advances in pharmacogenomics and population-based identification of "at-risk" groups

Advances in Pharmacogenomics and Population-based Identification of "At-Risk" Groups

Robert L. Davis, MD, MPHVisiting Scientist

Immunization Safety OfficeCenters for Disease Control and Prevention

Senior InvestigatorCenter for Health StudiesGroup Health Cooperative

Seattle, Washington


Goals:

To understand the genetic variations that predispose children, adolescents or adults to vaccine adverse events or vaccine failure


The prototypic study approach:

Design type: Case-control study (rare outcome)

Case definition (example): Seizures following MMR vaccinationControl definition (ex): Children vaccinated with MMR who did not experience seizures

Assess genetic differences between cases and controls, using either ‘candidate’ genes or ‘whole genome’ approach

Optimally: identify a single polymorphism or group of polymorphisms very common in cases, uncommon in controls


The prototypic study application:

If able to identify a single polymorphism or group of polymorphisms very common in cases yet uncommon in controls (ie high RR for disease):

Assess predictive power of polymorphism(s) when applied to populationHow many people need to be identified & excluded from vaccination to prevent one seizure?

Quantify risks and benefits of excluding children/adults from vaccinationMay be different depending on vaccine, outcome, likelihood of exp to wild type

disease, presence of herd immunity, etcEx: MMR and seizures Smallpox vaccine and myocarditis

Study/identify risk minimization processesEx: tylenol to prevent febrile seizures; vaccinating at different ages; not vaccinating, etc


How do we create the system necessary for the optimal scientific study?

Needs:

SystemBasic science backgroundTechnologyAnalytic capabilityScientistsEfficiencies



System needs:

Need to ascertain rare events after vaccination On the order of 1/1000 to 1/10,000 (or even rarer)

Cannot be done with premarketing or even postmarketing clinical trials

Option 1: VAERS (Vaccine Adverse Events Reporting System)Passively reported VAE

Option 2: Population based settingActive identification of VAEs possibleAdv: full spectrum of VAE

unbiased ascertainment



Systems: Vaccine Safety DatalinkBegan in 1991 as a collaborative project between CDC and four HMOs:

Group Health Cooperative, Seattle, WANorthwest Kaiser Permanente, Portland, ORNorthern California Kaiser Permanente, OaklandSouth California Kaiser Permanente, Los Angeles

Expanded in 2000 to include four more HMOs:Harvard Pilgrim Health Care, Boston, MAHealthPartners, Minneapolis, MNKaiser Permanente Colorado, Denver, COMarshfield Clinic, Marshfield, WI

Total over 10 million members

Vaccine Safety Datalink (VSD)

VaccinationRecords

HealthOutcomes

(Hospital)(ER)

(Clinic)

PatientCharacteristics

(Birth records)(Census)

VSD LinkedAnalysis Database



Needs:

SystemBasic science backgroundTechnologyAnalytic capabilityScientistsOther: Efficiencies



Needs:

Basic science background

Understanding of pathways involved in potential VAEsBasic disease pathogenesisInflammation pathwaysImmune response pathways

Used to identify potential candidate genes and candidate gene pathways

For many (if not most) of VAEs, this is currently unknown Distinct from medication AE related (for ex) to cyp450 pathway



Needs:




Needs:TechnologyAnalytic capability

Technology:Use of 500K chips for SNP analysis becoming more routineCould partner with producers of chips (Affy; Illumina etc) for cost, individualized

production etc

Specimen collection: typically blood samples – (buccal swabs or other in future offer possibility of ‘remote’/streamlined collection of specimens from case/family)

Data tracking one of major challenges of Human Genome ProjectWill need attention in any future endeavors for vaccine genomics



Needs:TechnologyAnalytic capability

500K chips give information on 500,000 single nucleotide polymorphisms

Challenges:‘typical’ logistic regression analysis has 10-100 covariates (not 500K)

1. Running chips is a specialized ‘knowledge/capability’2. Need mainframe computers for data storage and analysis3. Need advanced/new biostatistical algorithms for fitting models4. Almost guaranteed to find more false than true positives5. Individual SNPs might not be as important or illuminating as haplotypes


Needs:Analytic capability

1. Running chips is a specialized ‘knowledge/skill’2. Need mainframe computers for data storage and analysis

Need to create this capability (ie within CDC) or collaborate with academic partners

3. Need advanced/new biostatistical algorithms for fitting models

Needs specialized training in biostatistical genetics and genetic epidemiology


Needs:Analytic capability

4. Almost guaranteed to find more false than true positives

For candidate genes: can use standard approach

For non-candidate genes: (a) assess strength and consistency of association; (b) assess biologic plausibility (if possible)(c) replicate, replicate, replicate

5. Individual SNPs might not be as important or illuminating as haplotypes



Needs:




Needs:For identification of cases, selection of controls, and enrollment

Knowledge of vaccine/schedule/adverse eventsCollaborative network with organizations/populations of interestHistorically: infectious disease specialists; epidemiologists

For basic science/gene pathways:Immunologists/infectious disease specialistsGeneticists

For analysis:Collaboration with partners with capabilities to run samplesBiostatisticians/genetic epidemiologists to analyze data



Needs:



Needs:Efficiencies

Consider moving away from specific control groups.

Option: genotype 1000 people from each HMO and use that as a standard control group for every study

Expensive to begin with, but saves cost savings and more efficient in the long run


Vision for the Future:

Screen VSD data-sets yearly Identify subjects/collect specimens on cases

q yr: febrile seizures; severe limb swellingq 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxisw/high profile situations: ie intussusception;GBS

Run genome-scans (500K chips or higher) on cases Compare with standard age, HMO, race matched controls


Vision for the Future:Screen VSD data-sets yearly

Identify subjects/collect specimens on casesq yr: febrile seizures; severe limb swellingq 5 yrs: arthritis; prolonged crying; q 10 yrs: encephalopathy; GBS; anaphylaxisw/high profile situations: ie intussusception;GBS

Run genome-scans (500K chips or higher) on cases Compare with standard age, HMO, race matched controls

Assess findings for _candidate_ genesGenerate new set(s) of potential candidate genes/pathways for next iteration



Presently:

System: exists in integrated fashion (VSD)Basic science background/scientific expertise: needs concentration/integrationTechnology/Analytic capability: available; needs coordinated approachEfficiencies: needs evaluation

VSD Study Types

Age specific• Children

– Seizures (primarily febrile) after DTP and MMR

• Adolescents– Safety of new meningococcal conjugate vaccine

• Adults– Autoimmune thyroid disease– Multiple sclerosis after hepatitis B

• Elderly– Flu vaccine safety and efficacy

VSD Data management

Source Data: Data Center:Vaccination SAS programsHealth outcomes/diseasePatient characteristics

Analytic data file

Highly controlled processStandardized data collection from each site

Confidential and deidentifiedHIPAA compliant/Minimal data transfer

advances in pharmacogenomics and population-based identification of "at-risk" groups

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