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Identification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data

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Abstract

Despite advances in classification, treatment, and imaging, neuroendocrine tumours remain a clinically complex subject. In this work, we studied the genetic profile of well-differentiated pancreatic neuroendocrine tumours (PanNETs) in a cohort of Caucasian patients and analysed the signalling pathways and candidate genes potentially associated with the development of this oncological disease. Twenty-four formalin-fixed paraffin-embedded (FFPE) samples of well-differentiated PanNETs were subjected to massive parallel sequencing using the targeted gene panel (409 genes) of the Illumina NextSeq 550 platform (San Diego, USA). In 24 patients, 119 variants were identified in 54 genes. The median mutation rate per patient was 5 (2.8–7). The detected genetic changes were dominated by missense mutations (67%) and nonsense mutations (29%). 18% of the mutations were activating, 35% of the variants led to a loss of function of the encoded protein, and 52% were not classified. Twenty-six variants were described as new. Functionally significant changes in the tertiary structure and activity of the protein molecules in an in silico assay were predicted for 5 new genetic variants. The 5 highest priority candidate genes were selected: CREB1, TCF12, PRKAR1A, BCL11A, and BUB1B. Genes carrying the identified mutations participate in signalling pathways known to be involved in PanNETs; in addition, 38% of the cases showed genetic changes in the regulation of the SMAD2/3 signalling pathway. Well-differentiated PanNETs in a Russian cohort demonstrate various molecular genetic features, including new genetic variations and potential driver genes. The highlighted molecular genetic changes in the SMAD2/3 signalling pathway suggest new prospects for targeted therapy.

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References

  1. Zhang J, Francois R, Iyer R et al (2013) Current understanding of the molecular biology of pancreatic neuroendocrine tumors. J Natl Cancer Inst 105:1005–1017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wu J, Sun C, Li E, Wang J et al (2019) Non-functional pancreatic neuroendocrine tumours: emerging trends in incidence and mortality. BMC Cancer 19:334

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chai SM, Brown IS, Kumarasinghe MP (2018) Gastroenteropancreatic neuroendocrine neoplasms: selected pathology review and molecular updates. Histopathology 72:153–167

    Article  PubMed  Google Scholar 

  4. Chou WC, Lin PH, Yeh YC et al (2016) Genes involved in angiogenesis and mTOR pathways are frequently mutated in Asian patients with pancreatic neuroendocrine tumors. Int J Biol Sci 12:1523–1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Scarpa A, Chang DK, Nones K et al (2017) Whole-genome landscape of pancreatic neuroendocrine tumours. Nature 543:65–71

    Article  CAS  PubMed  Google Scholar 

  6. Tirosh A, Mukherjee S, Lack J et al (2019) Distinct genome-wide methylation patterns in sporadic and hereditary nonfunctioning pancreatic neuroendocrine tumors. Cancer 125:1247–1257

    Article  CAS  PubMed  Google Scholar 

  7. Raj N, Shah R, Stadler Z et al (2018) Real-time genomic characterization of metastatic pancreatic neuroendocrine tumors has prognostic implications and identifies potential germline actionability. JCO Precis Oncol 2:1–18

    Google Scholar 

  8. Ji S, Yang W, Liu J et al (2018) High throughput gene sequencing reveals altered landscape in DNA damage responses and chromatin remodeling in sporadic pancreatic neuroendocrine tumors. Pancreatology 18:318–327

    Article  CAS  PubMed  Google Scholar 

  9. Leung HHW, Chan AWH (2019) Updates of pancreatic neuroendocrine neoplasmin the 2017 World Health Organization classification. Surg Pract 23:42–47

    Article  Google Scholar 

  10. Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474

    Article  PubMed  Google Scholar 

  11. Vaser R, Adusumalli S, Leng SN et al (2016) SIFT missense predictions for genomes. Nat Protoc 11:1–9

    Article  CAS  PubMed  Google Scholar 

  12. Choi Y, Chan AP (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31:2745–2747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39:e118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schwarz JM, Cooper DN, Schuelke M et al (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11:361–362

    Article  CAS  PubMed  Google Scholar 

  16. Shihab HA, Gough J, Mort M et al (2014) Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum Genomics 8:11

    Article  PubMed  PubMed Central  Google Scholar 

  17. Chun S, Fay JC (2009) Identification of deleterious mutations within three human genomes. Genome Res 19:1553–1561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Desmet FO, Hamroun D, Lalande M et al (2009) Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 37:e67

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Davydov EV, Goode DL, Sirota M et al (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol 6:e1001025

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Tamborero D, Rubio-Perez C, Deu-Pons J et al (2018) Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 10:25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rogers MF, Shihab HA, Gaunt TR et al (2017) CScape: a tool for predicting oncogenic single-point mutations in the cancer genome. Sci Rep 7:11597

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Li MM, Datto M, Duncavage EJ et al (2017) Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 19:4–23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Richards S, Aziz N, Bale S et al (2015) ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405–424

    Article  PubMed  PubMed Central  Google Scholar 

  24. Schroeder MP, Rubio-Perez C, Tamborero D et al (2014) OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action. Bioinformatics 30:i549–555

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Raudvere U, Kolberg L, Kuzmin I et al (2019) g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res 47:W191–W198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wu CH, Arighi CN, Ross KE (2017) Protein bioinformatics. Protein Mod Netw Proteom 1558:235–255

    CAS  Google Scholar 

  27. Witvliet DK, Strokach A, Giraldo-Forero AF et al (2016) ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 32:1589–1591

    Article  CAS  PubMed  Google Scholar 

  28. Rodrigues CH, Pires DE, Ascher DB (2018) DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res 46:W350–W355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Frappier V, Chartier M, Najmanovich RJ (2015) ENCoM server: exploring protein conformational space and the effect of mutations on protein function and stability. Nucleic Acids Res 43:W395–400

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pires DE, Ascher DB, Blundell TL (2014) mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30:335–342

    Article  CAS  PubMed  Google Scholar 

  31. Pandurangan AP, Ochoa-Montaño B, Ascher DB et al (2017) SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res 45:W229–W235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pires DE, Ascher DB, Blundell TL (2014) DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res 42:W314–319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Szklarczyk D, Gable AL, Lyon D et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613

    Article  CAS  PubMed  Google Scholar 

  34. Chen J, Bardes EE, Aronow BJ et al (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305–311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Thakker RV (2014) Multiple endocrine neoplasia type 1 (MEN1) and type 4 (MEN4). Mol Cell Endocrinol 386:2–15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Waterhouse A, Bertoni M, Bienert S et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:W296–W303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Goddard TD, Huang CC, Meng EC et al (2018) UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci 27:14–25

    Article  CAS  PubMed  Google Scholar 

  38. Dreijerink KMA, Timmers HTM, Brown M (2017) Twenty years of menin: emerging opportunities for restoration of transcriptional regulation in MEN1. Endocr Relat Cancer 24:T135–T145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vandamme T, Beyens M, Boons G et al (2019) Hotspot DAXX, PTCH2 and CYFIP2 mutations in pancreatic neuroendocrine neoplasms. Endocr Relat Cancer 26:1–12

    Article  CAS  PubMed  Google Scholar 

  40. Hong X, Qiao S, Li F et al (2020) Whole-genome sequencing reveals distinct genetic bases for insulinomas and non-functional pancreatic neuroendocrine tumours: leading to a new classification system. Gut 69:877–887

    Article  PubMed  Google Scholar 

  41. Okada T, Singer S (2017) Integrin-alpha10 drives tumorigenesis in sarcoma. Oncoscience 4:31–32

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hofsli E, Wheeler TE, Langaas M et al (2008) Identification of novel neuroendocrine-specific tumour genes. Br J Cancer 99:1330–1339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Helsten T, Elkin S, Arthur E et al (2016) The FGFR landscape in cancer: analysis of 4,853 tumors by next-generation sequencing. Clin Cancer Res 22:259–267

    Article  CAS  PubMed  Google Scholar 

  44. Rossi G, Bertero L, Marchiò C et al (2018) Molecular alterations of neuroendocrine tumours of the lung. Histopathology 72:142–152

    Article  PubMed  Google Scholar 

  45. Amorim JP, Santos G, Vinagre J et al (2016) The role of ATRX in the alternative lengthening of telomeres (ALT) phenotype. Genes (Basel) 7:E66

    Article  CAS  Google Scholar 

  46. Jiao Y, Shi C, Edil BH et al (2011) DAXX/ATRX, MEN1, and mTOR pathway genes are frequently altered in pancreatic neuroendocrine tumors. Science 331:1199–1203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fang Z, Lin A, Chen J et al (2016) CREB1 directly activates the transcription of ribonucleotide reductase small subunit M2 and promotes the aggressiveness of human colorectal cancer. Oncotarget 7:78055–78068

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jiang BY, Zhang XC, Su J et al (2013) BCL11A overexpression predicts survival and relapse in non-small cell lung cancer and is modulated by microRNA-30a and gene amplification. Mol Cancer 12:61

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Khaled WT, Choon Lee S, Stingl J et al (2015) BCL11A is a triple-negative breast cancer gene with critical functions in stem and progenitor cells. Nat Commun 6:5987

    Article  CAS  PubMed  Google Scholar 

  50. Zhu L, Pan R, Zhou D et al (2019) BCL11A enhances stemness and promotes progression by activating Wnt/β-catenin signaling in breast cancer. Cancer Manag Res 11:2997–3007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Huang GX, Chen QY, Zhong LL et al (2019) Primary malignant gastrointestinal neuroectodermal tumor occurring in the ileum with intra-abdominal granulomatous nodules: a case report and review of the literature. Oncol Lett 17:3899–3909

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Fernandez-Cuesta L, Peifer M, Lu X et al (2014) Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids. Nat Commun 5:3518

    Article  PubMed  CAS  Google Scholar 

  53. Saloustros E, Salpea P, Starost M et al (2017) Prkar1a gene knockout in the pancreas leads to neuroendocrine tumorigenesis. Endocr Relat Cancer 24:31–40

    Article  CAS  PubMed  Google Scholar 

  54. Kamilaris CDC, Stratakis CA (2018) An update on adrenal endocrinology: significant discoveries in the last 10 years and where the field is heading in the next decade. Hormones (Athens) 17:479–490

    Article  Google Scholar 

  55. Lee CC, Chen WS, Chen CC et al (2012) TCF12 protein functions as transcriptional repressor of E-cadherin, and its overexpression is correlated with metastasis of colorectal cancer. J Biol Chem 287:2798–2809

    Article  CAS  PubMed  Google Scholar 

  56. He J, Shen S, Lu W et al (2016) HDAC1 promoted migration and invasion binding with TCF12 by promoting EMT progress in gallbladder cancer. Oncotarget 7:32754–32764

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yang J, Zhang L, Jiang Z et al (2019) TCF12 promotes the tumorigenesis and metastasis of hepatocellular carcinoma via upregulation of CXCR4 expression. Theranostics 9:5810–5827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Chen YF, Yang CC, Kao SY et al (2016) MicroRNA-211 enhances the oncogenicity of carcinogen-induced oral carcinoma by repressing TCF12 and increasing antioxidant activity. Cancer Res 76:4872–4886

    Article  CAS  PubMed  Google Scholar 

  59. Chen QB, Liang YK, Zhang YQ et al (2017) Decreased expression of TCF12 contributes to progression and predicts biochemical recurrence in patients with prostate cancer. Tumour Biol 39:1010428317703924

    PubMed  Google Scholar 

  60. Chen H, Lee J, Kljavin NM et al (2015) Requirement for BUB1B/BUBR1 in tumor progression of lung adenocarcinoma. Genes Cancer 6:106–118

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Yamamoto Y, Matsuyama H, Chochi Y et al (2007) Overexpression of BUBR1 is associated with chromosomal instability in bladder cancer. Cancer Genet Cytogenet 174:42–47

    Article  CAS  PubMed  Google Scholar 

  62. Ando K, Kakeji Y, Kitao H et al (2010) High expression of BUBR1 is one of the factors for inducing DNA aneuploidy and progression in gastric cancer. Cancer Sci 101:639–645

    Article  CAS  PubMed  Google Scholar 

  63. Tanaka K, Mohri Y, Ohi M et al (2008) Mitotic checkpoint genes, hsMAD2 and BubR1, in oesophageal squamous cancer cells and their association with 5-fluorouracil and cisplatin-based radiochemotherapy. Clin Oncol (R Coll Radiol) 20:639–646

    Article  CAS  Google Scholar 

  64. Yuan B, Xu Y, Woo JH et al (2006) Increased expression of mitotic checkpoint genes in breast cancer cells with chromosomal instability. Clin Cancer Res 12:405–410

    Article  CAS  PubMed  Google Scholar 

  65. Liu AW, Cai J, Zhao XL et al (2009) The clinicopathological significance of BUBR1 overexpression in hepatocellular carcinoma. J Clin Pathol 62:1003–1008

    Article  CAS  PubMed  Google Scholar 

  66. Zhao M, Mishra L, Deng CX (2018) The role of TGF-β/SMAD4 signaling in cancer. Int J Biol Sci 14:111–123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Rohira AD, Yan F, Wang L et al (2017) Targeting SRC coactivators blocks the tumor-initiating capacity of cancer stem-like cells. Cancer Res 77:4293–4304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kidd M, Schimmack S, Lawrence B et al (2013) EGFR/TGFα and TGFβ/CTGF signaling in neuroendocrine neoplasia: theoretical therapeutic targets. Neuroendocrinology 97:35–44

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We express gratitude for the support in the pathology validation of tumour samples from Karnauchov N.S. (head of the pathology department of Rostov Research Institute of Oncology) and Ponkina O.N. (head of the pathology department of Krasnodar regional hospital No. 1)

Funding

We received no financial support from grants of pharmaceutical or other commercial organizations.

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Authors and Affiliations

  1. Department of Abdominal Oncology No. 1, Rostov Research Institute of Oncology, Rostov-on-Don, Russia

    Oleg I. Kit & Vladimir S. Trifanov

  2. Laboratory of Molecular Oncology, Rostov Research Institute of Oncology, 14 line, 6, Rostov-on-Don, Russia, 344037

    Nataliya A. Petrusenko, Dmitry Y. Gvaldin, Denis S. Kutilin & Nataliya N. Timoshkina

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  1. Oleg I. Kit
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  2. Vladimir S. Trifanov
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  3. Nataliya A. Petrusenko
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Contributions

KOI: conception of the research, surgery and collection of clinical data. TVS: collection of the clinical data after the operation. Clinical control of the patients. GDY: bioinformatical analysis, visualization of obtained results, writing of the article. KDS: conducted NGS research. Analysis of the obtained data. PNA: conducted NGS research. TNN: writing and editing of the article text and data analysis.

Corresponding author

Correspondence to Dmitry Y. Gvaldin.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the ethics committee of the Rostov Research Institute of Oncology (RRIO) (protocol No. 9/1 of 04.03.2019) and has been performed in accordance with the ethical standarts laid down in the 2013 Declaration of Helsinki. Signed informed consent was obtained from all patients included in the study.

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Electronic supplementary material

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Online Resource 1. Characterization of de novo genetic variants (PDF 143 kb)

Online Resource 2. Genetic variants associated with hereditary syndromes (PDF 53 kb)

11033_2020_5534_MOESM3_ESM.pdf

Online Resource 3. The ranking of candidate genes based on their functional similarities with theknown genes participating in the oncogenesis of PanNETs (PDF 49 kb)

11033_2020_5534_MOESM4_ESM.pdf

Online Resource 4. Genes involved in regulation of the SMAD2/3 signalling pathway andchromatin remodelling (A), RAS signalling pathway and angiogenesis (B), histone methylationand the mTOR signalling pathway (C). (PDF 118 kb)

11033_2020_5534_MOESM5_ESM.xlsx

Online Resource 5. General characteristics of the genetic variants identified by the nextgeneration sequencing method (XLSX 17 kb)

Online Resource 6. Analysis of effect genetic variants in signal pathways by program“OncodriveROLE” (XLSX 13 kb)

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Kit, O.I., Trifanov, V.S., Petrusenko, N.A. et al. Identification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data. Mol Biol Rep 47, 4233–4243 (2020). https://doi.org/10.1007/s11033-020-05534-z

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