liberating the knowledge in your biospecimens:next generation biobanking

Copyright © 2009Proprietary & Confidential

Copyright © 2012 - Proprietary & Confidential

LIBERATING THE KNOWLEDGE IN YOUR BIOSPECIMENSNext Generation Biobanking

Mark A Collins Ph.D. Director of Marketing,

BioFortis, Inc.

BOOTH #403


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 examples


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 specific


The Landscape is changing…

Personalized medicine

Externalization

Big Data

Targeted Therapy & Companion DiagnosticsTargeted Trials

Translational Research / Biomarkers / Patient

Segmentation

Clinical data Clinical samples

Biobanks


Biobanking Challenges


The Critical Biobanking Challenges

Biobanks

Expectation of driving the

science

Externalization &

Collaboration

Increased regulatory

scrutiny


Driving the Science

• Generate scientific insights

• Beyond the specimen data

• Link in clinical and molecular data

Scientific Insights

Patient Data

Clinical Data

Molecular

SpecimenGenomic

NGS

EMR


Externalization and Collaboration

• Rich distributed “ecosystem” of collaborators, partners and vendors

Research Ecosystem

Pharma & BioPharma

Academic Centers

BiobanksCRO

Healthcare Providers


Increased Regulatory Scrutiny

• Privacy and regulatory issues

• Stringent adherence to compliance standards

Increased Regulatory

Scrutiny

PHI

Trial Use Samples

Future Use Samples

Chain-of-custody

Audit Trails


Bridging the Gap from Conventional Biobanking

Harmonize biospecimens with clinical and molecular data

Gain scientific insights

Support externalized, collaborative studies

Enhance security and compliance


“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.PGx


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Bridging the Clinical and Research Divide

14

Bench/Research Bedside/Clinical

Gene expressionProteomicsBioassaysImaging

DiagnosesMedicationsHealth recordsClinical data

Bridging the gap with frictionless

information exchange

Frictionless information exchange key to collaboration


The challenge of multidisciplinary data


At The Core… Labmatrix

• Data collection and harmonization “engine”

• Secure, collaborative environment

• Fine grained access controls

• Workflows

Web accessible Information management for clinical and translational research


Ad hoc query and visualization

Integration, Collaboration, Access Control

Translational Research Data

Repository

Biospecimen Management

Current Informatics Environment

LIMS ELN

OtherResearch Data Apps

CTMS EDC

OtherClinical

Data Apps

Research Systems Clinical Systems

Study Subject Management

EMR


Collect & Harmonize- Creating the information hub

• Hub is the foundation• Connect to tools to

collaboratively access and explore data

• Generate scientific insights


Clinical and molecular data

The Problem – data exploration

Data Managers, overwhelmed by

researchers questions on complex data sources

Researchers with many questions across

disciplines

“Weeks to months to NEVER”“Lost in Translation”

SELECT DISTINCT PATIENT_ID, SAMPLE_ID, SAMPLE_NAME

FROM SAMPLE_INVENTORY S INNER JOIN PATIENTS P ON S.PATIENT_ID = P.PATIENT_ID

INNER JOIN DIAGNOSIS D ON S.PATIENT_ID = D.PATIENT_ID

INNER JOIN MEDICATIONS M ON S.PATIENT_ID = M.PATIENT_ID

INNER JOIN BIOMARKERS B ON S.PATIENT_ID = B.PATIENT_ID

WHERED.DIAGNOSIS_NAME = ‘LUNG CANCER’ AND

M.MEDICATION_GENERIC_NAME = ‘CETUXIMAB’ ANDB.BIOMARKER_NAME = ‘EGFR’ AND

B.OBSERVATION = 1ORDER BY PATIENT_ID, SAMPLE_NAME

No common language for

questions


What is Deep Collaboration?

Single researcher in a silo often can go deep into the data, but maybe limited by their domain expertise

Small groups of researchers may be able to collaborate on asking questions but can’t go very deep with the tools they have today

QIAGRAM

Deep Collaboration is when multiple groups of researchers can collaborate in asking questions deeper into the layers of data. Shared domain knowledge allows deeper insights


Clinical and molecular data

Qiagram – Collaborative Scientific Intelligence

Researchers and data managers can collaborate

on creating queriesQiagram acts as a

shared, visual language for queries

More efficient and effective query creationTransparent to all stakeholders


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 programming

Real-time, visual, ad-hoc query tool, connecting researchers to answers

23


NEXT GENERATION BIOBANKING

Case Studies


Case Studies

• Clinical Trial Sample management and Future Use

• Clinical “Hub” – Institutional Research Collaborations

• Virtual Biobank


Background: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” samples

Case Study 1: Clinical Trials Sample & Future Use Sample Management


Site Sponsor

Vendor / CRO

Sample Shipment

Consent Deviation / Sample Destruction

(e.g. patient withdraw)

Sample Destruction

(patient withdraw or sponsor policy)

Patient Consent

Trial Setup &Sample Logistics

Sample CollectionSpecification

Sample Inventory

Consent Reconciliation

consent patients &

acquire samples

Sample Shipment

Sample Tracking in the Research Ecosystem


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 survival


“Drawing” Complex ad hoc queries

• Build the query up step-by-step

• Review the answers in real-time

• Scientific insights to drive decision making


More questions – beyond the specimen

Patient Profile

DCIS

T size>1cm, ER+, HER2/neu+, Node negative

Radiation therapy

BRCA1 mutation 185delAG

HOXB7 gene overexpression

Tissue banked for immunohistochemistry?

What is the incidence of breast cancer recurrence in patients with the following profile?

Type of Data

Clinical

Genotype

Gene Expression

Sample Management


Patient Profile

DCIST size>1cm, ER+, HER2/neu+, Node negative

Radiation therapy

BRCA1 mutation 185delAG

HOXB7 gene overexpression

Tissue banked for immunohistochemistry?

Real Knowledge to Make Decisions


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/researchers


Case Study 3: NIH

Next Generation Biobanking drives collaborative research hubs

Centralized Resources

Access ControlsAudit

Common Standards

System integration

Study #1

Study #2

Study #3

Study #4

Study #5

Study #6


Forms and Workflows for each study


Research BioBank 1BPathology Sample A597

(text-only label)

Sample Inventory & Operations


Intelligent Reporting


Case Study 4: Virtual Biobank

Background:International TB diagnostic biomarker study that have biosamples sent from international 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 privacy


Show BMI vs. Visit # for subjects who have completed the study at site 1.

Show and compare biomarker A and B values for each subject.

Limit to subjects from site 1 who are biomarker B positive.


Summary

BOOTH #403

• Biomarker research is collaborative

• Traditional biobanks are challenged

• Next generation biobank as a collaborative knowledgebase for biomarker discovery


Thank you - any Questions?

2012 Corporate Excellence Award

Come and see us at Booth #403


Case Study 2: Sample Centric CTMS

Study Design

ProtocolsPatient/SiteFinancials

Samples

How Many?What Kind?

When? Where

Real Time Monitoring & Reconciliation

of Samples with Study

Ensure Study is

Done Right

Study Design

Samples:How Many? What Kind?

When? Where?

Study Starts


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.


Proprietary & Confidential

• Pre-define all expected sample records (and their derivatives) based on your study’s specific SOPs or workflows

• Then actively monitor during the study

Case Study 2: Sample Centric CTMS

liberating the knowledge in your biospecimens:next generation biobanking

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