A Systems Genetics Approach Identifies , , and as Novel Aggressive Prostate Cancer Susceptibility Genes

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Prostate cancer is a remarkably common disease, and in 2014 it is estimated that it will account for 27% of new cancer cases in men in the US. However, less than 13% those diagnosed will succumb to prostate cancer, with most men dying from unrelated causes. The tests used to identify men at risk of fatal prostate cancer are inaccurate, which leads to overtreatment, unnecessary patient suffering, and represents a significant public health burden. Many studies have shown that hereditary genetic variation significantly alters susceptibility to fatal prostate cancer, although the identities of genes responsible for this are mostly unknown. Here, we used a mouse model of prostate cancer to identify such genes. We introduced hereditary genetic variation into this mouse model through breeding, and used a genetic mapping technique to identify 35 genes associated with aggressive disease. The levels of three of these genes were consistently abnormal in human prostate cancers with a more aggressive disease course. Additionally, hereditary differences in these same three genes were associated with markers of fatal prostate cancer in men. This approach has given us unique insights into how hereditary variation influences fatal forms of prostate cancer.

Vyšlo v časopise: A Systems Genetics Approach Identifies , , and as Novel Aggressive Prostate Cancer Susceptibility Genes. PLoS Genet 10(11): e32767. doi:10.1371/journal.pgen.1004809
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004809

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Zdroje

1. SiegelR, MaJ, ZouZ, JemalA (2014) Cancer statistics, 2014. CA Cancer J Clin 64: 9–29.

2. AggarwalR, ZhangT, SmallEJ, ArmstrongAJ (2014) Neuroendocrine prostate cancer: subtypes, biology, and clinical outcomes. J Natl Compr Canc Netw 12: 719–726.

3. HumphreyPA (2012) Histological variants of prostatic carcinoma and their significance. Histopathology 60: 59–74.

4. SunY, NiuJ, HuangJ (2009) Neuroendocrine differentiation in prostate cancer. Am J Transl Res 1: 148–162.

5. ShappellSB, ThomasGV, RobertsRL, HerbertR, IttmannMM, et al. (2004) Prostate pathology of genetically engineered mice: definitions and classification. The consensus report from the Bar Harbor meeting of the Mouse Models of Human Cancer Consortium Prostate Pathology Committee. Cancer Res 64: 2270–2305.

6. MarcusDM, GoodmanM, JaniAB, OsunkoyaAO, RossiPJ (2012) A comprehensive review of incidence and survival in patients with rare histological variants of prostate cancer in the United States from 1973 to 2008. Prostate Cancer Prostatic Dis 15: 283–288.

7. ShahRB, MehraR, ChinnaiyanAM, ShenR, GhoshD, et al. (2004) Androgen-independent prostate cancer is a heterogeneous group of diseases: lessons from a rapid autopsy program. Cancer Res 64: 9209–9216.

8. TerryS, BeltranH (2014) The Many Faces of Neuroendocrine Differentiation in Prostate Cancer Progression. Front Oncol 4: 60.

9. BeltranH, TomlinsS, AparicioA, AroraV, RickmanD, et al. (2014) Aggressive variants of castration-resistant prostate cancer. Clin Cancer Res 20: 2846–2850.

10. TanHL, SoodA, RahimiHA, WangW, GuptaN, et al. (2014) Rb loss is characteristic of prostatic small cell neuroendocrine carcinoma. Clin Cancer Res 20: 890–903.

11. Amin AlOA, Kote-JaraiZ, SchumacherFR, WiklundF, BerndtSI, et al. (2013) A meta-analysis of genome-wide association studies to identify prostate cancer susceptibility loci associated with aggressive and non-aggressive disease. Hum Mol Genet 22: 408–415.

12. GingrichJR, BarriosRJ, FosterBA, GreenbergNM (1999) Pathologic progression of autochthonous prostate cancer in the TRAMP model. Prostate Cancer Prostatic Dis 2: 70–75.

13. GingrichJR, BarriosRJ, KattanMW, NahmHS, FinegoldMJ, et al. (1997) Androgen-independent prostate cancer progression in the TRAMP model. Cancer Res 57: 4687–4691.

14. HurwitzAA, FosterBA, AllisonJP, GreenbergNM, KwonED (2001) The TRAMP mouse as a model for prostate cancer. Curr Protoc Immunol Chapter 20: Unit

15. PatelSJ, MolinoloAA, GutkindS, CrawfordNP (2013) Germline genetic variation modulates tumor progression and metastasis in a mouse model of neuroendocrine prostate carcinoma. PLoS One 8: e61848.

16. DahabrehIJ, ChungM, BalkEM, YuWW, MathewP, et al. (2012) Active surveillance in men with localized prostate cancer: a systematic review. Ann Intern Med 156: 582–590.

17. YehIT, ReddickRL, KumarAP (2009) Malignancy arising in seminal vesicles in the transgenic adenocarcinoma of mouse prostate (TRAMP) model. Prostate 69: 755–760.

18. TaniY, SuttieA, FlakeGP, NyskaA, MaronpotRR (2005) Epithelial-stromal tumor of the seminal vesicles in the transgenic adenocarcinoma mouse prostate model. Vet Pathol 42: 306–314.

19. BostwickDG, HossainD, QianJ, NeumannRM, YangP, et al. (2004) Phyllodes tumor of the prostate: long-term followup study of 23 cases. J Urol 172: 894–899.

20. SmithR, SheppardK, DipetrilloK, ChurchillG (2009) Quantitative trait locus analysis using J/qtl. Methods Mol Biol 573: 175–188.

21. SchadtEE, LambJ, YangX, ZhuJ, EdwardsS, et al. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37: 710–717.

22. DrakeTA, SchadtEE, DavisRC, LusisAJ (2005) Integrating genetic and gene expression data to study the metabolic syndrome and diabetes in mice. Am J Ther 12: 503–511.

23. VavouriT, McEwenGK, WoolfeA, GilksWR, ElgarG (2006) Defining a genomic radius for long-range enhancer action: duplicated conserved non-coding elements hold the key. Trends Genet 22: 5–10.

24. ShabalinAA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28: 1353–1358.

25. BenjaminiY, DraiD, ElmerG, KafkafiN, GolaniI (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125: 279–284.

26. CeramiE, GaoJ, DogrusozU, GrossBE, SumerSO, et al. (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2: 401–404.

27. GaoJ, AksoyBA, DogrusozU, DresdnerG, GrossB, et al. (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6: l1.

28. TaylorBS, SchultzN, HieronymusH, GopalanA, et al. (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18: 11–22.

29. GohaganJK, ProrokPC, HayesRB, KramerBS (2000) The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial of the National Cancer Institute: history, organization, and status. Control Clin Trials 21: 251S–272S.

30. ProrokPC, AndrioleGL, BresalierRS, BuysSS, ChiaD, et al. (2000) Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials 21: 273S–309S.

31. YeagerM, OrrN, HayesRB, JacobsKB, KraftP, et al. (2007) Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 39: 645–649.

32. AgarwalAK, GargA (2010) Enzymatic activity of the human 1-acylglycerol-3-phosphate-O-acyltransferase isoform 11: upregulated in breast and cervical cancers. J Lipid Res 51: 2143–2152.

33. WitteJS, GoddardKA, ContiDV, ElstonRC, LinJ, et al. (2000) Genomewide scan for prostate cancer-aggressiveness loci. Am J Hum Genet 67: 92–99.

34. SchwarzeSR, LuoJ, IsaacsWB, JarrardDF (2005) Modulation of CXCL14 (BRAK) expression in prostate cancer. Prostate 64: 67–74.

35. AugstenM, HagglofC, OlssonE, StolzC, TsagozisP, et al. (2009) CXCL14 is an autocrine growth factor for fibroblasts and acts as a multi-modal stimulator of prostate tumor growth. Proc Natl Acad Sci U S A 106: 3414–3419.

36. AugstenM, SjobergE, FringsO, VorrinkSU, FrijhoffJ, et al. (2014) Cancer-associated fibroblasts expressing CXCL14 rely upon NOS1-derived nitric oxide signaling for their tumor-supporting properties. Cancer Res 74: 2999–3010.

37. ChiaverottiT, CoutoSS, DonjacourA, MaoJH, NagaseH, et al. (2008) Dissociation of epithelial and neuroendocrine carcinoma lineages in the transgenic adenocarcinoma of mouse prostate model of prostate cancer. Am J Pathol 172: 236–246.

38. Ittmann M, Huang J, Radaelli E, Martin P, Signoretti S, et al.. (2013) Animal Models of Human Prostate Cancer: The Consensus Report of the New York Meeting of the Mouse Models of Human Cancers Consortium Prostate Pathology Committee. Cancer Res

39. ZhouZ, Flesken-NikitinA, CorneyDC, WangW, GoodrichDW, et al. (2006) Synergy of p53 and Rb deficiency in a conditional mouse model for metastatic prostate cancer. Cancer Res 66: 7889–7898.

40. EelesRA, OlamaAA, BenllochS, SaundersEJ, LeongamornlertDA, et al. (2013) Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet 45: 385–391.

41. FarberCR (2013) Systems-level analysis of genome-wide association data. G3 (Bethesda) 3: 119–129.

42. TruettGE, HeegerP, MynattRL, TruettAA, WalkerJA, et al. (2000) Preparation of PCR-quality mouse genomic DNA with hot sodium hydroxide and tris (HotSHOT). Biotechniques 29: 52–54.

43. GiubellinoA, ShankavaramU, BullovaP, SchovanekJ, ZhangY, et al. (2014) High-throughput screening for the identification of new therapeutic options for metastatic pheochromocytoma and paraganglioma. PLoS One 9: e90458.

44. ChurchillGA, DoergeRW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138: 963–971.

45. LanderES, BotsteinD (1986) Mapping complex genetic traits in humans: new methods using a complete RFLP linkage map. Cold Spring Harb Symp Quant Biol 51 Pt 1: 49–62.

46. StoreyJD, TibshiraniR (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440–9445.

47. MortazaviA, WilliamsBA, McCueK, SchaefferL, WoldB (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5: 621–628.

48. WangK, SinghD, ZengZ, ColemanSJ, HuangY, et al. (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38: e178.

49. LiB, DeweyCN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12: 323.