Jul 20, 2021: Check out information on the new Japanese database from Medical Data Vision

The new Medical Data Vision (EBM Provider®) Health Insurance Association Database contains inpatient and outpatient information from different institutions. Data are available since April 2012 containing >6 million patient data from more than 120 health insurance associations. The database has diagnosis, procedure, drug and cost data.

Jul 15, 2021: Our 15th Australian profile – a Biobank!

The Australian Breast Cancer Tissue Bank (ABCTB) is an open access, not-for-profit biobank established in 2005 containing 7,134 donors, and 156,080 available samples as of June 2021. The ABCTB holds samples of cancerous and normal breast tissue and blood collected during routine pathology from women (and men) with breast cancer, as well as additional information about the health and breast cancer treatment of donors.

Australian Breast Cancer Tissue Bank (ABCTB) (Australia)

Database Contact Data

Westmead Biobank
E-mail: westmead.biobank@sydney.edu.au

Alternate Contact

www.westmead.org.au/core-facilities/our-facilities/biobanking/

References of Studies Using/Describing Database

1. Coignard J, Lush M, Beesley J, O’mara TA, Dennis J, Tyrer JP, Barnes DR, McGuffog L, Leslie G, Bolla MK, Adank MA. A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nature communications. 2021 Feb 17;12(1):1-22.

2. Doan TB, Cheung V, Clyne CD, Hilton HN, Eriksson N, Young MJ, Funder JW, Muscat GE, Fuller PJ, Clarke CL, Graham JD. A tumour suppressive relationship between mineralocorticoid and retinoic acid receptors activates a transcriptional program consistent with a reverse Warburg effect in breast cancer. Breast Cancer Research. 2020 Dec;22(1):1-6.

3. Heng B, Bilgin AA, Lovejoy DB, Tan VX, Milioli HH, Gluch L, Bustamante S, Sabaretnam T, Moscato P, Lim CK, Guillemin GJ. Differential kynurenine pathway metabolism in highly metastatic aggressive breast cancer subtypes: beyond IDO1-induced immunosuppression. Breast Cancer Research. 2020 Dec;22(1):1-4.

4. Naik N, Madani A, Esteva A, Keskar NS, Press MF, Ruderman D, Agus DB, Socher R. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nature communications. 2020 Nov 16;11(1):1-8.

5. de Luca XM, Newell F, Kazakoff SH, Hartel G, Reed AE, Holmes O, Xu Q, Wood S, Leonard C, Pearson JV, Lakhani SR. Using whole-genome sequencing data to derive the homologous recombination deficiency scores. NPJ breast cancer. 2020 Aug 7;6(1):1-8.

6. Liu J, Prager-van der Smissen WJ, Collée JM, Bolla MK, Wang Q, Michailidou K, Dennis J, Ahearn TU, Aittomäki K, Ambrosone CB, Andrulis IL. Germline HOXB13 mutations p. G84E and p. R217C do not confer an increased breast cancer risk. Scientific reports. 2020 Jun 16;10(1):1-4.

7. Rawat RR, Ortega I, Roy P, Sha F, Shibata D, Ruderman D, Agus DB. Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images. Scientific reports. 2020 Apr 29;10(1):1-3.

8. ABCTB Investigators, NBCS Collaborators. Two truncating variants in FANCC and breast cancer risk. Scientific reports. 2019 Dec 1;9(1):12524.

9. Horas K, Zheng Y, Fong‐Yee C, Macfarlane E, Manibo J, Chen Y, Qiao J, Gao M, Haydar N, McDonald MM, Croucher PI. Loss of the vitamin D receptor in human breast cancer cells promotes epithelial to mesenchymal cell transition and skeletal colonization. Journal of Bone and Mineral Research. 2019 Sep;34(9):1721-32.

10. Morten BC, Chiu S, Oldmeadow C, Lubinski J, Scott RJ, Avery-Kiejda KA. The intron 3 16 bp duplication polymorphism of p53 (rs17878362) is not associated with increased risk of developing triple-negative breast cancer. Breast cancer research and treatment. 2019 Feb;173(3):727-33.

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