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Advances in addiction researchApplying genetic biomarkers to personalize treatments

June 3, 2015

Webinar Series

Sponsored by:

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office Andy Brooks, Ph.D.

Rutgers UniversityNew Brunswick, NJ

James Baurley, Ph.D.BioRealm, LLCMonument, CO


June 3, 2015

Webinar Series

Sponsored by:

Precision Medicine: A Scientists Guide to Transformational Technologies and Novel ApplicationsDr. Andrew BrooksCOO, RUCDR Infinite BiologicsScientific Director, Bioprocessing Solutions Alliance

3

6/3/2015 4CONFIDENTIAL

• Integrating sample collection, bioprocessing and management with an eye on the molecular central dogma

• Understanding the power of evolving technologies and developing a sample centric roadmap for future sample use

• Creation of sample quality control metrics to standardize across all collections

• Creating a resource that will integrate seamlessly with both industrial and academic collaborative opportunity

Science and Technology:Driving Innovation

• Diagnostics, Therapeutics and Pharmaceuticals tailored for “you” in your specific environment

Defining Importance of Personalized/Precision Medicine

5

Disease Classification: Shift from Anatomy/Histology to Molecular Etiology

6

• Biology Redefined

Lung CancerNon‐Small Cell

MacConaill L E , Garraway L A JCO 2010;28:5219-5228

Sample Collection Errors

2%98%

Percentage of misidentified samples in a biobank

Percentage of collection errors that occur outside of the biobank

Dr. Andy Brooks in “Q&A: RUCDR's Andy Brooks on the Challenges Facing Biorepositories and the Rise of Biobank Arrays”BioArray News, April 23, 2013

Management of complete lifecycle andcomprehensive sample management is critical

Manage the Sample Lifecycle

9

STORAGE

EXTRACTION

COLLECTION

Potential for Adverse Sample Conditions

Limiting Biological Resources:Growing Demand for Access

• Best sample preservation/ processing practices of yesterday may not be best choice for future molecular analyses

• Use least amount of possible primary sample for analysis

• Goal is to better integrate sample collection directly with biosample processinglaboratory activities

Illustrates decreasing size of samplecollections and increasing distributionof samples to sites for analysis byBioprocessing Solutions Alliance

Functional Essentials:Maximizing Biological Resources

• Maximal use of primary samples– Undefined application for downstream analyses

• Efficient processing– Maximizing extraction technologies to improve yield and

quality• Appropriate storage

– Defining storage formats and temperatures to maximize storage infrastructure

• Nucleic acid amplification / Cell line establishment– Creating renewable resources to preserve primary sample

and/or precious collections• Appropriate distribution guidelines

– Define needs for specific downstream applications to preserve sample resources

DNA Extraction/QC Workflow Evolution

OLD

NEW

GenderPolymorphic

13

Define the affects of storage Create processing and aliquoting strategies

that are sample dependent Create robust biomaterials that can be

compared across sites (i.e. gene expression analysis)

Understand the Limitations and Utilization of Sample Types

14

Comprehensive Solutions for Tissue Sample Preservation and Processing

Nucleic Acid Quality Application Performance

Variability (expression) Histology/IHC

% S

ucce

ssfu

l Sam

ples

Qua

lity

Sco

re%

Con

cord

ance

Qua

lity

Sco

re

Frozen Tissue FFPE RNALater PaxTissue

Evaluate sample quality as a function of downstream applications

Understand laboratory impact of sample variability

Preserve both clinical and research analytical and diagnostic workflows

Create functional QC metrics for every sample type

6/3/2015 15

• Increasing need for more data sooner to support translational strategies

• Growth in “omics” technologies is increasing the diversity of sample types and sample analytesthat need to be managed

• Improved visibility to better annotated samples to support sample selection, research utilization and prevention of duplication

• Movement towards companion diagnostics is increasing need for sample collection and storage

• Understanding of the quality of specimens is vital so the right samples are stored and selected for downstream processing and analytical testing

CONFIDENTIAL

Science is a Key Driver for Precision Medicine Program Development

Growth in targeted drug therapies and personalized medicine, combined with the increasing pressure on biopharmaceutical companies to advance research in a more cost efficient manner is increasing the demand for high‐quality, well‐annotated biological samples.

Bioprocessing Solutions AllianceInnovative Service Solutions

Sample Preparation & Extraction

Genetic, Genomic, Gene ExpressionAnalytical Services

Cell Culture &Stem Cell Services

• Sample aliquoting• DNA/RNA extraction (organic, solid phase, salting out and bead based extraction chemistries) • Blood fractionation

• PCR/qPCR• Sequencing• Genotyping• Gene expression• Microarray• Molecular genetics diagnostic

services• Assay design

• Transformation of lymphocytes from blood to (Lymphoblast cell line or Cryopreserved lymphocyte)

• Transient and long term storage in liquid nitrogen

• Stem cells• iPSC production

Bioinformatics &Analytical Services

• Experimental design consultation• Production of common genetic

maps• Resolution of linkages or

genotyping discrepancies• DNA identity fingerprinting• Functional data analysis for genome and transcriptomewide analyses

• Customizable data formatting

Biobanking• Mechanical: ‐70⁰C, ‐80⁰C, ‐20⁰C, 5⁰C• Automated: ‐80⁰C• Liquid Nitrogen: ‐190⁰C (liquid and vapor phase)• Bulk or non‐bulk: 15⁰C ‐ 27⁰C

Repository Management Operations:Enterprise Level Integration

Sample Management Operations

Infrastructure

Liability / Business Continuity

Equipment & Supplies

ProcessesSample Collection

Logistics

Compliance & Quality

Data Integrity & Management

Project Oversight

ControlsStandards & Policies

Scalable & Dedicated Resources

Technology

Bioprocessing

Bioprocessing and Biobanking:Integrated Sample Management

LIMS

Samples Samples Registered

Sample Inventory Data Sample

Bioprocessing Data

Registration Biobanking Bioprocessing Analytics

Consolidation-collect / transport -verify receipt-register

Reconciliation-track chain-of-custody-identify redundancies-curate sample library-aliquot and prepare for testing

Analytics

Rationalization-design strategies/workflows-functional QC-analytical QC

Optimization-dispose of sample redundancies-enrich targets – right sample, right place, right time-QC testing analysis

11 22 33 44

11 33 44

VALUE

Improve QualityReduce TimeReduce Costs

Speed to Market

Biobanking

22

Sample bioprocessing supports identification and inventory optimization

• Diagnosis– Earlier Detection / Single Cell Analysis / Cell Free Detection

• Prognosis– Functional Variant Analysis / Clinical Trial Qualification

• Treatment Plan– Targeted therapeutics

• Rx plan– Metabolic pathway analysis / Minimize target competition

• Monitoring Residual or Metastatic Disease– Follow on disease management

The Reality of a Personalized Medicine Approach

19

Technology has enabled…

• Collecting the sample– Standardization to ensure sample quality (*Regulatory oversight)

• Processing the sample– Establish performance metrics for comparison over time and across tests

(*Standardization metrics)

• Storing the sample– Regulated storage to ensure follow on analyses are not compromised

(*Regulatory compliance)

• Analyzing the sample– Choosing the appropriate technology and the appropriate tests

(*Physician Education)

• INTEGRATING sample data– Data interpretation (*Commercial vs. Physician Guided)

Key Components of a Precision Medicine Program

20

• Expansion beyond the easy targets• Metabolic, Cardiac, Neuro, Psychiatric, etc…

• Earlier baseline genetic risk profiling• Sequencing genomes at birth

• Real time health/genomic monitoring• Biosensor arrays, Smart Homes

• Personalized Rx solutions• Comprehensive medical, genomic and environmental data integration

Future of Personalized Medicine

21

• Global leader in comprehensive sample management solutions

– Prepares and stores millions of samples for bioscience R&D

• Pioneer of Good Storage Practices (GSP)• Offer a state‐of‐the‐art approach to

sample preparation and renewable resourcing of research samples

• Provides innovative ISISS® sample management technology system that provides web‐based access to global sample inventories

• Sample logistics and relocation expertise

• World’s largest genetics based biorepository and top tier research center

– Distributes over 1 million biological and cell‐based samples for discovery research

– Customers include: Mayo Clinic, Sloan Kettering Cancer Center, Army, NIH

• Provides the highest quality biomaterials, technical consultation and logistics

• Pioneers innovative technology platforms– Stem cell research and nucleic acid QC and

nanofluidics technologies

• Utilizes proprietary products such as SNPtraceTM to perform functional quality control

Bioprocessing Solutions Creates a Standardized & Global Operational Environment

22

BiobankingRedefined

6/3/2015 23

• Upstream clinical sample data elements extracted and sent electronically• Sample registered in ISISS® sample management inventory database • Samples are stored and mined for downstream bioprocessing• BSALIMS captures laboratory testing data • Sample inventory data connects to BSALIMS data to improve future sample selection upstream

CONFIDENTIAL

BioProcessing & Biobanking Integration: Sample and Data Harmonization

BioProcessing Solutions Alliance Client Selected LabClient Samples

24

• Collaborative Goals– Importance of sample lifecycle management– Maximizing biological resources is vital– Biobanking best practice implementation– Scientific consulting– Sample collection – reduce sample collection errors– Sample bioprocessing (QC platforms and processes)

Smokescreen Assay DevelopmentBiomaterial QC & Sample Management

25

The Future is Now…

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office Andy Brooks, Ph.D.

Rutgers UniversityNew Brunswick, NJ



June 3, 2015

Webinar Series

Sponsored by:

Smokescreen® ‐ a unified platform for addiction research

BioRealm, LLC

James Baurley, Ph.D.Co‐founder and Principal Data Scientist

27

Motivation

Individual responses are influenced by variation in biological pathways

30PharmGKB nicotine metabolism pathway. Image by PharmGKB and Stanford University.

Motivating Questions

• How does variation in G, E, pathways influence…– Dependence?– Treatment response?– Risk of related diseases?– Adverse events?

31

A uniform platform is needed to...

• Capture genotypes

• Combine it with phenotypes and exposures

• Analyze data in new ways

32

Our requirements

• Common panel• Hypothesis testing and discovery• Fully customizable array• Reproducible manufacturing• Automated and high quality workflows• Paired software and services for common bioinformatics and analysis tasks

• Cost

33

Patient biospecimen collection

Raw data uploaded for processing

De-identified data entered into secure

web application

DNA samples processed at partner lab

Algorithms applied to cleaned dataReports

A uniform and simple process

296K Imputation‐based GWAS backbone (multi‐population) 11K Fine‐mapping markers for

CHRNA5‐CHRNA3‐CHRNB4 and CYP2A6‐B6

255K255KMulti‐population tag SNPs for >1000 addiction‐related genes17K Exome SNPs and indels5K Loss‐of function markers

5K Top NeuroSNP markers2K PNAT SNPs12K Quit Success Score v1.0 SNPs657 High‐priority literature SNPs1.9K Tobacco smoke constituent uptake and metabolic phenotype markers

Smokescreen® Genotyping ArrayA platform for addiction research

10K eQTLs6K AIMs2K Pharmacogenomic SNPs9K HLA/KIR markers

9K UK Biobank lung function markers2K UK Biobank cardiovascular markers1.2K Psychiatric markers3K Lung cancer markers8K NHGRI GWAS Catalog markers

Comorbidities and DiseasesCandidate Addiction Genes

Genome‐wide Discovery Fine‐mapping

General Content

Priority Addiction Content

Affymetrix® Axiom® Platform>646K SNPs

35

Imputation‐based genome‐wide coverage

MAF≥ 0.05

MAF≥ 0.01

% SNPs imputation r2 ≥ 0.8

36

†Nelson SC, Doheny KF, Pugh EW, et al. Imputation‐based genomic coverage assessments of current human genotyping arrays. G3 (Bethesda). 2013;3(10):1795‐807.‡Data calculated by BioRealm using methods described in Nelson et al. (2013)

Candidate Addiction GenesNeuroSNP Project

Pharmacogenetics of Nicotine Addiction Treatment (PNAT)

Tennessee Mouse Genome Consortium

1000+ Addiction Genes• Pairwise tagging using 1000 Genomes data, June 2012 (255K tag SNPs)

• Includes rare exonic, LoF, and eQTL markers

• >97% coverage (MAF ≥ 0.05, r2 ≥ 0.9) in East Asian (ASN), European (EUR), and Africans (YRI)

1. Dopamine Synthesis2. Neurotransmitter Transporters3. Adrenergic System4. Alcohol System5. Cholinergic System6. Dopamine System7. Endocannabinoid System8. Cessation Pharmacogenomics 9. GABA System10. Glutamatergic System11. Nicotine Metabolism System12. Nicotine System13. Opioid System14. Serotonergic System15. Tyrosine16. Mouse Addiction QTLs17. Investigator‐nominated

Fine‐mapping of 15q25.1 (CHRNA5‐A3‐B4)

• Nicotinic receptors• chr15:78673671‐79225948 (~550 kb)• 8948 SNPs and indels (all known SNPs, MAF>0)• Average of 1 marker per 62 bp


Fine‐mapping CYP2A6‐CYP2B6• All known rare and common SNPs (MAF>0) from 1KG, HapMap, Exome sequencing project

• CYP2A6, n=612, average of 1 marker per 75 bp

• CYP2B6, n=1,628, average of 1 marker per 45 bp

Additional tagging of common SNPs (MAF≥0.05) around region, including EGLN2, CYP2A7, CYP2G1P, CYP2B7P1

296K Imputation‐based GWAS backbone (multi‐population) 11K Fine‐mapping markers for

CHRNA5‐CHRNA3‐CHRNB4 and CYP2A6‐B6

255K255KMulti‐population tag SNPs for >1000 addiction‐related genes17K Exome SNPs and indels5K Loss‐of function markers

5K Top NeuroSNP markers2K PNAT SNPs12K Quit Success Score v1.0 SNPs657 High‐priority literature SNPs1.9K Tobacco smoke constituent uptake and metabolic phenotype markers

Smokescreen® Genotyping ArrayA platform for addiction research

10K eQTLs6K AIMs2K Pharmacogenomic SNPs9K HLA/KIR markers

9K UK Biobank lung function markers2K UK Biobank cardiovascular markers1.2K Psychiatric markers3K Lung cancer markers8K NHGRI GWAS Catalog markers

Comorbidities and DiseasesCandidate Addiction Genes

Genome‐wide Discovery Fine‐mapping

General Content

Priority Addiction Content

Affymetrix® Axiom® Platform>646K SNPs

45

Smokescreen® Analytic Software and ServicesA platform for addiction research

46


Quality control of Smokescreen data

1 Axiom Best-Practices workflow

2 Check for batch effects

3 Genotyped sex check

4 Replicate sample concordance

5 SNP reproducibility in sample replicates and Mendelian error checks in trios

6 Hardy-Weinberg Equilibrium calculation

7 Identification of duplicates with different subject identifiers

8 Identification and/or verification of related individuals

9 Excess heterozygosity check

10 Sample and SNP quality statistics and reports

11 Analysis ready data delivery in PLINK format

12 dbGAP submission

47


Analysis of Smokescreen Data

1 Ancestry estimation with Structure and fastStructure software packages

2 Principal component analysis (PCA)

3 Imputation to 1000 Genomes Project

4 Select samples and variants for genetic association scan

5 Combine results in meta-analyses

Interpretation and Reporting

1 Ancestry and population structure graphics

2 Reporting of clinically relevant panels (PharmGKB panel) and biomarkers

3 Tables and figures

48

Nicotine metabolism in multiple populations

• Individuals with higher rate of nicotine metabolism and clearance are more likely to have higher smoking exposure levels (Benowitz, et al. 2003).

• Understanding population‐specific variation in nicotine metabolic genes needed for biomarker development and optimization (Quaak, et al. 2009, Whirl‐Carrillo, et al. 2012).

• Study evaluated variation in 12 nicotine metabolic genes in relation to the nicotine metabolism ratio (NMR) of trans‐3’‐hydroxycotinine to cotinine in multiple populations.

• 346 individuals from three laboratory‐based studies of nicotine metabolism were genotyping using the Smokescreen platform.

49

In the liver, enzymes from cytochrome P450 (CYP) and UDP‐glucuronosyltransferases (UGT) families take nicotine as a substrate, influencing its metabolites’ ratios (Benowitz, et al. 2009).

50PharmGKB nicotine metabolism pathway. Image by PharmGKB and Stanford University.

Study Population

• Individuals selected from three laboratory‐based studies of nicotine metabolism:– Pharmacokinetics in Twins (“PKTWIN”, Swan et al., 2003)

– Pharmacogenetics Study of Nicotine Metabolism (“588”, Dempsey et al., 2004)

– Nicotine Metabolism Study on Individuals with smokers as relatived (“SMOFAM”, Swan et al., 2003)

• Analysis in EuA (n=160), AfA (n=48), AsA (n=52). Combined in meta‐analysis.

51

Nicotine metabolism analysis plan

52

P explanatory variables Yi NMR for individual iX matrix of P variablesE (Yi ) = μi

g link functionβp regression coefficient for variable pIp indicator if variable p is in the model

Black – metaRed – EuABlue – AfAGreen ‐ AsA

41330000 41340000 41350000 41360000 41370000

02

46

812

chr19:41329443-41376352 CYP2A6

chr 19 position

-log1

0(p-

val)

41480000 41490000 41500000 41510000 41520000 41530000 41540000

01

23

45

6

chr19:41477204-41544303 CYP2B6

chr 19 position

-log1

0(p-

val)

53

Discussion• CYP2A6 and UGT2B10 together are responsible for metabolizing and clearing 85‐90% of nicotine intake (Murphy et al. 2014)

• CYP2B6 metabolizes bupropion, used for cessation to reduce nicotine craving (Bloom et al. 2013).

• Association pattern and significance with NMR remained intact for CYP2B6 after analysis adjusted for CYP2A6’s top SNP (other CYP2A6 SNPs also retained significance)

• These markers may be used to model smoking behavior, treatment response, and tobacco‐related diseases.

54

EExposures

G

X1

Genes

Unobserved intermediate events

YDisease

“Topology” of the networkZ

External biological knowledge (“Ontologies”)

X2 X3

XnXn‐1

Next steps – a pathway perspective to prediction

Main messages

• Trend towards custom targeted arrays (AffymetrixAxiom) and paired analytic software and services to interpret data.

• Biomarker‐based study designs are powerful at characterizing associations in diverse populations.

• Select array content to match your research questions.• Multivariate statistical approaches are needed to develop prediction models.

• Cycle needed between research and numerous clinical products

56

Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office To submit your

questions, click theAsk a Question

button


June 3, 2015

Webinar Series

Sponsored by:

Andy Brooks, Ph.D.Rutgers UniversityNew Brunswick, NJ


For related information on this webinar topic, go to:

Look out for more webinars in the series at:

webinar.sciencemag.org

To provide feedback on this webinar, please e‐mailyour comments to [email protected]

Brought to you by the Science/AAAS Custom Publishing Office


June 3, 2015

Webinar Series

Sponsored by:

www.affymetrix.com/axiom_biobankwww.biostorage.com/bioprocessing

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