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Biobanks and Databases

The SUN Study Biobank


The SUN Study Biobank is a repository of sequential samples of human serum, plasma and DNA from patients with prostate cancer and healthy volunteers. Samples are collected every 6 months from the moment of diagnosis, pre-treatment and then after treatment and follow up.  Funding is in place to expand the SUN study to collect samples from patients with GI malignancies and breast cancer and in the future to also include ovarian cancer. The ethics approval is for biomarker discovery in all types of cancer. The SUN Study Biobank is a member of the NCRI Confederation of Biobanks.
https://www.ncri.org.uk/about-us/

Contact: Dr Agnieszka Michael
amichael@nhs.net

Publications resulting from the use of samples donated from the SUN study biobank:

1) Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study.

Adams C, Richmond RC, Santos Ferreira DL, et al.

Cancer Epidemiol Biomarkers Prev. 2018 Oct 23.

2) Large-scale transcriptome-wide association study identifies new prostate cancer risk regions.

Mancuso N, Gayther S, Gusev A, Zheng W, Penney KL, Kote-Jarai Z, Eeles R, Freedman M, Haiman C, Pasaniuc B; PRACTICAL consortium.

Nat Commun. 2018 Oct 4;9(1):4079

3) Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma.

Went M, Sud A, Försti A, et al.

Nat Commun. 2018 Sep 13;9(1):3707.

4) Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

Dadaev T, Saunders EJ, Newcombe PJ, et al.

Nat Commun. 2018 Jun 11;9(1):2256.

5) Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Schumacher FR, Al Olama AA, Berndt SI, et al.

Nat Genet. 2018 Jul;50(7):928-936.

6) AA9int: SNP Interaction Pattern Search Using Non-Hierarchical Additive Model Set.

Lin HY, Huang PY, Chen DT, et al.

Bioinformatics. 2018 Jun 7

7) Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

Saunders EJ, Dadaev T, Leongamornlert DA, et al.

Br J Cancer. 2018 Mar 20;118(6):e9.

8) Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts.

Seibert TM, Fan CC, Wang Y, et al.

BMJ. 2018 Jan 10;360:j5757.

9) Circulating vitamin D concentration and risk of seven cancers: Mendelian randomisation study.

Dimitrakopoulou VI, Tsilidis KK, Haycock P et al.

BMJ. 2017 Oct 31;359:j4761.

10) Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium.

Lophatananon A, Stewart-Brown S, Kote-Jarai Z, et al.

Br J Cancer. 2017 Aug 22;117(5):734-743.

11) Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer.

Zuber V, Bettella F, Witoelar A; PRACTICAL Consortium; CRUK GWAS; BCAC Consortium; TRICL Consortium, Andreassen OA, Mills IG, Urbanucci A.

BMC Genomics. 2017 Mar 31;18(1):270.

12) SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Lin HY, Chen DT, Huang PY, et al.

Bioinformatics. 2017 Mar 15;33(6):822-833.

13) Investigating the possible causal role of coffee consumption with prostate cancer risk and progression using Mendelian randomization analysis.

Taylor AE, Martin RM, Geybels MS, et al.

Int J Cancer. 2017 Jan 15;140(2):322-328.

14) Alcohol consumption and prostate cancer incidence and progression: A Mendelian randomisation study.

Brunner C, Davies NM, Martin RM, et al.

Int J Cancer. 2017 Jan 1;140(1):75-85

15) Polyunsaturated fatty acids and prostate cancer risk: a Mendelian randomisation analysis from the PRACTICAL consortium.

Khankari NK, Murff HJ, Zeng C, et al.

Br J Cancer. 2016 Aug 23;115(5):624-31

16) Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types.

Kar SP, Beesley J, Amin Al Olama A, et al.

Cancer Discov. 2016 Sep;6(9):1052-67.

17) Assessing the role of insulin-like growth factors and binding proteins in prostate cancer using Mendelian randomization: Genetic variants as instruments for circulating levels.

Bonilla C, Lewis SJ, Rowlands MA, et al.

Int J Cancer. 2016 Oct 1;139(7):1520-33.

18) Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Gusev A, Shi H, Kichaev G, et al.

Nat Commun. 2016 Apr 7;7:10979.

19) Pubertal development and prostate cancer risk: Mendelian randomization study in a population-based cohort.

Bonilla C, Lewis SJ, Martin RM, et al.

BMC Med. 2016 Apr 4;14:66.

20) Blood lipids and prostate cancer: a Mendelian randomization analysis.

Bull CJ, Bonilla C, Holly JM, et al.

Cancer Med. 2016 Jun;5(6):1125-36.

21) The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium.

Davies NM, Gaunt TR, Lewis SJ, et al.

Cancer Causes Control. 2015 Nov;26(11):1603-16

22) Prediction of individual genetic risk to prostate cancer using a polygenic score.

Szulkin R, Whitington T, Eklund M, et al.

Prostate. 2015 Sep;75(13):1467-74.

23) Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Amin Al Olama A, Dadaev T, Hazelett DJ, et al.

Hum Mol Genet. 2015 Oct 1;24(19):5589-602.

24) Genome-wide association study of prostate cancer-specific survival.

Szulkin R, Karlsson R, Whitington T, et al.

Cancer Epidemiol Biomarkers Prev. 2015 Nov;24(11):1796-800

25) Prediction of individual genetic risk to prostate cancer using a polygenic score.

Szulkin R, Whitington T, Eklund M, et al.

Prostate. 2015 Sep;75(13):1467-74

26) Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Amin Al Olama A, Dadaev T, et al

Hum Mol Genet. 2015 Oct 1;24(19):5589-602

27) A genome-wide pleiotropy scan for prostate cancer risk.

Panagiotou OA, Travis RC, Campa D et al.

Eur Urol. 2015 Apr;67(4):649-57.

28) A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer.

Al Olama AA, Kote-Jarai Z, Berndt SI, et al.

Nat Genet. 2014 Oct;46(10):1103-9


The BCSP database


The BCSP database (Bowel Cancer Screening System [BCSS]) provides a rich source of data for studies designed to enhance the performance of the BCSP.  The research team at the Southern Hub has access to the screening data for the south of England and can apply for access to data for England on a project-by-project basis. (Usual NHS NIGB Information Governance rules apply and research proposals must be approved by the BCSP Research Committee.)  The database is populated with the screening records for all individuals within the target age-range (7.7 million 60-74-year-olds in the gFOBt programme; xx million 55-year-olds in the FS programme). Records include screening activity (invitations, reminders, returned test kit details and results etc), data from SSP referrals and consultations, colonoscopy outcomes and pathology reports.

Contact: Helen Seaman, BCSP Southern Programme Hub Epidemiology Lead
helenseaman@nhs.net
Telephone: 01483 409855