LIBERATING THE KNOWLEDGE       IN YOUR BIOSPECIMENS       Next Generation Biobanking          Mark A Collins Ph.D.        ...
Introduction • Biomarkers are everywhere • Biomarker research is collaborative • Biomarkers depend on biobanks • Tradition...
Some trends… • Number of new drugs in   decline • Rise in approval of drugs   with companion   diagnostics • 10% FDA appro...
The Landscape is changing…                                                              Targeted Therapy &    Personalized...
Biobanking ChallengesCopyright © 2013 - Proprietary & Confidential
The Critical Biobanking Challenges                                                Externalization                         ...
Driving the Science                                                                          Patient                      ...
Externalization and Collaboration                                                                    Pharma &             ...
Increased Regulatory Scrutiny                                                                         PHI • Privacy and   ...
Bridging the Gap from Conventional                       Biobanking                                                       ...
“Companies that have access to   millions of highly annotated   biospecimens with clear   consent, traceability and tools ...
Powered ByCopyright © 2013 - Proprietary & Confidential
Bridging the Gap from Conventional                       Biobanking                                                       ...
Bridging the Clinical and Research Divide                             Gene expression      Bridging the gap    Diagnoses  ...
The challenge of multidisciplinary dataCopyright © 2013 - Proprietary & Confidential
At The Core… Labmatrix • Data collection and   harmonization   “engine” • Secure,   collaborative   environment • Fine gra...
Ad hoc query and visualization                                                 Integration, Collaboration, Access Control ...
Collect & Harmonize- Creating the                        information hub • Hub is the foundation • Connect to tools to   c...
Bridging the Gap from Conventional                       Biobanking                                                       ...
The Problem – data exploration                                                Data Managers,                              ...
What is Deep Collaboration?                                                                                          Q    ...
Qiagram – Collaborative Scientific                                     Intelligence                                       ...
Collaborative Scientific Intelligence • Facilitate deep   collaboration on research   questions • Generate scientific insi...
Case Studies       NEXT GENERATION       BIOBANKINGCopyright © 2013 - Proprietary & Confidential
Case Studies • Clinical Trial Sample management and Future   Use • Clinical “Hub” – Institutional Research   Collaboration...
Case Study 1: Clinical Trials Sample &      Future Use Sample ManagementBackground:Manage internal and external data on sa...
Sample Tracking in the Research Ecosystem                 Site                                                            ...
Case Study 1: Sophisticated Questions          for Future Use samples 1. Do we have more than one metastatic samples    fr...
“Drawing” Complex ad hoc queries• Build the  query up step-  by-step• Review the  answers in  real-time• Scientific  insig...
More questions – beyond the specimen            What is the incidence of breast cancer recurrence in                     p...
Real Knowledge to Make DecisionsPatient ProfileDCIST size>1cm, ER+, HER2/neu+,Node negativeRadiation therapyBRCA1 mutation...
Case Study 3: Clinical “Hub” • Provide an infrastructure for an institute to   perform clinical studies • Promote standard...
Case Study 3: NIH                               Study #4                                Study #1                          ...
Forms and Workflows for each studyCopyright © 2013 - Proprietary & Confidential
Sample Inventory & Operations                                                 Research BioBank 1B                         ...
Intelligent ReportingCopyright © 2013 - Proprietary & Confidential
Case Study 4: Virtual BiobankBackground:International TB diagnostic biomarker study that have biosamples sent frominternat...
Show BMI vs. Visit # for subjects who            Show and compare biomarker A and Bhave completed the study at site 1.    ...
Summary • Biomarker research is   collaborative • Traditional biobanks are   challenged • Next generation biobank as a   c...
Thank you - any Questions?                                          Come and see us at Booth #403Copyright © 2013 - Propri...
Case Study 2: Sample Centric CTMS            Study                                 Study                                  ...
Real World Requirements            In a biomarker experiment concerning a            particular trial, we‟d like to map ou...
Case Study 2: Sample Centric CTMS• Pre-define all expected  sample records (and their  derivatives) based on your  study’s...
Upcoming SlideShare
Loading in …5
×

Liberating the knowledge in your biospecimens:Next Generation BioBanking

818 views
610 views

Published on

Is your biobank ready for the demands of biomarker based research?
Traditional biobanking software is sample-centric. However, Next Generation Biobanking software extends support into biomarker-based clinical research carried out in a distributed ecosystem of vendors, partners and collaborators, while ensuring security and compliance. Thei presentation from the 2013 CHI Molecular Medicine Triconference discusses the challenges facing biobanks in the personalized medicine era and reviews BioFortis' Next Generation Biobanking platform using several case studies.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
818
On SlideShare
0
From Embeds
0
Number of Embeds
127
Actions
Shares
0
Downloads
37
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • ----- Meeting Notes (2/12/13 20:39) -----intro words - thank organizersOn the surface a talk about biobanking might seem an unusual topic for a session on collaboration. However I want to use biobanking to illustrate the pressures on modern research and how by solving biobanking challenges we can improve collaboration and drive better outcomes.
  • Biomarkers are key to personalized medicine Biomarker research is inherently collaborative
  • ----- Meeting Notes (2/12/13 21:25) -----why are biobanks so important - here are some trends….
  • ----- Meeting Notes (2/12/13 21:25) -----the landscape of research is changing - the road to personalized medicince as we can see is a long and complex on but is founded on identfying biomarkers and for that we need biospecimens - highly annotated biospecimens
  • Modern biomarker based discovery is now reliant on a rich networked, distributed ecosystem of partners, vendors and collaborators
  • Harmonization of biospecimen data with clinical and molecular data – requires tight linkage of the biospecimen with the plethora of molecular and clinical data collected in the course of a study. Support for externalized, collaborative studies - providing the critical information infrastructure to leverage a distributed research ecosystem and its network of collaborators, partners and vendors.  Gain scientific insights - puts powerful tools in the hands of the researcher to facilitate hypotheses driven research without programming or IT help Enhance Security and Compliance - fine-grained access and security controls that extend to all the data linked to a biospecimen, to ensure compliance with all regulatory guidelines.
  • ----- Meeting Notes (2/12/13 21:25) -----So biobanking is really foundational to biomarker research and personalized medicine. However the kind of biobank that is talked about here is really a knowledge base of rich biomarker-specimen and patinet information – we have coined the term “next generation biobank” to describe these new kinds of biobanks that go beyond just a static collection of samples
  • Harmonization of biospecimen data with clinical and molecular data – requires tight linkage of the biospecimen with the plethora of molecular and clinical data collected in the course of a study. Support for externalized, collaborative studies - providing the critical information infrastructure to leverage a distributed research ecosystem and its network of collaborators, partners and vendors.  Gain scientific insights - puts powerful tools in the hands of the researcher to facilitate hypotheses driven research without programming or IT help Enhance Security and Compliance - fine-grained access and security controls that extend to all the data linked to a biospecimen, to ensure compliance with all regulatory guidelines.
  • Increasing emphasis on personalized and targeted therapies which requires free exchange of information between research and the clinic.BioFortis technology enables this frictionless information exchange
  • Pharma & Biotech
  • Harmonization of biospecimen data with clinical and molecular data – requires tight linkage of the biospecimen with the plethora of molecular and clinical data collected in the course of a study. Support for externalized, collaborative studies - providing the critical information infrastructure to leverage a distributed research ecosystem and its network of collaborators, partners and vendors.  Gain scientific insights - puts powerful tools in the hands of the researcher to facilitate hypotheses driven research without programming or IT help Enhance Security and Compliance - fine-grained access and security controls that extend to all the data linked to a biospecimen, to ensure compliance with all regulatory guidelines.
  • ----- Meeting Notes (2/12/13 22:06) -----ask the right questions, be able to collaborate an levergae domain expertise
  • Self explanatory
  • Qiagram allows researchers to explore data by asking questions without the need to program or bring in an IT professional, reducing the time to ask complex questions from days to hours, while allowing all stakeholders to collaborate in the process
  • Liberating the knowledge in your biospecimens:Next Generation BioBanking

    1. 1. LIBERATING THE KNOWLEDGE IN YOUR BIOSPECIMENS Next Generation Biobanking Mark A Collins Ph.D. Director of Marketing, BioFortis, Inc. BOOTH #403Copyright © 2012 - Proprietary & Confidential Copyright © 2009 Proprietary & Confidential
    2. 2. Introduction • Biomarkers are everywhere • Biomarker research is collaborative • Biomarkers depend on biobanks • Traditional biobanks are challenged • Shifting to the next generation biobank • Next generation biobank as a collaborative knowledgebase for biomarker discovery • Real world examplesCopyright © 2013 - Proprietary & Confidential
    3. 3. Some trends… • Number of new drugs in decline • Rise in approval of drugs with companion diagnostics • 10% FDA approved drugs have Pharmacogenomic labeling • 50% of drugs in pipeline are biomarker-based • 1-3B samples banked…and growing • >75% biobanks are disease specificCopyright © 2013 - Proprietary & Confidential
    4. 4. The Landscape is changing… Targeted Therapy & Personalized Targeted Trials Companion Diagnostics medicine Translational Research / Biomarkers / Patient Externalization Segmentation Clinical data Clinical samples Big Data BiobanksCopyright © 2013 - Proprietary & Confidential
    5. 5. Biobanking ChallengesCopyright © 2013 - Proprietary & Confidential
    6. 6. The Critical Biobanking Challenges Externalization & Collaboration Expectation of Increased driving the regulatory science scrutiny BiobanksCopyright © 2013 - Proprietary & Confidential
    7. 7. Driving the Science Patient Data • Generate scientific Clinical EMR insights Data • Beyond the specimen data Scientific • Link in clinical and Insights NGS Molecular molecular data Genomic SpecimenCopyright © 2013 - Proprietary & Confidential
    8. 8. Externalization and Collaboration Pharma & BioPharma • Rich distributed “ecosystem” of Healthcare Academic collaborators, Providers Centers partners and Research vendors Ecosystem CRO BiobanksCopyright © 2013 - Proprietary & Confidential
    9. 9. Increased Regulatory Scrutiny PHI • Privacy and regulatory issues Trial Use Audit Trails • Stringent adherence Increased Samples to compliance Regulatory Scrutiny standards Chain-of- Future Use custody SamplesCopyright © 2013 - Proprietary & Confidential
    10. 10. Bridging the Gap from Conventional Biobanking Enhance security and Support compliance externalized, Gain collaborative scientific studies Harmonize insights biospecimens with clinical and molecular dataCopyright © 2013 - Proprietary & Confidential
    11. 11. “Companies that have access to millions of highly annotated biospecimens with clear consent, traceability and tools to rapidly mine for desired profiles will have an edge in biomarker- based discovery, segmenting patients for clinical trials and developing companion diagnostic /theranostic applications” Large Pharma Dir.PGxCopyright © 2013 - Proprietary & Confidential
    12. 12. Powered ByCopyright © 2013 - Proprietary & Confidential
    13. 13. Bridging the Gap from Conventional Biobanking Enhance security and Support compliance externalized, Gain scientific collaborative insights studies Harmonize biospecimen s with clinical and molecular dataCopyright © 2013 - Proprietary & Confidential
    14. 14. Bridging the Clinical and Research Divide Gene expression Bridging the gap Diagnoses Proteomics with frictionless Medications Bioassays information Health records Imaging exchange Clinical data Bench/Research Bedside/Clinical Frictionless information exchange key to collaborationCopyright © 2013 - Proprietary & Confidential 14
    15. 15. The challenge of multidisciplinary dataCopyright © 2013 - Proprietary & Confidential
    16. 16. At The Core… Labmatrix • Data collection and harmonization “engine” • Secure, collaborative environment • Fine grained access controls • WorkflowsCopyright © 2013 - Proprietary & Confidential
    17. 17. Ad hoc query and visualization Integration, Collaboration, Access Control Research Systems Clinical Systems Translational Research Data Repository LIMS ELN CTMS EDC Biospecimen Management EMR Study Subject Management Other Other Research Current Informatics Clinical Data Apps Environment Data AppsCopyright © 2013 - Proprietary & Confidential
    18. 18. Collect & Harmonize- Creating the information hub • Hub is the foundation • Connect to tools to collaboratively access and explore data • Generate scientific insightsCopyright © 2013 - Proprietary & Confidential
    19. 19. Bridging the Gap from Conventional Biobanking Enhance security and Support compliance externalized, Gain collaborative scientific studies Harmonize insights biospecimen s with clinical and molecular dataCopyright © 2013 - Proprietary & Confidential
    20. 20. The Problem – data exploration Data Managers, Researchers with many overwhelmed by questions across researchers questions on disciplines complex data sources No common language for questions SELECT DISTINCT PATIENT_ID, SAMPLE_ID, SAMPLE_NAME FROM SAMPLE_INVENTORY S INNER JOIN PATIENTS P ON S.PATIENT_ID = P.PATIENT_ID Clinical and INNER JOIN DIAGNOSIS D ON S.PATIENT_ID = D.PATIENT_ID INNER JOIN MEDICATIONS M ON S.PATIENT_ID = M.PATIENT_IDmolecular data INNER JOIN BIOMARKERS B ON S.PATIENT_ID = B.PATIENT_ID WHERE D.DIAGNOSIS_NAME = „LUNG CANCER‟ AND M.MEDICATION_GENERIC_NAME = „CETUXIMAB‟ AND B.BIOMARKER_NAME = „EGFR‟ AND B.OBSERVATION = 1 ORDER BY PATIENT_ID, SAMPLE_NAME “Weeks to months to NEVER” “Lost in Translation”Copyright © 2013 - Proprietary & Confidential
    21. 21. What is Deep Collaboration? Q I A G R A M Single researcher in a Small groups of Deep Collaboration is when silo often can go deep researchers may be able multiple groups of into the data, but maybe to collaborate on asking researchers can collaborate limited by their domain questions but can’t go in asking questions deeper expertise very deep with the tools into the layers of data. they have today Shared domain knowledge allows deeper insightsCopyright © 2013 - Proprietary & Confidential
    22. 22. Qiagram – Collaborative Scientific Intelligence Researchers and data Qiagram acts as a managers can collaborate shared, visual on creating queries language for queries Clinical andmolecular data More efficient and effective query creation Transparent to all stakeholdersCopyright © 2013 - Proprietary & Confidential
    23. 23. Collaborative Scientific Intelligence • Facilitate deep collaboration on research questions • Generate scientific insights from less than perfect data • Collaboratively build and test hypotheses as a team • Build complex, domain specific queries without programmingCopyright © 2013 - Proprietary & Confidential 23
    24. 24. Case Studies NEXT GENERATION BIOBANKINGCopyright © 2013 - Proprietary & Confidential
    25. 25. Case Studies • Clinical Trial Sample management and Future Use • Clinical “Hub” – Institutional Research Collaborations • Virtual BiobankCopyright © 2013 - Proprietary & Confidential
    26. 26. Case Study 1: Clinical Trials Sample & Future Use Sample ManagementBackground:Manage internal and external data on samples collected from clinical trials.Key Objectives:• Provide specimen management for hundreds of trials and millions of samples• Track patient consents on samples• Maintain regulatory compliance• Reconcile different sets of data from CT partners• Provide real-time knowledge on current sample inventory status• Provide support for “future-use” samplesCopyright © 2013 - Proprietary & Confidential
    27. 27. Sample Tracking in the Research Ecosystem Site Sponsor Patient Consent consent Consent patients & Reconciliation acquire samples Consent Deviation / Sample Destruction (e.g. patient withdraw) Sample Collection Trial Setup & Specification Sample Logistics Sample Destruction (patient withdraw or sponsor policy) Sample Sample Inventory Shipment Sample Shipment Vendor / CROCopyright © 2013 - Proprietary & Confidential
    28. 28. Case Study 1: Sophisticated Questions for Future Use samples 1. Do we have more than one metastatic samples from the same patient? 2. Do we have primary and metastatic samples from the same patient? 3. Treatment of metastatic disease 4. Overall survivalCopyright © 2013 - Proprietary & Confidential
    29. 29. “Drawing” Complex ad hoc queries• Build the query up step- by-step• Review the answers in real-time• Scientific insights to drive decision making Copyright © 2013 - Proprietary & Confidential
    30. 30. More questions – beyond the specimen What is the incidence of breast cancer recurrence in patients with the following profile?Type of Data Patient Profile DCISClinical T size>1cm, ER+, HER2/neu+, Node negative Radiation therapyGenotype BRCA1 mutation 185delAGGene Expression HOXB7 gene overexpressionSample Management Tissue banked for immunohistochemistry?Copyright © 2013 - Proprietary & Confidential
    31. 31. Real Knowledge to Make DecisionsPatient ProfileDCIST size>1cm, ER+, HER2/neu+,Node negativeRadiation therapyBRCA1 mutation 185delAGHOXB7 geneoverexpressionTissue banked forimmunohistochemistry?Copyright © 2013 - Proprietary & Confidential
    32. 32. Case Study 3: Clinical “Hub” • Provide an infrastructure for an institute to perform clinical studies • Promote standards/best practices • Provide individual study/researcher portals • Access controls • Permit collaboration, query across studies/groups/researchersCopyright © 2013 - Proprietary & Confidential
    33. 33. Case Study 3: NIH Study #4 Study #1 Centralized Resources Access Controls Audit Study #5 Common Study #2 Standards System integration Study #6 Study #3 Next Generation Biobanking drives collaborative research hubsCopyright © 2013 - Proprietary & Confidential
    34. 34. Forms and Workflows for each studyCopyright © 2013 - Proprietary & Confidential
    35. 35. Sample Inventory & Operations Research BioBank 1B Pathology Sample A597 (text-only label)Copyright © 2013 - Proprietary & Confidential
    36. 36. Intelligent ReportingCopyright © 2013 - Proprietary & Confidential
    37. 37. Case Study 4: Virtual BiobankBackground:International TB diagnostic biomarker study that have biosamples sent frominternational collection sites to be assayed and banked at U.S. facilities.Key Objectives:• Accessible from all collaborating locations• Store relevant patient clinical and visits information• Track sample collection and shipping information• Show real-time study results from prebuilt or ad hoc queries• Improve data quality, consistency, and privacyCopyright © 2013 - Proprietary & Confidential
    38. 38. Show BMI vs. Visit # for subjects who Show and compare biomarker A and Bhave completed the study at site 1. values for each subject. Limit to subjects from site 1 who are biomarker B positive. Copyright © 2013 - Proprietary & Confidential
    39. 39. Summary • Biomarker research is collaborative • Traditional biobanks are challenged • Next generation biobank as a collaborative knowledgebase for biomarker discovery BOOTH #403Copyright © 2013 - Proprietary & Confidential
    40. 40. Thank you - any Questions? Come and see us at Booth #403Copyright © 2013 - Proprietary & Confidential
    41. 41. Case Study 2: Sample Centric CTMS Study Study Study Starts Design Design Samples: Protocols How Many? Patient/Site What Kind? Financials When? Where? Real Time Samples Ensure Monitoring & Reconciliation How Many? Study is of Samples What Kind? Done Right with Study When? WhereCopyright © 2013 - Proprietary & Confidential
    42. 42. Real World Requirements In a biomarker experiment concerning a particular trial, we‟d like to map out the expected sample collection according to the trial protocol and patient enrollment. Afterward, the sample ordering information and assay results, which are stored in separate databases, would be queried to generate a report for QC and management purpose.Copyright © 2013 - Proprietary & Confidential
    43. 43. Case Study 2: Sample Centric CTMS• Pre-define all expected sample records (and their derivatives) based on your study’s specific SOPs or workflows• Then actively monitor during the study Proprietary & ConfidentialCopyright © 2013 - Proprietary & Confidential

    ×