Familial Cancer

, Volume 17, Issue 1, pp 141–153 | Cite as

Potentially pathogenic germline CHEK2 c.319+2T>A among multiple early-onset cancer families

  • Mev Dominguez-ValentinEmail author
  • Sigve Nakken
  • Hélène Tubeuf
  • Daniel Vodak
  • Per Olaf Ekstrøm
  • Anke M. Nissen
  • Monika Morak
  • Elke Holinski-Feder
  • Alexandra Martins
  • Pål Møller
  • Eivind Hovig
Original Article


To study the potential contribution of genes other than BRCA1/2, PTEN, and TP53 to the biological and clinical characteristics of multiple early-onset cancers in Norwegian families, including early-onset breast cancer, Cowden-like and Li-Fraumeni-like syndromes (BC, CSL and LFL, respectively). The Hereditary Cancer Biobank from the Norwegian Radium Hospital was used to identify early-onset BC, CSL or LFL for whom no pathogenic variants in BRCA1/2, PTEN, or TP53 had been found in routine diagnostic DNA sequencing. Forty-four cancer susceptibility genes were selected and analyzed by our in-house designed TruSeq amplicon-based assay for targeted sequencing. Protein- and RNA splicing-dedicated in silico analyses were performed for all variants of unknown significance (VUS). Variants predicted as the more likely to affect splicing were experimentally analyzed by minigene assay. We identified a CSL individual carrying a variant in CHEK2 (c.319+2T>A, IVS2), here considered as likely pathogenic. Out of the five VUS (BRCA2, CDH1, CHEK2, MAP3K1, NOTCH3) tested in the minigene splicing assay, only NOTCH3 c.14090C>T (p.Ser497Leu) showed a significant effect on RNA splicing, notably by inducing partial skipping of exon 9. Among 13 early-onset BC, CSL and LFL patients, gene panel sequencing identified a potentially pathogenic variant in CHEK2 that affects a canonical RNA splicing signal. Our study provides new information on genetic loci that may affect the risk of developing cancer in these patients and their families, demonstrating that genes presently not routinely tested in molecular diagnostic settings may be important for capturing cancer predisposition in these families.


Early-onset breast cancer Cowden-like syndrome Li-Fraumeni-like syndrome Gene panel testing CHEK2 RNA splicing mutations 



American College of Medical Genetics and Genomics


Breast cancer


Cowden syndrome


Colorectal cancer


Cowden syndrome like


Li-Fraumeni syndrome


Li-Fraumeni like syndrome


Mismatch repair genes


Next generation sequencing


Single nucleotide polymorphism


Single-nucleotide variants


Variants of unclassified significance


Wild type



We thank the included families for their participation and contribution to this study.


This work was supported by the Radium Hospital Foundation (Oslo, Norway), Helse Sør-Øst (Norway), the French Association Recherche contre le Cancer (ARC), the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (Gefluc), the Association Nationale de la Recherche et de la Technologie (ANRT, CIFRE PhD fellowship to H.T.) and by the OpenHealth Institute.

Compliance with ethical standards

Conflict of interests

The authors declare that they have no competing interests.

Supplementary material

10689_2017_11_MOESM1_ESM.docx (226 kb)
Supplementary material 1 (DOCX 226 KB)
10689_2017_11_MOESM2_ESM.docx (16 kb)
Supplementary material 2 (DOCX 16 KB)


  1. 1.
    Kurian AW, Hare EE, Mills MA et al (2014) Clinical evaluation of a multiple-gene sequencing panel for hereditary cancer risk assessment. J Clin Oncol 32(19):2001–2009. doi: 10.1200/JCO.2013.53.6607 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Hegde M, Ferber M, Mao R et al (2014) ACMG technical standards and guidelines for genetic testing for inherited colorectal cancer (lynch syndrome, familial adenomatous polyposis, and MYH-associated polyposis). Genet Med 16(1):101–116. doi: 10.1038/gim.2013.166 CrossRefPubMedGoogle Scholar
  3. 3.
    Richards S, Aziz N, Bale S et al (2015) 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(5):405–424. doi: 10.1038/gim.2015.30 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kleinberger J, Maloney KA, Pollin TI, Jeng LJ (2016) An openly available online tool for implementing the ACMG/AMP standards and guidelines for the interpretation of sequence variants. Genet Med 18(11):1165. doi: 10.1038/gim.2016.13 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Amendola LM, Jarvik GP, Leo MC et al (2016) Performance of ACMG-AMP variant-interpretation guidelines among nine laboratories in the clinical sequencing exploratory research consortium (vol 98, pg 1067, 2016). Am J Hum Genet 99(1):247. doi: 10.1016/j.ajhg.2016.06.001 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Maxwell KN, Hart SN, Vijai J et al (2016) Evaluation of ACMG-guideline-based variant classification of cancer susceptibility and non-cancer-associated genes in families affected by breast cancer. Am J Hum Genet 98(5):801–817. doi: 10.1016/j.ajhg.2016.02.024 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Park KS, Cho EY, Nam SJ, Ki CS, Kim JW (2016) Comparative analysis of BRCA1 and BRCA2 variants of uncertain significance in patients with breast cancer: a multifactorial probability-based model versus ACMG standards and guidelines for interpreting sequence variants. Genet Med 18(12):1250–1257. doi: 10.1038/gim.2016.39 CrossRefPubMedGoogle Scholar
  8. 8.
    Cybulski C, Nazarali S, Narod SA (2014) Multiple primary cancers as a guide to heritability. Int J Cancer 135(8):1756–1763. doi: 10.1002/ijc.28988 CrossRefPubMedGoogle Scholar
  9. 9.
    Malone KE, Daling JR, Thompson JD, O’Brien CA, Francisco LV, Ostrander EA (1998) BRCA1 mutations and breast cancer in the general population - Analyses in women before age 35 years and in women before age 45 years with first-degree family history. Jama 279(12):922–929 doi: 10.1001/jama.279.12.922 CrossRefPubMedGoogle Scholar
  10. 10.
    Peto J, Collins N, Barfoot R et al (1999) Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst 91(11):943–949CrossRefPubMedGoogle Scholar
  11. 11.
    Loizidou M, Marcou Y, Anastasiadou V, Newbold R, Hadjisavvas A, Kyriacou K (2007) Contribution of BRCA1 and BRCA2 germline mutations to the incidence of early-onset breast cancer in Cyprus. Clin Genet 71(2):165–170. doi: 10.1111/j.1399-0004.2007.00747.x CrossRefPubMedGoogle Scholar
  12. 12.
    Moller P, Hagen AI, Apold J et al (2007) Genetic epidemiology of BRCA mutations—family history detects less than 50% of the mutation carriers. Eur J Cancer 43(11):1713–1717. doi: 10.1016/j.ejca.2007.04.023 CrossRefPubMedGoogle Scholar
  13. 13.
    Pradella LM, Evangelisti C, Ligorio C et al (2014) A novel deleterious PTEN mutation in a patient with early-onset bilateral breast cancer. Bmc. Cancer 14:70. doi: 10.1186/1471-2407-14-70 PubMedPubMedCentralGoogle Scholar
  14. 14.
    Moller P, Stormorken A, Holmen MM, Hagen AI, Vabo A, Maehle L (2014) The clinical utility of genetic testing in breast cancer kindreds: a prospective study in families without a demonstrable BRCA mutation. Breast Cancer Res Treat 144(3):607–614. doi: 10.1007/s10549-014-2902-1 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Mavaddat N, Peock S, Frost D et al (2013) Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst 105(11):812–822. doi: 10.1093/jnci/djt095 CrossRefPubMedGoogle Scholar
  16. 16.
    Wu CC, Shete S, Amos CI, Strong LC (2006) Joint effects of germ-line p53 mutation and sex on cancer risk in Li-Fraumeni syndrome. Cancer Res 66(16):8287–8292. doi: 10.1158/0008-5472.Can-05-4247 CrossRefPubMedGoogle Scholar
  17. 17.
    Pharoah PDP, Guilford P, Caldas C, Consortiu IGCL (2001) Incidence of gastric cancer and breast cancer in CDH1 (E-cadherin) mutation carriers from hereditary diffuse gastric cancer families. Gastroenterology 121(6):1348–1353. doi: 10.1053/gast.2001.29611 CrossRefPubMedGoogle Scholar
  18. 18.
    Hearle N, Schumacher V, Menko FH et al (2006) Frequency and spectrum of cancers in the Peutz-Jeghers syndrome. Clin Cancer Res 12(10):3209–3215. doi: 10.1158/1078-0432.Ccr-06-0083 CrossRefPubMedGoogle Scholar
  19. 19.
    Bubien V, Bonnet F, Brouste V et al (2013) High cumulative risks of cancer in patients with PTEN hamartoma tumour syndrome. J Med Genet 50(4):255–263. doi: 10.1136/jmedgenet-2012-101339 CrossRefPubMedGoogle Scholar
  20. 20.
    Rustad CF, Bjornslett M, Heimdal KR, Maehle L, Apold J, Moller P (2006) Germline PTEN mutations are rare and highly penetrant. Hered Cancer Clin Pract 4(4):177–185. doi: 10.1186/1897-4287-4-4-177 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Aloraifi F, McCartan D, McDevitt T, Green AJ, Bracken A, Geraghty J (2015) Protein-truncating variants in moderate-risk breast cancer susceptibility genes: a meta-analysis of high-risk case-control screening studies. Cancer Genet 208(9):455–463. doi: 10.1016/j.cancergen.2015.06.001 CrossRefPubMedGoogle Scholar
  22. 22.
    Antoniou AC, Casadei S, Heikkinen T et al (2014) Breast-cancer risk in families with mutations in PALB2. N Engl J Med 371(6):497–506. doi: 10.1056/NEJMoa1400382 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hobert JA, Eng C (2009) PTEN hamartoma tumor syndrome: an overview. Genet Med 11(10):687–694. doi: 10.1097/GIM.0b013e3181ac9aea CrossRefPubMedGoogle Scholar
  24. 24.
    Daly MB, Axilbund JE, Buys S et al (2010) Genetic/familial high-risk assessment: breast and ovarian. J Natl Compr Canc Netw 8(5):562–594CrossRefPubMedGoogle Scholar
  25. 25.
    Tan MH, Mester J, Peterson C et al (2011) A clinical scoring system for selection of patients for PTEN mutation testing is proposed on the basis of a prospective study of 3042 probands. Am J Hum Genet 88(1):42–56. doi: 10.1016/j.ajhg.2010.11.013 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Ngeow J, Sesock K, Eng C (2015) Breast cancer risk and clinical implications for germline PTEN mutation carriers. Breast Cancer Res Treat. doi: 10.1007/s10549-015-3665-z PubMedGoogle Scholar
  27. 27.
    Mester J, Eng C (2015) Cowden syndrome: recognizing and managing a not-so-rare hereditary cancer syndrome. J Surg Oncol 111(1):125–130. doi: 10.1002/jso.23735 CrossRefPubMedGoogle Scholar
  28. 28.
    Birch JM, Hartley AL, Tricker KJ et al (1994) Prevalence and diversity of constitutional mutations in the P53 Gene among 21 Li-Fraumeni families. Cancer Res 54(5):1298–1304PubMedGoogle Scholar
  29. 29.
    Malkin D, Li FP, Strong LC et al (1990) Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science 250(4985):1233–1238CrossRefPubMedGoogle Scholar
  30. 30.
    Srivastava S, Zou ZQ, Pirollo K, Blattner W, Chang EH (1990) Germ-line transmission of a mutated p53 gene in a cancer-prone family with Li-Fraumeni syndrome. Nature 348(6303):747–749. doi: 10.1038/348747a0 CrossRefPubMedGoogle Scholar
  31. 31.
    Varley JM (2003) Germline TP53 mutations and Li-Fraumeni syndrome. Hum Mutat 21(3):313–320. doi: 10.1002/humu.10185 CrossRefPubMedGoogle Scholar
  32. 32.
    Olivier M, Eeles R, Hollstein M, Khan MA, Harris CC, Hainaut P (2002) The IARC TP53 database: new online mutation analysis and recommendations to users. Hum Mutat 19(6):607–614. doi: 10.1002/humu.10081 CrossRefPubMedGoogle Scholar
  33. 33.
    Li L, Chen HC, Liu LX (2009) Sequence alignment algorithm in similarity measurement. Int Forum Info Technol Appl Proc 2009 1:453–456 doi: 10.1109/Ifita.2009.119 Google Scholar
  34. 34.
    McKenna A, Hanna M, Banks E et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303. doi: 10.1101/gr.107524.110 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucl Acids Res 38(16):e164. doi: 10.1093/nar/gkq603 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Sherry ST, Ward MH, Kholodov M et al (2001) dbSNP: the NCBI database of genetic variation. Nucl Acids Res 29(1):308–311CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Genomes Project C, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526(7571):68–74. doi: 10.1038/nature15393 CrossRefGoogle Scholar
  38. 38.
    Lek M, Karczewski KJ, Minikel EV et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616):285–291. doi: 10.1038/nature19057 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Landrum MJ, Lee JM, Riley GR et al (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucl Acids Res 42(D1):D980–D985. doi: 10.1093/nar/gkt1113 CrossRefPubMedGoogle Scholar
  40. 40.
    Apweiler R, Bairoch A, Wu CH et al (2004) UniProt: the Universal Protein knowledgebase. Nucl Acids Res 32:D115–D119. doi: 10.1093/nar/gkh131 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucl Acids Res 42(D1):D222–D230. doi: 10.1093/nar/gkt1223 CrossRefPubMedGoogle Scholar
  42. 42.
    den Dunnen JT, Antonarakis SE (2000) Mutation nomenclature extensions and suggestions to describe complex mutations: A discussion. Hum Mutat 15(1):7–12CrossRefPubMedGoogle Scholar
  43. 43.
    Thompson BA, Spurdle AB, Plazzer JP et al (2014) Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database. Nat Genet 46(2):107–115. doi: 10.1038/ng.2854 CrossRefPubMedGoogle Scholar
  44. 44.
    Houdayer C, Caux-Moncoutier V, Krieger S et al (2012) Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants. Hum Mutat 33(8):1228–1238. doi: 10.1002/humu.22101 CrossRefPubMedGoogle Scholar
  45. 45.
    Di Giacomo D, Gaildrat P, Abuli A et al (2013) Functional analysis of a large set of BRCA2 exon 7 variants highlights the predictive value of hexamer scores in detecting alterations of exonic splicing regulatory elements. Hum Mutat 34(11):1547–1557. doi: 10.1002/humu.22428 CrossRefPubMedGoogle Scholar
  46. 46.
    Erkelenz S, Hillebrand F, Widera M et al (2015) Balanced splicing at the Tat-specific HIV-1 3’ss A3 is critical for HIV-1 replication. Retrovirology 12:29. doi: 10.1186/s12977-015-0154-8 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Xiong HY, Alipanahi B, Lee LJ et al (2015) The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218):1254806. doi: 10.1126/science.1254806 CrossRefPubMedGoogle Scholar
  48. 48.
    Soukarieh O, Gaildrat P, Hamieh M et al (2016) Exonic splicing mutations are more prevalent than currently estimated and can be predicted by using in silico tools. PLoS Genet 12(1):e1005756. doi: 10.1371/journal.pgen.1005756 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Tavtigian SV, Deffenbaugh AM, Yin L et al (2006) Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet 43(4):295–305. doi: 10.1136/jmg.2005.033878 CrossRefPubMedGoogle Scholar
  50. 50.
    Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4(7):1073–1082. doi: 10.1038/nprot.2009.86 CrossRefPubMedGoogle Scholar
  51. 51.
    Stone EA, Sidow A (2005) Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res 15(7):978–986. doi: 10.1101/gr.3804205 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(4):248–249. doi: 10.1038/nmeth0410-248 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11(4):361–362. doi: 10.1038/nmeth.2890 CrossRefPubMedGoogle Scholar
  54. 54.
    Gaildrat P, Killian A, Martins A, Tournier I, Frebourg T, Tosi M (2010) Use of splicing reporter minigene assay to evaluate the effect on splicing of unclassified genetic variants. Methods Mol Biol 653:249–257. doi: 10.1007/978-1-60761-759-4$415 CrossRefPubMedGoogle Scholar
  55. 55.
    Landrum MJ, Lee JM, Benson M et al (2016) ClinVar: public archive of interpretations of clinically relevant variants. Nucl Acids Res 44(D1):D862–D868. doi: 10.1093/nar/gkv1222 CrossRefPubMedGoogle Scholar
  56. 56.
    Kalia SS, Adelman K, Bale SJ et al (2017) Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med 19(2):249–255. doi: 10.1038/gim.2016.190 CrossRefPubMedGoogle Scholar
  57. 57.
    Eng C (2000) Will the real Cowden syndrome please stand up: revised diagnostic criteria. J Med Genet 37(11):828–830CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Ngeow J, Liu C, Zhou K, Frick KD, Matchar DB, Eng C (2015) Detecting germline PTEN mutations among at-risk patients with cancer: an age- and sex-specific cost-effectiveness analysis. J Clin Oncol 33(23):2537–2581. doi: 10.1200/Jco.2014.60.3456 CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Hamblin A, Wordsworth S, Fermont JM et al (2017) Clinical applicability and cost of a 46-gene panel for genomic analysis of solid tumours: retrospective validation and prospective audit in the UK National Health Service. PLoS Med 14(2):e1002230. doi: 10.1371/journal.pmed.1002230 CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Pinto P, Paulo P, Santos C et al (2016) Implementation of next-generation sequencing for molecular diagnosis of hereditary breast and ovarian cancer highlights its genetic heterogeneity. Breast Cancer Res Treat 159(2):245–256. doi: 10.1007/s10549-016-3948-z CrossRefPubMedGoogle Scholar
  61. 61.
    Yadav S, Fulbright J, Dreyfuss H et al (2015) Outcomes of retesting BRCA-negative patients using multigene panels. J Clin Oncol 33(Suppl 28):23Google Scholar
  62. 62.
    Yurgelun MB, Kulke MH, Fuchs CS et al (2017) Cancer susceptibility gene mutations in individuals with colorectal cancer. J Clin Oncol. doi: 10.1200/JCO.2016.71.0012 Google Scholar
  63. 63.
    Lincoln SE, Kobayashi Y, Anderson MJ et al (2015) A systematic comparison of traditional and multigene panel testing for hereditary breast and ovarian cancer genes in more than 1000 patients. J Mol Diagn 17(5):533–544. doi: 10.1016/j.jmoldx.2015.04.009 CrossRefPubMedGoogle Scholar
  64. 64.
    Antoniou AC, Foulkes WD, Tischkowitz M (2014) Breast-cancer risk in families with mutations in PALB2 reply. New Engl J Med 371(17):1651–1652PubMedGoogle Scholar
  65. 65.
    Krepischi AC, Pearson PL, Rosenberg C (2012) Germline copy number variations and cancer predisposition. Future Oncol 8(4):441–450. doi: 10.2217/fon.12.34 CrossRefPubMedGoogle Scholar
  66. 66.
    Villacis RA, Basso TR, Canto LM et al (2017) Rare germline alterations in cancer-related genes associated with the risk of multiple primary tumor development. J Mol Med. doi: 10.1007/s00109-017-1507-7 PubMedGoogle Scholar
  67. 67.
    Villacis RA, Miranda PM, Gomy I et al (2016) Contribution of rare germline copy number variations and common susceptibility loci in Lynch syndrome patients negative for mutations in the mismatch repair genes. Int J Cancer 138(8):1928–1935. doi: 10.1002/ijc.29948 CrossRefPubMedGoogle Scholar
  68. 68.
    O’Keefe C, McDevitt MA, Maciejewski JP (2010) Copy neutral loss of heterozygosity: a novel chromosomal lesion in myeloid malignancies. Blood 115(14):2731–2739. doi: 10.1182/blood-2009-10-201848 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Mev Dominguez-Valentin
    • 1
    Email author
  • Sigve Nakken
    • 1
  • Hélène Tubeuf
    • 2
    • 3
  • Daniel Vodak
    • 1
  • Per Olaf Ekstrøm
    • 1
  • Anke M. Nissen
    • 4
    • 5
  • Monika Morak
    • 4
    • 5
  • Elke Holinski-Feder
    • 4
    • 5
  • Alexandra Martins
    • 2
  • Pål Møller
    • 1
    • 6
    • 7
  • Eivind Hovig
    • 1
    • 8
    • 9
  1. 1.Department of Tumor Biology, Institute for Cancer ResearchOslo University HospitalOsloNorway
  2. 2.Normandy Centre for Genomic and Personalized MedicineInserm-U1245, UNIROUEN, Normandie UnivRouenFrance
  3. 3.Interactive BiosoftwareRouenFrance
  4. 4.Medizinische Klinik und Poliklinik IV, Campus InnenstadtKlinikum der Universität MünchenMunichGermany
  5. 5.MGZ—Medizinisch Genetisches ZentrumMunichGermany
  6. 6.Department of Human MedicineUniversität Witten/HerdeckeWittenGermany
  7. 7.Department of Medical GeneticsOslo University HospitalOsloNorway
  8. 8.Department of InformaticsUniversity of OsloOsloNorway
  9. 9.Instituteof Cancer Genetics and InformaticsOslo University HospitalOsloNorway

Personalised recommendations