Driving innovation through collaboration: development of clinical annotation datasets for brain cancer biobanking

• Published in: Neuro-oncology practice 2020-01, Vol.7 (1), p.31-37
• Main Authors: Gedye, Craig, Sachchithananthan, Mythily, Leonard, Robyn, Jeffree, Rosalind L, Buckland, Michael E, Ziegler, David S, Graeber, Manuel B, Day, Bryan W, McDonald, Kerrie L, Lasocki, Arian, Nowak, Anna K
• Format: Article
• Language: English
• Subjects: Original
• ISSN: 2054-2577; 2054-2585
• DOI: 10.1093/nop/npz036

Abstract Background A key component of cancer research is the availability of clinical samples with appropriately annotated clinical data. Biobanks facilitate research by collecting/storing various types of clinical samples for research. Brain Cancer Biobanking Australia (BCBA) was established to facilitate the networking of brain cancer biobanking operations Australia-wide. Maximizing biospecimen utility in a networked biobanking environment requires the standardization of procedures and data across different sites. The aim of this research was to scope and develop a recommended clinical annotation dataset both for pediatric and adult brain cancer biobanks. Methods A multidisciplinary working group consisting of members from the BCBA Consortium was established to develop clinical dataset recommendations for brain cancer biobanks. A literature search was undertaken to identify any published clinical dataset recommendations for brain cancer biobanks. An audit of data items collected and stored by BCBA member biobanks was also conducted to survey current clinical data collection practices across the BCBA network. Results BCBA has developed a clinical annotation dataset recommendation for pediatric and adult brain cancer biobanks. The clinical dataset recommendation has 5 clinical data categories: demographic, clinical and radiological diagnosis and surgery, neuropathological diagnosis, patient treatment, and patient follow-up. The data fields have been categorized into 1 of 3 tiers; essential, preferred, and comprehensive. This enables biobanks and researchers to prioritize appropriately where resources are limited. Conclusion This dataset can be used to guide the integration of data from multiple existing biobanks for research studies and for planning prospective brain cancer biobanking activities.