a. nlm fellow wa - office of biorepositories and ... a. fearn, mba nlm informatics research fellow...
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Paul A. Fearn, MBANLM Informatics Research FellowUniversity of Washington, Seattle, WA
This research is supported in part by the NLM training (NIH NLM #T15 LM07442) and ITHS (NIH NCRR 1 UL1 RR 025014) grants
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How do we capture, store, retrieve and keep (or link) all necessary data / information for biorepository and biospecimen research?
What is the scope of informatics for this area?What are the boundaries between systems?What standards and systems already exist?What are the informatics gaps and opportunities?
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NCI OBBR Biospecimen Research DatabaseBiospecimen scienceSearch terms / indexing
Literature ReviewStandards for biological and biomedical investigationsBiorepository informatics
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https://brd.nci.nih.gov (NCI OBBR web site)Search PubMed for biospecimen scienceExtracted and curated information / SOPsBRD search criteria / termsBiospecimen type: blood, cell, fluid, tissueAnalyte: DNA, RNA, protein…Technology platform: PCR, FISH, CGH..Location: blood, serum, plasma, pancreas, bladderDiagnosis: melanoma, prostatitisPreservation type: formalin, frozenExperimental factor: deparaffinization, ischemia time
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Tracking expression of samples in literatureAlignment with BRD of biospecimen scienceTrack provenance back to to specimen/source
Informatics problem: harmonizing and aligning the terms across framework
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Variety of public databases (e.g. GenBank, GEO, Peptidome, PDB)
How is the sample information reported?Information to determine provenance?Can we determine biospecimen collection, processing, and handling protocols and variations in generation of these data?
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“Evolution” of standards for biological and biomedical investigationChecklists (MIAME to MIBBI)Format: XML and TAB (MAGE‐ML to ISA‐TAB)Terminologies / ontologies (MGED‐O, NCIt, OBI)UML Data / Object Models: (MAGE‐OM to FuGE)
Coverage of BRD technology platforms?Alignment of sample annotation?
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Technology Platform MIBBI Data Format CV/ontology Model
DNA SequencingMIGS, MIAME, MINSEQE
MAGE-ML, MAGE-TAB, ISA-TAB
FISH MISFISHIEANISEED, COMPARE OBI FuGE
PCRMIAME, MIqPCR (MIQE) RDML SO
DNA Microarray MIAMEMAGE-ML, MAGE-TAB MGED Ontology MAGE-OM
Electrophoresis MIAPE-GE GelML, AGML OBI FuGEImmunohistochemistry MISFISHIE ISA-TAB OBI FuGEGC-MS MIAPE-MS mzML
Tissue microarrayTMA DES, TMA-TAB Stanford TMA-OM
GC-MS MIAPE-MS mzMLStandard H-and-E microscopy OMEFlow Cytometry MIFlowCyt FuGE
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International Society of Biological and Environmental Repositories (ISBER)Best Practices for Repositories, 2008Revised version in progressHuman and non‐human data elements
NCI OBBRBest Practices for Biospecimen Repositories, 2007 Revised version in progress
http://www.isber.org/ and http://biospecimens.cancer.gov/
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From separation from source to analyteContainers, exposures, and “treatments”Unique identifiers / barcodesSystem users, locations and timestampsSecurity and audit trailClinical pathology / laboratory medicine findings
Clinical tests and results & images (TRIs)Quality assurance / quality control TRIsResearch TRIs
Retrospective information (queries and reports)Prospective information (SOPs and workflow support)
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Precise sample provenance trackingLinks back to source / donorLinks forward to investigations, data and publications
Detailed collection, handling, processing dataFrom giant binders of SOPs to FuGE protocolsSupporting documents (i.e. consents, protocols)
Other links, integration and interoperabilityUses publically documented interfaces (APIs)Uses standard vocabularies / terminologies
Stewardship informationDonor preferences, consents, authorizationsOrganizational or regulatory constraints
Tracking of variable costs
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Human source / donor / patientIndividual identifiers, history, environment, familyLongitudinal follow‐up after specimen collectionCDISC study data standardsBiomedical / health data standards ( i.e. FMA, UMLS)How many CDEs describe a person? 50? 1500?
Non human sourceIndividual: identifiers, history, environment, familyCollection / processing locationOther relevant standards (i.e. Taxonomy, CARO)
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caTissue UML modelCommon Biospecimen Model (CBM)
Specimen Locators▪ http://biospecimens.cancer.gov/locator (NCI)▪ http://biospecimens.ordr.info.nih.gov/ (NIH)▪ Harvard Catalyst▪ CHTN http://www.chtn.nci.nih.gov/biospecimens.html▪ Arizona Specimen Locator
caLIMS2 (to connect caTissue with caArray, etc)FuGE UML model for investigations
Model for standard operating procedures (SOPs)▪ Processing steps▪ Operators▪ TimestampsExtensible (e.g. FuGEFlow)Could support biospecimen science
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XMLMAGE‐MLGelMLRDML (RT‐PCR)mzML (mass spectrometry)
TAB / spreadsheetMAGE‐TABISA‐TAB
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UMLS MetathesaurusNCI Thesaurus (NCIt)▪ caTissue▪ Common Biospecimen Model (CBM)
LOINCSNOMED‐CTCPT, ICD, etc
Ontology for Biomedical Investigations (OBI)
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5AM Solutions – Specimen Locator: http://www.5amsolutions.com/Artificial Intelligence in Medicine ‐ TissueMetrix: http://www.aim.on.ca/Aptia – Visual Specimen Manager: http://www.aptia.comBioFortis – LabMatrix: http://www.biofortis.comCaisis: http://www.caisis.orgcaTissue: http://catissuecore.wustl.edu/Daedalus Software – BTM: http://www.daedalussoftware.comFreezerworks: http://www.freezerworks.com/GenoLogics – BioVault: http://www.genologics.com/GenVault: http://www.genvault.com/Healthcare IT – BIGR (formerly Ardais): http://www.healthcit.comIMS ‐ BSI‐II: http://www.bsi‐ii.com/LabAnswer: http://www.labanswer.comLabVantage ‐ Sapphire: http://www.labvantage.com/LabWare LIMS: http://www.labware.comOcimum Biosolutions ‐ Biotracker: http://www3.ocimumbio.com/PercipEnz –OnCore: http://www.percipenz.com/PhaseForward –Waban: http://www.phaseforward.com/Thermo Scientific –Nautilus LIMS: http://www.thermo.com
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Many “workhorse” Excel and Access systemscaTissue emerging as standard in cancerCTSAs/i2b2 developing repository informatics
Need for practical informatics ecosystemMoving towards the standardsMigration to enterprise solutionsIntegration with data repositories, AP‐LIS and CP‐LISPublic interfaces to CBM, caTissue, i2b2, etcData capture with simple workflow‐friendly systems (i.e. AP‐LIS, CP‐LIS, REDCap)Integrate biospecimen research with repository operations
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Avoid creating new biospecimen terminologies, formats, object models if an existing one can be adopted or adaptedAlign, harmonize and link data and systems across five components of frameworkKeep the costs and barriers to use lowReality of long tail of investigators / labsReality of time and resource pressure/constraints
Facilitate biospecimen science by integrating research with repository operations
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University of WashingtonNicholas AndersonMalia FullertonKelly Fryer‐EdwardsJames BrinkleyPeter Tarczy‐HornochLawrence TrueRodney SchmidtDavid ChouMarc Provence
UCLARobert DennisAndrew Helsley
DaedalusAzita SharifStefano Santoro
ISBEric Deutsch
ISBER Informatics WGCheryl Michaels, Freezerworks
Prostate SPORE Informatics Group
UW, UCLA, UCSFBaylor, MDACCNorthwestern, Mayo Clinic, U of MichiganHarvard/DFCI, Johns Hopkins, MSKCC
NCI OBBR
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