! Data Management Systems and Practices of the Cancer Institute NSW Biobanking Stakeholders Network Report and Recommendations

Commissioned by A/Prof Kevin Spring*, Centre for Oncology Education & Research Translation (CONCERT), and submitted to the Biobanking Stakeholders Network (BSN) as part of the project entitled “Towards Harmonisation of Biobanking Data Management Systems with CINSW BSN”.

Dr Jeff Christiansen and Jake Farrell

* For further information regarding this report please contact A/Prof Kevin Spring ([email protected])

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! ! Table of Contents

1! Background ...... 7!

2! Aims ...... 11!

3! Methods ...... 12! 3.1! Identification of biobanks to be interviewed ...... 12! 3.2! Survey Design ...... 12! 3.3! Survey Execution ...... 12! 3.4! Metadata mapping ...... 13!

4! Results ...... 14! 4.1! Systems used to manage various aspects of the biobank ...... 14! 4.2! Primary IT/data management software systems in use ...... 16! 4.3! Tailoring and installation of the system ...... 18! 4.4! Querying ...... 18! 4.4.1! Querying by biobank staff and registered collaborators ...... 18! 4.4.2! Querying via a public interface ...... 19! 4.5! Read access and patient de-identification ...... 20! 4.6! Adding or modifying database content ...... 20! 4.7! System administration, back-ups and security ...... 20! 4.8! IT systems and data management effort ...... 21! 4.8.1! System set-up effort ...... 21! 4.8.2! Data management effort ...... 21! 4.8.3! IT system administration effort ...... 21! 4.9! Information schemas, data standards ...... 22! 4.9.1! Information schemas ...... 22! 4.9.2! Data standards ...... 25! 4.10! Additional Files ...... 25! 4.11! Data size ...... 26! 4.11.1! Core biobank database ...... 26! 4.11.2! Additional Files ...... 26! 4.12! Links with other systems ...... 26! 4.12.1! Links with internal systems ...... 26! 4.12.2! Links with external systems ...... 26! 4.13! Improvement suggestions ...... 27!

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4.13.1! Improvements to individual biobank systems ...... 27! 4.13.2! Improvements across the BSN ...... 28! 4.13.3! Systems elsewhere ...... 30!

5! Discussion & Recommendations ...... 33!

6! Conclusions ...... 41!

7! Acknowledgements ...... 42!

Appendix 1 ...... 43! Survey Process Standard Operating Procedure ...... 43!

Appendix 2 ...... 85! Survey Results ...... 85! Schema cross walk / metadata mapping ...... 140!

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Executive Summary In July 2013, the South West Sydney Translational Cancer Research Unit1 commissioned Intersect Australia Ltd. to conduct a review of the data management capability and IT systems in use across cancer related biobanks in NSW on behalf of the Cancer Institute NSW Biobanking Stakeholder Network (BSN). The goals of this review were to: • Gain an in-depth understanding of the IT systems and associated operational procedures in use across the BSN, • Identify the current levels of harmonisation in the data collection/management systems and practices across the BSN, • Identify minimal IT standards and identify gaps in the application of these and approaches that could address this, • Compare and contrast the data fields held by BSN biobanks with the Cancer Australia biospecimen minimal data set, and identify IT approaches that could address any imbalance, • Determine the interoperability of the current systems in use and if there is scope for any integration of data sets for the common good and approaches that could address this, and to • Map the financial and workforce support of BSN biobank data management systems and to determine both the costs and benefits of implementing more centralised IT/data management approaches and the steps required to implement these. To undertake the review, Intersect worked with staff conducting 2 other concurrent BSN survey-based projects2, to identify 23 cancer biobanks in NSW and then interview associated staff. In depth interviewing of staff from all 23 biobanks was undertaken to gather the data for this study. The principal findings of the review are: • 20 of the 23 BSN biobanks have an established, centralised data management approach/system. • The information architectures across the BSN are highly variable. o ~1/3 of BSN biobanks use a single IT system to manage all of their data and operational aspects, whereas a further ~2/3 banks use between 2-4 systems. o 10 distinct underlying primary data management IT systems are in use: Cansto (developed by the Garvan Institute) is the most widely used (used by 7 banks). Caisis, Biogenix and FilemakerPro based systems are used by 2 BSN biobanks each. The 7 remaining biobanks have utilised or built 7 distinct and different systems. o Each system has been developed to work in concert with existing research and/or clinical systems at the host institution/hospital. o A preliminary metadata mapping exercise across ~1/2 of the BSN biobanks shows that the vast majority of data fields stored are not common and do not adhere to a standard.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 Re-named as the Centre for Oncology Education and Research Translation (CONCERT) in 2014. 2 ‘Definition of minimum standards for CINSW BSN biobanks’ (Champion: A/Prof Jennifer Byrne, The Kids Cancer Alliance), and ‘Digital imaging capability and use across cancer-related biobanks in NSW’ (Champion: Prof Rodney Scott, Hunter Translational Cancer Research Unit).

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• 1 BSN biobank (ABCTB) provides a publicly accessible search interface, 19/20 provide search mechanisms for biobank staff and collaborators. • 4 BSN biobanks have submitted data for inclusion in the Australasian Biospecimen Network “Tissue Specimen Locator” (which provides a mechanism for sample searching across multiple Australian biobanks). • Significant set-up and installation effort of each system already in use has been undertaken (on average between 30-80 days work for IT staff sourced primarily from within the biobank or institution; or on a contractual basis). System administration tasks are primarily provided in-kind by IT staff in the host institution. • Funding is generally lacking for ongoing development or system improvements. • Most stakeholders consulted would welcome more efficient and/or easier-to-use IT systems, however are reluctant to migrate from one platform to another, as the systems in place are largely fit-for-purpose. • The most common suggestions for improvements across the sector are: o Improved access by biobanks operations staff to data housed elsewhere (e.g. clinical follow-up, death records). o Funding to pay for IT support when modification/improvement of databases is required. o A search system across multiple banks (preferably internationally).

The Cancer Institute NSW and other organisations associated with the BSN have invested in a number of cancer-related biobanks across NSW. Currently the facilities operate largely independently of each other and while the governance, operational and IT landscape of the biobanks is multifaceted, there is potential for better coordination and harmonisation of a core set of information between biobanks. This approach would allow both a unified biospecimen search mechanism to be implemented and the potential to enable new uses for biobank specimen data via data linkage to various health related datasets. Initial steps to achieve these goals are set out in the following recommendations.

Recommendations:

A. IT systems/platforms in use across the BSN 1. Retain data management / IT platforms already in operation at individual BSN biobanks as opposed to roll-out of a common biobanking data management platform across all BSN biobanks. 2. IT advice and practical support is available to newly established BSN biobanks to establish a fit-for-purpose data management system that will operate effectively with existing data systems at their site, and with other banks in the BSN. 3. In light of limited technical and/or financial support available to newly formed BSN biobanks, a biobanking data management IT option for newly established biobanks to investigate is Cansto. Although Cansto is not an open-source, community developed system, 7 banks across the BSN utilise the system and service agreements (at cost-recovery rates) are available with

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the Garvan Institute where a new Cansto instance can be tailored on average over a 6-week period, and then hosted for an on-going fee. B. Harmonisation of BSN biobanking information through development of a Tissue Specimen Locator 4. Support is provided for the development of a BSN Tissue Specimen Locator (TSL), or to further develop the existing Australasian Biospecimen Network (ABN) TSL system, such that it can be expanded to efficiently incorporate data from all BSN biobanks. 5. A minimal dataset is developed that contains the information required from a BSN biobank for inclusion in the TSL. Any minimal dataset developed should align with other minimal dataset specifications being developed for other purposes (such as the Cancer Australia Biospecimen data set specification for tissue banking). 6. A metadata mapping/crosswalk of the data-fields from each BSN biobank to the minimal TSL dataset is undertaken. 7. In cases where data fields within the minimal TSL dataset are not currently captured by an individual biobank, technical support is available to enable these banks to capture this information, so that all BSN biobanks adhere to the minimal information specification. 8. Data access/sharing policies and technical mechanisms are developed across the BSN that would allow automated (either ‘push’ or ‘pull’) secure copying of the minimal TSL dataset from individual biobanks to a central BSN database. 9. Support is provided for development and central hosting of a database that contains the minimal core data from each BSN biobank and delivers this information to a publicly accessible TSL search interface. 10. Staffing is supported to develop and maintain the TSL system.

C. Enhancement of the ability to link BSN biobanking records to other health related datasets via Data Linkage 11. A minimal dataset is developed that contains participant variable information from suitable BSN biobanks that can be utilised for data linkage via CHeReL to other publically available health related databases datasets. 12. A metadata mapping/crosswalk of the data-fields from each suitable BSN biobank to the minimal participant variable dataset is undertaken. 13. In cases where data fields within the minimal participant variable dataset is not currently captured by a suitable biobank, technical support is available to enable these banks to capture this information and adhere to the minimal information specification. 14. Data access/sharing policies and technical mechanisms are developed across the BSN that would allow automated (either ‘push’ or ‘pull’) secure copying of the minimal participant variable dataset from individual biobanks to a central BSN database. 15. Support is provided for development and secure, central hosting of a database that contains the minimal core participant variable data from each BSN biobank and securely delivers this information to CHeReL. 16. Consideration is given to funding a full-time Data Manager for the BSN, to manage and co- ordinate the data-related activities outlined above.

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1 Background Data associated with specimens collected and stored in biobanks (e.g. clinical, demographic, specimen quality, collection procedures etc.) is of critical importance to providing biological, medical and operational context. This perspective is necessary in order for any specimen to have value and usefulness in research. As outlined by Vaught et al3, in their review of international efforts to develop biospecimen best practice, best data practices ensure that “data are collected according to recognised standards, in particular, in identifying the pathologic status of the biospecimen, determining the common data elements that need to be collected, and assuring that the data are collected according to the privacy rules and other human subjects data protection rules and regulations”. Tightly linked to any data collection and management practice, is the IT system chosen to manage the information and associated data management processes. The National Cancer Institute (NCI) 2011 Best Practices Guidelines4, states that “An informatics system should support all aspects of biospecimen resource operations, including, but not limited to, tracking of research participant enrollment and consent; biospecimen collection, processing, storage, and dissemination; QA/QC processes and documentation; collection of or electronic linkage to research participant (i.e., clinical) data; data security; and management reporting functions (e.g., generating reports on inventory, collection, utilization, QA, etc.). In addition, the system should store a minimum, common set of clinical data. Biospecimen resource informatics systems are a key tool in providing accountability of biospecimens (e.g., location) and related data uses to research participants”. Furthermore, the report states, that “the informatics systems should ensure interoperability of systems (i.e., other biospecimen resources or different data systems) because this is key to exchanging data and biospecimens. This should include integrating with other systems where genomic, proteomic, radiology imaging, pathology imaging, and other relevant data are captured or shared”. In order to understand the operational and governance workings of cancer-related biobanks in NSW, the Cancer Institute NSW conducted a two-phase survey in 2009. 17 banks were identified and subsequently interviewed and/or surveyed. The resulting 2009 Report5 summarises the findings, yet information included in the published document outlining the data management systems and practices in use at each biobank is brief. Included in the Report is a general statement that “Each bank had developed a database, often in Microsoft Excel or Microsoft Access”, and Appendix D of the Report also presents individual synopses of the design, governance and working structures of the biobanks surveyed, which include a brief summary of the data management processes and systems in place at each bank. These are listed below in Table 1.

Biobank Name (c. 2009) Statements from 2009 Report regarding IT / data management

Sydney Melanoma Unit (SMU) “Biobank data stored on EXCEL spreadsheet using data entry guidelines, with paper Biobank note of specimen location. Treatment, follow-up and pathology details are entered into a research database. Developing link to SMU clinical database”

Australian Prostate Cancer “Minimum pathology and clinical data sets routinely stored on web-based central Collaboration (APCC) BioResource database. Additional clinical data collected manually”

NSW Pancreatic Cancer Network “General clinic-pathological information collected including management, medical Tissue Bank history, outcomes. Database linking pathology and clinical data designed in-house. Data linkage with CCR (Clinical Cancer Registries) for notification and cause of death” !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 3 Vaught, J.B, et al. (2010). International Efforts to Develop Biospecimen Best Practices. Cancer Epidemiol Biomarkers Prev 19(4): 913-915. 4 http://biospecimens.cancer.gov/bestpractices/2011‐NCIBestPractices.pdf 5 Welberry et al, “A comprehensive review of cancer-related biobanks in New South Wales”. Sydney; Cancer Institute NSW October 2009. http://www.cancerinstitute.org.au/media/25802/2009-10_review_cancer_related_biobanks_in_nsw.pdf

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South Western Sydney Colorectal “Patient clinical details stored in Excel database in password protected server. Tumour Bank, Garvan Standard pathology data is collected, and survival data collected by CCR”

Kolling Institute Tissue Banks: “Pathology data stored in ACCESS database. No formal links to clinical database yet established”. • Breast Cancer Tissue Bank • Neuro-endocrine Tissue Bank • Upper GI Tissue Bank • Gynaecology Tissue Bank Australasian Brain Tumour Bank “Pathology data stored in ACCESS database on Kolling server. Tumour bank ethics (Kolling Institute) approval requires that clinical database be kept separate from pathology database. Clinical database in development. The need for a more sophisticated system linking pathology and clinical data has been recognised (i.e. WAIMR system)”.

Breast Cancer Tissue Bank (BCTB), “Clinical history, pathology and treatment information is collected and entered into based at WMI BCTB database (situated on server at management hub, with site-specific access)”

Westmead Gynaecological Tissue “Specimens collected post NHMRC grant funding from ABN are stored onsite in ABN Bank specimen locator database. Aiming to have all specimens put on ABN specimen locator database in future (including those collected prior to grant). A more sophisticated system for linking pathology and clinical follow-up databases would be welcomed”.

Children’s Hospital Westmead “Clinical, pathology and long term follow-up data collected by Oncology Dept., and Paediatric Tumour Bank available to tumour bank on request. Biobank database contains basic demographic information but is not linked to clinical database stored in Oncology Department. A more sophisticated system to link databases would be welcomed for research, although may need to be tailored to paediatrics / multiple tumour types. One computer houses the tumour bank database.”

Children’s Cancer Institute “Routine clinical information stored includes first and surname, sex, DOB, diagnosis Australia (CCIA) Tumour Bank date, hospital, referring doctor, diagnosis and primary site. Limited clinical follow-up data collected and entered manually (i.e. date and site of relapse, date of death). Database secure and regularly checked vs. samples in freezer.”

Surgical Oncology Group Sarcoma “Routine clinical information includes demographic, tumour and treatment Tumour Bank, Prince of Wales characteristics, outcomes. Samples and data linked via MRN and surname. Database Hospital updated after patient visits with annual review of outcomes”

Colorectal Cancer Tissue Bank, “Patients consented by clinical nurse who also collects specimens and enters database Integrated Cancer Research UNSW details. SOPs internally developed over last 15 years of bank operation. Internally developed database to link specimens to data. In the process of linking with BioGrid data linkage system.”

Brain Tumour Bank, Prince of “Clinical demographic data only obtained” Wales Private Hospital

The Cancer Council NSW Tissue “Clinical data collected via patient questionnaire. Links to other sources planned for Bank later in the study. Additional data linkage via CHeReL” Table 1. Summary of NSW cancer-related biobanking IT systems and data management practices c. 2009. The above table includes information extracted from the 2009 Welberry Review of Cancer-related biobanks in NSW (Table 9.3 “Design and Governance findings for each biobank Interviewed and/or Surveyed”) and includes summaries of the IT systems and/or data management practices utilised by various cancer related biobanks in NSW in 2009.

The 2009 Report also includes several “Future options for New South Wales biobanking”, that include possibilities for data management and data linkage (Section 8.2.8). These state: “The growth of biobanks over the past five to 10 years has already yielded a need for more sophisticated bioinformatics processes and systems to be used. If future growth through development of biobank consortia is to be realised, then the need for improved data management and data linkage systems

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! ! will increase. In redeveloping databases and data management, biobanks and biobank consortia could, for example, through the use of tools such as the LIMS (Laboratory Information Management System) at WAGER (Western Australian Genetic Epidemiology Resource) and data linkage systems (such as WADLS (Western Australian Data Linkage Service), CHeReL (NSW Centre for Health Record Linkage) or BioGrid Australia), systematically track follow-up and outcomes data for donors and enable new uses for biobank specimen data in terms of population studies. It is also worth noting that a variety of free software tools are available to biobanks (such as CaBig, Caisis, CTRNet (Canadian Tumour Repository Network)’s ATiM (Advanced Tissue Management)) to facilitate operations. There are also important data standardisation initiatives in the United States, Canada and Europe as well as Australia. Biobanks in NSW could benefit tremendously from leveraging off existing resources in these areas”. The 2009 Report also presents 2 potential models for operation of biobanking in NSW and identifies the IT Infrastructure and Resources that would be required for each: (a) a Stakeholder Network – “It is expected that the stakeholder networks would develop from good will of existing managers/staff and other interested parties and as such would not require any additional staffing to be formed. Development of a specimen locator network would require IT infrastructure to establish and maintain a website and specimen locator software. It may also be necessary to employ staff (e.g. 0.5–1.0 FTE) to manage the processes and ensure the network is up-to-date with all member biobank information. Expansion of existing specimen locator networks where possible could reduce costs and resources required”; and (b) a Consortia approach – “Biobanks require IT infrastructure to: track and monitor sample locations; manage clinical information about donors including de- and re-identification of samples as required; and monitor follow-up and outcomes data of donors. Of the banks surveyed, 60 per cent did attribute costs to IT infrastructure, 27 per cent attributed one per cent of funding to IT and another 13 per cent attributed 5–8 per cent of funds to IT. Considering the essential role of IT in data management, the survey suggested relatively little to no money was actively invested in this area of biobanking. Twenty per cent of biobanks surveyed did not yet have any computing hardware attributable to the biobank, and the remaining banks had internally developed databases located on institutional servers … As biobanks grow, it will be important that they invest into their IT and data management systems. Tools such as the laboratory information management system from WAGER could be used to help biobanks with data management issues and lessons from other well established biobanks (e.g. Victorian Cancer Biobank) could be taken in regards to appropriate software and database systems for biobanking needs”. Following on from the 2009 Report, in April 2013, an informal telephone interview-based survey was conducted by Intersect Australia Ltd on behalf of the Cancer Institute NSW Biobanking Stakeholders Network (BSN), of six biobanks6 representing a number of the Translational Cancer Research Unit/Centre (TCRU/C)s across the BSN. This brief survey suggested that at the time, the data management landscape across the BSN was fragmented, with many different software systems in use, ranging from bespoke platforms built using tools such as FileMakerPro7, to open-source solutions such as CaTissue8 and Caisis9, and commercial packages such as Biogenix10. It was found that the different implementations of these software systems were all able to record certain information features related to both the participant and specimen, but these are not necessarily all the same aspects across all the biobanks. Additionally, these information aspects were not necessarily described using standard descriptors, which has an impact on the ability to link data across (and beyond) the network.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 6 Kids Cancer Alliance Tumour Bank; Australian Breast Cancer Tissue Bank; Lowy Biorepository; Gynaecological Oncology Biobank at Westmead; Children’s Cancer Institute Australia Tissue Bank; South West Sydney Comprehensive Cancer Tissue Bank. 7 http://www.filemaker.com 8 http://cabig.cancer.gov/solutions/applications/catissue/ 9 http://www.caisis.org 10 http://www.genixventures.com/project/8-biogenix/

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In 2013, the Cancer Institute NSW, via the BSN, funded three sister projects (focusing on biobanking governance/practice11, digital imaging systems12 and IT/data management systems in use) to comprehensively review cancer-related biobanks in NSW, gauge how the cancer biobanking industry has changed/matured since the 2009 review, and to suggest options for harmonising the network. This report outlines the survey into data management practices and systems across the BSN and its findings.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 11 ‘Definition of minimum standards for CINSW BSN biobanks’ (Champion: A/Prof Jennifer Byrne, The Kids Cancer Alliance) 12 ‘Digital imaging capability and use across cancer-related biobanks in NSW’ (Champion: Prof Rodney Scott, Hunter Translational Cancer Research Unit).!

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2 Aims The overall aim of this project is to perform a thorough information gathering exercise across the BSN members and relevant stakeholders to understand the existing and required data management procedures and software for each member biobank, and to suggest approaches to facilitate harmonisation of the biobanking information systems used across the BSN. Specific aims are to: • Determine what biobanking data management systems are in use across the BSN, and how these are used for data collection, storage and management. • Identify the current levels of harmonisation in biobanking data management systems and data collection/management practices across the BSN. • Identify minimal IT standards and identify gaps in the application of these and approaches that could address this. • Compare and contrast the data fields held by BSN biobanks with the Cancer Australia biospecimen minimal data set, and identify IT approaches that could address any imbalance. • Determine the interoperability of the current systems in use and if there is scope for any integration of data sets for the common good (e.g. a search engine with access to all biobank holdings, cloud storage sharing capacity). • Map the financial and workforce support of BSN biobank data management systems and determine the costs and benefits of implementing more centralised IT/data management approaches and the steps required to implement these.

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3 Methods 3.1 Identification of biobanks to be interviewed In conjunction with the teams undertaking the 2 other concurrent BSN survey-based projects, 23 cancer biobanks were identified in NSW, by accessing publically available data and contacting biobanks and precinct research managers. 3.2 Survey Design A survey (see Appendix 1 [Attachment A, “IT Survey”]) was designed that focused on determining the following for each biobank: • the IT platform(s) used • operational procedures managed by the IT platform(s) • data fields included • data standards used • ability to link data to other data collection/management systems (e.g. clinical) within the biobank • ability to link data to other biobanks and beyond • data security and access • desired functionalities not offered by the current IT platform(s) • staffing (operational and IT support) levels The survey tool was produced in Microsoft Excel, to ensure usability in the absence of an Internet network connection at the various survey sites (see below). 3.3 Survey Execution The survey tool was circulated to CINSW Translational Cancer Research Unit/Centre representatives within the BSN, who were invited to provide feedback, before piloting commenced with three volunteer biobanks. The survey was then rolled out to the remaining 20 biobanks over a two-month period. A comprehensive Standard Operating Procedure (Appendix 1) was developed to ensure both inter- bank consistency, and to serve as a legacy document for any future projects. All surveys were conducted through face-to-face interviews, with the questions in the survey tool providing the script for a semi-structured interview. Operators of the 23 biobanking data management systems in use across the BSN were interviewed for responses to the following several sections of the survey: • Systems used to manage various aspects of the biobank • System installation • Querying • Read/write access

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• System Administration • IT (and IT related) staff • Machine readable data • Additional files • Links with other systems • System security • Improvement suggestions Interviews were recorded to obtain complete and accurate responses, which were then transcribed into a single master spreadsheet. 3.4 Metadata mapping Following the interviews, the information architecture underpinning the primary data management system for a number biobanks was received (n=10). Formats varied from schema diagrams (.vsd format), paper copies of schema diagrams, Data Dictionary documents, or copies of User Manuals for the biobank (in .pdf format). A metadata mapping (or “cross-walk”), was performed by the authors of this report (with no further input from the interviewees) to identify all data fields with matching meanings from the various input schemas, as well as unmatched data fields. A matrix of the metadata cross-walk was compiled manually in a Microsoft Excel spreadsheet and a conservative approach was taken where a match was not considered if the meaning of a data field was not clear (as based on the information provided (i.e. data element tags and any further context provided)).

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4 Results Of the 23 biobanks identified, all were interviewed in conjunction with the teams undertaking the 2 other concurrent BSN survey-based projects. See Appendix 2 for detailed responses. 4.1 Systems used to manage various aspects of the biobank Interviewees were asked to name the systems used to manage the following various aspects of their biobank: • Specimen/sample information (i.e. Tissue Information; Sample Type; Collection, Processing and Storage Method(s); Storage Location(s)); • Digital Image Data (i.e. Image Capture and Management); • Participant related information (i.e. Pathology Reports; Consent; Medical and Lifestyle History; Diagnoses; Treatment History; Demographic and Survival Information); and • Operational Aspects (i.e. Generating Sample and Participant Identifiers; Sample Tracking; Aliquot Management; Receptacle Labelling; Report Generation; Communication with other Biobanking Resources and Protocol Management). The results are presented in Figure 1 (overleaf). The responses show that apart from Protocols (which are generally managed with versioned Microsoft Word documents), and Digital Image Management (that tend to be managed via vendor supplied specialist software), about one third of biobanks (7/23) use a single system to manage all of their data and operational aspects. The remaining sixteen biobanks use a combinational approach, with multiple data management systems in place. Eleven banks use 2 systems; two banks use 3 systems; and two banks use 4 systems. One biobank has no systems in place as it is in a start-up phase. Despite the majority of biobanks having more than one system in place (16/23), most of these banks still tend to rely on one of their constituent IT systems to manage the majority of the data and management aspects associated with their bank. For example, of the eleven banks utilising 2 systems, nine of these use one system to manage more than 80% of the data and operational aspects listed above. !

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! ! 4.2 Primary IT/data management software systems in use The identity of the primary IT/data management software system in use by the 23 BSN-associated biobanks is shown in Figures 2 and 3.

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Figure 2. Primary data management systems in use across the BSN.

Cansto is used as the primary data management system by 7 biobanks. Caisis, Biogenix and databases developed using FilemakerPro are all used by 2 biobanks each. CaTissue/OpenSpecimen, Excel and Access based-solutions are used by one bank each. Three banks have developed their own custom solution. Of those shown as N/A (*), 1 bank is not yet formally operating, 1 bank has no formal centralised data management system (it is the responsibility of various researchers to manage the collective data informally) and 1 bank is utilising temporary systems prior to rolling out a more permanent solution. ** - One bank has no one primary data management system, and uses a combination of InForm GTM (Oracle) + Open Clinica + Access + Excel.

Cansto13 is the most widely used system across the BSN, and is utilised by 7/23 biobanks including all 5 Garvan Institute-based BSN biobanks, the sister Garvan Institute/Royal Prince Alfred Hospital Breast Cancer biobank(s), and the Asbestos Diseases Research Institute (ADRI) Mesothelioma biobank at Concord Hospital. Caisis14 is used by 2/23 biobanks, both of which are based at Westmead Hospital (The Australian Breast Cancer Tissue Bank (ABCTB) and the Westmead Urological Biobank). 2/23 biobanks use databases that have been built using FileMakerPro15 - both are based at Westmead Hospital (the Westmead Gynaecological Oncology Tumour Bank and the Storr Liver Unit biobank). Biogenix16 is used by the two BSN biobanks focusing on paediatric cancer (the Tumour Bank at The

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 13 http://linkage.garvan.unsw.edu.au/bioinformatics/cansto.html 14 http://www.caisis.org 15 http://www.filemaker.com/au/products/filemaker-pro/ 16 http://www.genixventures.com/project/8-biogenix/

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Figure 3.

Primary data management systems in use across the BSN, by geographical location.

The identity of the primary data management system in use at each BSN associated biobank is indicated by the colour code (see ‘KEY’). Abbreviations used to denote individual biobanks:

ABCTB – Australian Breast Cancer Tissue Bank ADRI -Asbestos Diseases Research Institute Biobank APGI – Australian Pancreatic Genome Resource Bioresource CCIA – Children’s Cancer Institute Australia Tissue Bank CCNSW – Cancer Council NSW CHWTB – Children’s Hospital at Westmead Tumour Bank HCB – Hunter Cancer Biobank HCC – Hepatocellular Cancer Biobank HeadNeck – Sydney Head and Neck Biobank KingBr – Breast Biobank: Kinghorn Cancer Centre Kolling – Kolling Combined Biobanks LCB – Lung Cancer Biobank Lowy – Lowy Biorepository (incl. HSA Biobank) MIA – Melanoma Institute of Australia Biobank NCRB – Nepean Cancer Research Biobank NHMRC CTC – NHMRC Clinical Trials Centre – Biospecimen Collections of Cancer Trial Co-operative Groups Clinical Trials OCB – Ovarian Cancer Biobank RPAB – RPA Breast Biobank SLU – Storr Liver Unit SVPC – St Vincent’s Campus Prostate Cancer Biobank SWSCCB – South West Sydney Comprehensive Cancer Tumour Bank WGB –Gynaecological Oncology Biobank at Westmead WUB – Westmead Urological Cancer Biobank

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Children’s Hospital at Westmead and the Children’s Cancer Institute of Australia (CCIA) biobank). CaTissue/OpenSpecimen17 is used by a single biobank (the Lowy Biorepository). Microsoft Access18- and Microsoft Excel19-based solutions are used by 1 bank each (The Kolling Institute Combined Tumour Bank and the Sydney Head and Neck Tumour Bank respectively). Three biobanks have developed bespoke databases (Hunter Cancer Biobank (HCB), Melanoma Institute of Australia (MIA) and Cancer Council NSW (CCNSW)). Three banks have no formal system currently in place as they are either in the process of setting up operations (Nepean Cancer Research Biobank, South West Sydney Comprehensive Cancer Tumour Bank), or operate without formal centralised data management (Hepatocellular Cancer Biobank). Of the systems in place, the majority are commercial products (i.e. FileMakerPro, Biogenix, Microsoft Access, Microsoft Excel and Cansto20) and collectively these are used by 14/20 BSN biobanks. 2 of the systems are free open source products that have been built by a community of developers (i.e. Caisis and CaTissue/OpenSpecimen21), and collectively these are used by 3/20 banks. The 3 bespoke systems (used by HCB, CCNSW and MIA) have various front-end applications that access back-end SQL/MySQL relational databases. 4.3 Tailoring and installation of the system In all 20 banks with a formal data management system in place, the system has been tailored for the biobank. The process has included effort by biobank operational staff in all cases, mostly in defining specifications but also in setting up the system themselves in periods when no or limited IT support has been available (n=3). In most cases this tailoring work has also been performed in conjunction with either dedicated biobank IT staff (n=2), an employee of an IT company providing the software (n=2), casual IT contractors (n=5) or Institutional IT staff (n=13). Installation of the system has been performed by institute or university IT staff in 16 cases (on institute, university or hospital servers), by biobank staff in 2 cases (on desktop machines), and by an employee of the IT company providing the software in 1 case. When asked would it technically be possible to add additional functions to their system, 19/20 respondents answered ‘yes’ and that this work would be carried out by either Institute/University IT staff (n=12), an IT contractor (n=5), biobank operations staff (n=4) an employee of the IT company providing the software (n=2), or a combination of these roles. 4.4 Querying 4.4.1 Querying by biobank staff and registered collaborators Of the 20 biobanks that have an established IT/data management system in place, 19 have at least one method that allows biobank operations staff and registered collaborators to query the contents of the system. Of these 19 banks, only 3 allow all biobank staff to perform queries. More commonly, the capacity to perform queries is arranged for a variety of specific roles including: Biobank Manager (n=12); Data Manager (n=12); Research Assistant (n=9); Research Officer (n=5); Biobank Director (n= 5); Clinical Research Associate (n=4), Registered User (n=1); Tissue Management Coordinator (n=1); Pathologist (n=1); Data Analyst (n=1); Other roles (n=4).

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 17 http://cabig.cancer.gov/solutions/applications/catissue/ 18 http://office.microsoft.com/en-au/access/ 19 http://office.microsoft.com/en-au/excel/ 20 Cansto was developed by Garvan Institute IT. The code in not open source. The Cansto system (including the associated GQA (General Query Application) graphical user interface) and hosting by the Garvan Institute is provided at cost recovery rates. (Gerard Hammond, Garvan Institute IT, pers. comm.) 21 Although CaTissue/OpenSpecimen is free and open source, tiered support contracts are offered by Krishnagi Solutions for a fee http://catissueplus.org/pricing

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14 of the 19 biobanks provide this query functionality through a standalone client program that is installed on local machines (9 have this software installed either on Macs or PCs; 4 have software exclusively installed on PCs; and 1 has the software installed exclusively on Macs). The remaining 5/19 biobanks access their query functionality via a web application (i.e. a tool that is available through a web browser and is accessible via Macs or PCs (including tablets) with an internet connection). 6 of the 19 biobanks also perform direct SQL querying on their database by staff, who are deemed to be sufficiently proficient. The single biobank that does not have a method that allows biobank operations staff to query the contents of the system, requires the data manager from another BSN biobank to perform SQL queries of their (Caisis-based) system on their behalf. This service is provided for a fee. 16 of the 20 biobanks felt that current querying methods for staff were adequate. Of the 4 banks who did not, the main drawbacks emphasized were: biobank staff lacked the level of SQL expertise to be able to query their data efficiently (n=2); the available Graphical User Interface (GUI) was inadequate (n=1); and inconsistencies are noted in results when an identical query is constructed in two different ways using the GUI (n=1). 4.4.2 Querying via a public interface Only one BSN-associated biobank provides a publicly accessible interface for querying the biobank (ABCTB. See http://www.abctb.org.au/abctbNew2/ResearchSpecimenQuery.aspx). The interface is a web application that is accessible through any web browser, and provides a mechanism for the public to construct a simple query of a limited set of information to gauge how many samples of interest are available in the bank. Several filtering options are available that can be combined: • Cancer Type [invasive] OR [in situ] OR [no cancer] • Histopathological Grade • Marker: ER [negative] OR [positive] OR [equivocal] OR [not performed] • Marker: PR [negative] OR [positive] OR [equivocal] OR [not performed] • Marker: Her-2 [negative] OR [positive] OR [equivocal] OR [not performed] • Tissue Type: Blood [yes] OR [no] • Tissue Type: Fresh_tissue [yes] OR [no] • Tissue Type: FFPE [yes] OR [no] • Histology Image [yes] OR [no] Results are returned in a table listing a sample Identifier, Age at Diagnosis, Specimen Type, and a link to launch a histology image (if an image is available). If samples are found through the simple search, a researcher can then use an on-line form to prepare and submit an expression of interest to use samples in the bank22. This is submitted to staff at ABCTB as part of a standard application procedure23. Development of this interface has meant considerable time and effort savings for biobank staff, who otherwise need to liaise with researchers with casual enquiries of this type, manually construct similar simple searches and respond on a case-by-case basis.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 22 http://www.abctb.org.au/abctbNew2/EOI.aspx 23 http://www.abctb.org.au/abctbNew2/ApplicationProcedures.aspx

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This public facing interface was considered adequate by ABCTB staff, however it was felt that more comprehensive search functionalities would also be useful, such as that offered by the ‘Sample Finder’ on the UK Breast Cancer Campaign Tissue Bank website24, which allows additional filtering of information by various other characteristics (patient, invasive, non-invasive, and sample), and provides real-time feedback to a user with regards to numbers of available samples. Additionally, published research outputs that have arisen from use of samples originating in the UK Breast Cancer Campaign Tissue Bank are indicated and these are available via links to journal articles in PubMed and Gene Expression Omnibus (GEO) expression data at the National Center for Biotechnology Information (NCBI) where appropriate. 4.5 Read access and patient de-identification The types of users who have read access to the contents of the biobanking databasing systems vary across the BSN. In 17/20 banks, all staff have read access to view content, whereas in 3/20 banks read access is assigned to certain biobank staff only. Additionally, 7/20 biobanks also provide some direct read access to registered collaborators (including referring clinicians). In most biobanks (16/20), a proportion of data is de-identified and not presented to various sets of users (e.g. certain biobank staff, collaborators, staff at certain tissue collection sites etc.). In all cases, data fields that are de-identified include participant details. These may include: name (n=16); address (n=14); date of birth (n=14); Medical Record Number (n=11); Medicare number (n=7) or other identifying details (n=3). Patient de-identification is achieved through a number of methods: selective presentation of information in the system based on user roles that are recognised at system log-in (n=10); manual de-identification of search results (performed by a member of staff with read access) prior to passing the de-identified information onto others (n=5) or in an ad hoc manner (n=1). 4.6 Adding or modifying database content User types that can modify database contents also vary across the BSN. In most cases (16/20) only certain biobank staff can make changes. Other banks allow any staff (n=8); registered collaborators (n=4) or registered pathologists (n=1) to make content changes. In most cases, changes are made manually via the user interface (n=18), but a small number of banks (n=2) also have the ability to additionally make changes in batches via mass imports or via the use of a script. In the 13 instances where the biobank holds information derived from documents such as clinical or pathology reports, this information is entered manually by staff into the database system from scans of paper-based documents in all but 1 case. The single bank with an automated method for this process uses HL7 messaging25 from associated hospital systems to fulfill this objective. In 16 instances, all changes made to data in the system are logged and a complete record is kept, whereas in 1 bank only the last change is logged. In 3 banks, there is no mechanism to record changes. In all 4 cases where an incomplete record of changes is kept, this function was seen as desirable. 4.7 System administration, back-ups and security In most cases (18/20), administration of the BSN biobanking systems is the responsibility of IT staff at the auspicing institution (i.e. hospital, institute or university). In 2 cases, this responsibility lies with biobank staff themselves. Tasks that are performed include administering access (20/20); performing data back-ups (20/20); installing software updates (14/20) and performing data linkage (3/20). !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 24 https://breastcancertissuebank.org/bcc/tissueBank?Name=sampleFinder 25 http://www.hl7.org.au

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Data back-ups are performed in all 20 cases, both of the biobanking database itself, and for 14 banks, any additional files that may be associated with the bank (see Section 5.10 below). In all but two cases (n=18), this is performed in accordance with the policy and procedures of the auspicing institution, and data backups are produced using an automated method and stored on institutional infrastructure (that may be either on-site or off-site). In two cases biobank staff themselves are responsible for managing data back-ups. In these cases the back-ups are stored on hard drives either housed in the same office or in a different part of the hospital as the primary copy, and the back-ups are produced either automatically (using Apple Time Machine, n=1), or manually (n=1). Security measures applied to BSN biobank information systems encompass placement of the system on a secure server and employing access restrictions (n=14), or by utilising access restrictions alone (n=6). 4.8 IT systems and data management effort 4.8.1 System set-up effort When asked to approximate the total amount of effort expended in setting up the current system, 14/20 respondents provided a numerical figure. Of these, 3 banks have spent between ~80-100 days of 1 FTE effort setting up their system; 7 banks ~30 days of effort; and 3 banks less than 30 days. Of the 7 banks requiring ~30 days of effort in system set-up, all utilise Cansto which takes approximately 6 weeks of effort to tailor and instantiate each instance by Garvan institute IT staff. Note however that overall, it is estimated that ~ 2 years of 1.0FTE (i.e. 500 days) development effort has been spent on Cansto26. Several banks were unable to provide a numerical estimate of effort. 2 banks stated that system set- up and tailoring had been on an ad hoc and on-going basis (one of which has been in existence since prior to 2009 and therefore a considerable amount of cumulative effort has been expended in developing this system); and 4 banks could not provide this figure. Staff performing system set-up were either: (a) based in the Garvan Institute (n=7, i.e. for all Garvan-based Cansto systems as well as an additional instance based elsewhere (who consider the Garvan Institute in this capacity to be a contractor)); (b) contractors (n=2); (c) staff based within the auspicing institution (excluding the Garvan Institute cases discussed above) (n=9); or (d) unknown (as system set-up occurred prior to current staff being associated with the bank, n=2). 4.8.2 Data management effort When asked to approximate the amount of effort expended in data entry and management tasks, 17/20 respondents provided a numerical figure (expressed as the percentage of total biobanking- related effort). 1 bank estimated that ~80% of biobank staff effort was focussed in this area whereas 7 banks estimated these tasks required between 30-60% of their collective effort. The remaining 9 banks spent less than 30% of time on data entry/management tasks. 3 respondents could not provide an estimate. In all cases this effort was undertaken by staff within the biobank, with 3 banks also stating that additional staff at either a closely related biobank, or at other tissue collection sites affiliated with the biobank were involved. 4.8.3 IT system administration effort For approximately 50% of biobanks (11/20), a minimal or negligible amount of effort by biobank staff was required to perform IT system administration tasks (note however that whilst minimal effort is !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 26 G. Hammond (Garvan Institute IT) – personal communication

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! ! required for systems administration tasks for each of the 7 instances of Cansto, maintenance of all the Cansto instances in use by the Garvan Institute equates to about 0.2FTE per year). 5 banks could not provide an estimate of the amount of effort that was expended on system administration tasks, and a further 4 banks stated that this work was done in an ad hoc manner. One bank estimated that 0.5FTE was spent on system administration tasks. System administration tasks are largely performed by hospital or university IT staff within the auspicing institution or are contracted to a 3rd party, with only 2 biobanks performing these tasks themselves (the two banks who store data on local desktop machines rather than on institutional servers or filesystems). 4.9 Information schemas, data standards 4.9.1 Information schemas An information schema (i.e. a list of data fields) was provided by 10 of the banks interviewed, in various formats such as database schemas, data dictionaries, or user manuals/handbooks. The landscape of the information architecture across these 10 banks was assessed by comparing and contrasting all the data fields of all 10 schemas through a metadata mapping or ‘cross-walk’ whereby semantically equivalent terms were matched between each schema. A high level overview of the output is depicted in Figure 4, and details are presented in Appendix 3. The landscape across the 10 information architectures is highly variable. Only one of the 1000+ data fields is common across all 10 systems (), however a number of other information aspects are recorded across most of the systems examined, albeit in a variable fashion (e.g. the participant name could be recorded in one field , or using various combinations of a number of fields such as , , , and