological categories and between different prostate tissue samples from the same patients.
Fig. 2: Expression of selected fusion genes with relevance to prostate cancer.
Fig. 3: Distribution of expression status for 2115 mutations in 64 malignant prostate cancer samples.
Fig. 4: Inter-patient and intra-patient heterogeneity scores.
Fig. 5: Genes differentially expressed in EPE vs. non-EPE malignant tissues from the same prostates.
Fig. 6: Genes differentially expressed in ETS positive vs. negative malignant tissues from the same prostates.


  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin. 2021;71:209–49.

    Google Scholar 

  2. Ruijter ET, Van de Kaa CA, Schalken JA, Debruyne FM, Ruiter DJ. Histological grade heterogeneity in multifocal prostate cancer. Biological and clinical implications. J Pathol. 1996;180:295–9.

    Article  CAS  PubMed  Google Scholar 

  3. Løvf M, Zhao S, Axcrona U, Johannessen B, Bakken AC, Carm KT, et al. Multifocal primary prostate cancer exhibits high degree of genomic heterogeneity. Eur Urol. 2019;75:498–505.

    Article  PubMed  CAS  Google Scholar 

  4. Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell. 2015;163:1011–25.

    Article  CAS  Google Scholar 

  5. Kumar-Sinha C, Tomlins SA, Chinnaiyan AM. Recurrent gene fusions in prostate cancer. Nat Rev Cancer. 2008;8:497–511.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tomlins SA, Laxman B, Varambally S, Cao X, Yu J, Helgeson BE, et al. Role of the TMPRSS2-ERG gene fusion in prostate cancer. Neoplasia 2008;10:177–IN9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tomlins SA, Mehra R, Rhodes DR, Smith LR, Roulston D, Helgeson BE, et al. TMPRSS2:ETV4 gene fusions define a third molecular subtype of prostate cancer. Cancer Res. 2006;66:3396–400.

    Article  CAS  PubMed  Google Scholar 

  8. Tomlins SA, Bjartell A, Chinnaiyan AM, Jenster G, Nam RK, Rubin MA, et al. ETS gene fusions in prostate cancer: from discovery to daily clinical practice. Eur Urol. 2009;56:275–86.

    Article  CAS  PubMed  Google Scholar 

  9. Pettersson A, Graff RE, Bauer SR, Pitt MJ, Lis RT, Stack EC, et al. The TMPRSS2: ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and meta-analysis. Cancer Epidemiol Prev Biomark. 2012;21:1497–509.

    Article  Google Scholar 

  10. Paulo P, Barros‐Silva JD, Ribeiro FR, Ramalho‐Carvalho J, Jerónimo C, Henrique R, et al. FLI1 is a novel ETS transcription factor involved in gene fusions in prostate cancer. Genes Chromosomes Cancer. 2012;51:240–9.

    Article  CAS  PubMed  Google Scholar 

  11. Pflueger D, Terry S, Sboner A, Habegger L, Esgueva R, Lin P-C, et al. Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing. Genome Res. 2011;21:56–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Barbieri CE, Baca SC, Lawrence MS, Demichelis F, Blattner M, Theurillat J-P, et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat Genet. 2012;44:685–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Salami SS, Hovelson DH, Kaplan JB, Mathieu R, Udager AM, Curci NE, et al. Transcriptomic heterogeneity in multifocal prostate cancer. JCI insight. 2018;3:e123468.

    Article  PubMed Central  Google Scholar 

  14. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma. Am J Surg Pathol. 2016;40:244–52.

    Article  PubMed  Google Scholar 

  15. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Andrews S, FastQC: a quality control tool for high throughput sequence data. 2010. Available online at:

  17. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    Article  CAS  PubMed  Google Scholar 

  19. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol. 2011;7:e1001138.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nicorici D, Şatalan M, Edgren H, Kangaspeska S, Murumägi A, Kallioniemi O, et al. FusionCatcher–a tool for finding somatic fusion genes in paired-end RNA-sequencing data. BioRxiv. 2014.

  22. Haas BJ, Dobin A, Stransky N, Li B, Yang X, Tickle T, et al. STAR-Fusion: fast and accurate fusion transcript detection from RNA-Seq. BioRxiv. 2017.

  23. Pederzoli F, Bandini M, Marandino L, Ali SM, Madison R, Chung J, et al. Targetable gene fusions and aberrations in genitourinary oncology. Nat Rev Urol. 2020;17:613–25.

    Article  CAS  PubMed  Google Scholar 

  24. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 2014;67:1–48.

    Google Scholar 

  27. Lüdecke D, Makowski D, Waggoner P. Performance: assessment of regression models performance. R Package Version. 2019;04:2.

    Google Scholar 

  28. Kremer A, Kremer T, Kristiansen G, Tolkach Y. Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers. BMC Urol. 2019;19:1–10.

    Article  CAS  Google Scholar 

  29. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 2019;35:2084–92.

    Article  CAS  PubMed  Google Scholar 

  31. Ignatiadis N, Klaus B, Zaugg JB, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods. 2016;13:577–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinforma. 2009;10:161.

    Article  CAS  Google Scholar 

  34. Sergushichev A, An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. BioRxiv. 2016.

  35. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Benaglia T, Chauveau D, Hunter D, Young D. mixtools: an R package for analyzing finite mixture models. J Stat Softw. 2009;32:1–29.

    Article  Google Scholar 

  37. Carm KT, Hoff AM, Bakken AC, Axcrona U, Axcrona K, Lothe RA, et al. Interfocal heterogeneity challenges the clinical usefulness of molecular classification of primary prostate cancer. Sci Rep. 2019;9:1–6.

    Article  CAS  Google Scholar 

  38. Boormans JL, Korsten H, Ziel‐van der Made AJ, van Leenders GJ, de Vos CV, Jenster G, et al. Identification of TDRD1 as a direct target gene of ERG in primary prostate cancer. Int J Cancer. 2013;133:335–45.

    Article  CAS  PubMed  Google Scholar 

  39. Gandellini P, Casiraghi N, Rancati T, Benelli M, Doldi V, Romanel A, et al. Core biopsies from prostate cancer patients in active surveillance protocols harbor PTEN and MYC alterations. Eur Urol Oncol. 2019;2:277–85.

    Article  PubMed  Google Scholar 

  40. Ribeiro FR, Henrique R, Martins AT, Jerónimo C, Teixeira MR. Relative copy number gain of MYC in diagnostic needle biopsies is an independent prognostic factor for prostate cancer patients. Eur Urol. 2007;52:116–25.

    Article  PubMed  Google Scholar 

  41. Koh CM, Bieberich CJ, Dang CV, Nelson WG, Yegnasubramanian S, De Marzo AM. MYC and prostate cancer. Genes Cancer. 2010;1:617–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Amaro A, Esposito AI, Gallina A, Nees M, Angelini G, Albini A, et al. Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel. Cancer Metastasis Rev. 2014;33:657–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Miyashita M, Oshiumi H, Matsumoto M, Seya T. DDX60, a DEXD/H box helicase, is a novel antiviral factor promoting RIG-I-like receptor-mediated signaling. Mol Cell Biol. 2011;31:3802–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wu Y, Wu X, Wu L, Wang X, Liu Z. The anticancer functions of RIG-I–like receptors, RIG-I and MDA5, and their applications in cancer therapy. Transl Res. 2017;190:51–60.

    Article  CAS  PubMed  Google Scholar 

  45. de Andrade LF, Tay RE, Pan D, Luoma AM, Ito Y, Badrinath S, et al. Antibody-mediated inhibition of MICA and MICB shedding promotes NK cell–driven tumor immunity. Science 2018;359:1537–42.

    Article  CAS  Google Scholar 

  46. Cifaldi L, Monaco EL, Forloni M, Giorda E, Lorenzi S, Petrini S, et al. Natural killer cells efficiently reject lymphoma silenced for the endoplasmic reticulum aminopeptidase associated with antigen processing. Cancer Res. 2011;71:1597–606.

    Article  CAS  PubMed  Google Scholar 

  47. Trendel JA, Ellis N, Sarver JG, Klis WA, Dhananjeyan M, Bykowski CA, et al. Catalytically active peptidylglycine α-amidating monooxygenase in the media of androgen-independent prostate cancer cell lines. J Biomol Screen. 2008;13:804–9.

    Article  CAS  PubMed  Google Scholar 

  48. Tan Y, Zhou G, Wang X, Chen W, Gao H. USP18 promotes breast cancer growth by upregulating EGFR and activating the AKT/Skp2 pathway. Int J Oncol. 2018;53:371–83.

    CAS  PubMed  Google Scholar 

  49. Peng P, Wu W, Zhao J, Song S, Wang X, Jia D, et al. Decreased expression of Calpain-9 predicts unfavorable prognosis in patients with gastric cancer. Sci Rep. 2016;6:1–11.

    CAS  Google Scholar 

  50. Wang Q, Peng R, Wang B, Wang J, Yu W, Liu Y, et al. Transcription factor KLF13 inhibits AKT activation and suppresses the growth of prostate carcinoma cells. Cancer Biomark. 2018;22:533–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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The study was funded by the South-Eastern Norway Regional Health Authority (Project numbers 2017045, 2019016, and 2020063), the Research Council of Norway through its FRIPRO funding scheme (262529/F20 and Toppforsk-250993), the Norwegian Cancer Society (Grant number 208197), and a grant from Centre for Molecular Medicine Norway’s programme for networking. The study was granted secure storage and high-performance computation resources from NorStore and University of Oslo’s Services for Sensitive Data (NS9013S and p19, respectively). We are grateful to the individuals with prostate cancer and their families for contributing to this study.

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JMS handled the data, performed bioinformatics analyses, interpreted results, prepared figures, and tables, and wrote the report. BJ contributed to the bioinformatics analyses and managed the computational infrastructure. XZ performed survival analyses. SGK and KTC performed wet-lab validation. MB and UA performed histopathological evaluation. AS contributed to analyses on somatic mutations. KA had the clinical responsibility. AM, RAL, UA, KA, and RIS secured funding for the project. RIS conceptualized the research. All authors provided feedback on interpretation of results and on the report.

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Correspondence to Rolf I. Skotheim.

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Strømme, J.M., Johannessen, B., Kidd, S.G. et al. Expressed prognostic biomarkers for primary prostate cancer independent of multifocality and transcriptome heterogeneity. Cancer Gene Ther 29, 1276–1284 (2022).

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