Download The Realization of Genomic Medicine

The Realization of Genomic
Medicine
HL7 Work Group
September 14, 2011
Leslie G Biesecker, MD
Chief and Senior Investigator
Genetic Disease Research Branch
National Human Genome Research Institute
National Institutes of Health
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Background
Assumptions
• Substantial component of common disease is
an amalgamation of rare processes
• Genetic testing facilitates genotypephenotype correlation
• Prediction – ‘Personalized medicine’
• Limited to germline
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Current Clinical Practice Paradigm
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Gather history
Examine patient
Formulate differential diagnosis
Apply clinical test(s) to patient
Interpret result(s), refine differential, diagnose
Treat
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Low Throughput Paradigms
• Must frontload hypotheses, differentials and
phenotyping because the assays or clinical
tests are
– Rate/time limiting
– Expensive
– Noisy
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Low Throughput Paradigms
• Must frontload hypotheses, differentials and
phenotyping because the assays or clinical
tests are
– Rate/time limiting
– Expensive
– Noisy
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ClinSeq™ Cohort
• Enrolled >900 subjects
• Primary phenotype atherosclerosis
• Consented for
– Full sequencing
– Data sharing
– Return of results
– Downstream phenotyping - any
• 572 exomes
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Whole Genome Sequencing
• Use read depth as a proxy for CNVs
• Manually reviewed WGS data
– (Note that software to do this is available)
• 1.7 Mb Deletion of 17p12, including PMP22 gene
-> Hereditary liability to Nerve and Pressure
Palsies
• Patient previously unaware of diagnosis
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What about known disease genes?
• Intersect all variants in a WGS data set with a
database of ‘known’ disease-causing
mutations
Results of HGMD intersection
• 64 variants at positions said to cause disease
in HGMD
– 43 >> literature review suggests not causative of
mendelian trait
– 17 >> reference sequence has allele HGMD says is
causative
– 5 >> apparently causative
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Five disease gene mutations
Mut. ID
Gene
Disease
Subst.
State
CM094237
WNT10A
Ectodermal Dysplasia
p.Phe228Ile
het
CM001280
SLC26A4
Pendred syndrome
p.Thr193Ile
het
CM990677
GALT
Galactosemia
p.Met336Leu
Het
CM094615
SEC23B
Dyserythropoietic anemia
p.Val426Ile
Het
CM012456
RP2
XL retinitis pigmentosa
p.Arg282Trp
hemi
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Clinical Analysis: Cancer
• 37 Cancer genes
• Cancer exome: 451
Total variants
• Filtered
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Frequency
Damaging
Literature review
Database review
Family history
• Categorized by modified
IARC scheme
– 5: >99% prob.
pathogenic
– 2: 95-99%
– 3: 5-95%
– 2: .1-5%
– 1: <.1%
– 0 Poor quality sequence
call
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Clinical Example: BRCA2 Frameshift
• p.T2766NfsX11 - known pathogenic allele
• Not a high-risk pedigree
• Note that this paradigm does not require
that the patient or their relative has
suffered or died of this disease
• True preventive medicine
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Genomics and Clinical Care I
• Newborn exam – normal
• Genome sequenced from cord blood
• Using parent and clinician key, interrogate
genome for NBS gene panel
• Order recommended follow-up tests
• Restrict diet
• Consult to metabolic expert
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Genomics and Clinical Care II
• Patient presents to clinic for asthma
• History and pertinent examination
• Using patient &clinician key, interrogate
genome for susceptibility & pharmacogenetics
• Prescribe treatment
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Genomics and Clinical Care III
• Couple presents to clinic for preconceptual
counseling
• Using patient, partner, and clinician key,
interrogate genomes for carrier states
• If one hit each in a gene, refer for counseling
and consideration of PND, PGD, etc.
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Genomics and Clinical Care IV
• Patient presents to clinic for routine
healthcare evaluation
• Patient reviews interactive educational tool on
breast/ovarian cancer susceptibility
• Using patient and clinician key, interrogate
genome for susceptibility alleles
• If abnormal allele identified, refer to cancer
genetics clinic
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Why Not?
• Infrastructure to generate, store, distribute data
• Clinical research to define utility of approach
• Clinician-friendly analytic software & robust
databases
• Changing clinician training, attitudes, & practice
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Why Not?
• Infrastructure to generate, store, distribute data
• Clinical research to define utility of approach
• Clinician-friendly analytic tools & robust
databases
• Changing clinician training, attitudes, & practice
• All of these are hard to do and will take time
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Clinician-Friendly Algorithms
• Most genetics and genomics should disappear
into general and non-genetic subspecialty
practice
• Flag mutations for which it is essential to
practice highest standard of non-directive
counseling
• Rare, atypical, outlier cases efficiently shunted
to an expert
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But We Are Stuck
• Many components to
develop and test
• Need to find a way
forward
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Rare Disease Challenge
• How many syndromes?
– Syndromes Head & Neck
• >2,500 entities
– London Medical Database
• >4,500 entities
– Many rare, few with genes, few
with natural history
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Build Out From Rare Diseases
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Build sequencing & data infrastructure
Start with specialists using informatics tools
Specialists teach generalists
Learn about many ‘incidental’ findings from
these families
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Data Infrastructure
• Interrogate genome once
– Prediction is that doing this once will be cheaper
than lifetime cost of multiple interrogations
• Secondary benefit is instant availability
– Consequence is that one gets much more data
than anyone needs or wants
• Secure storage with ready accessibility
• Robust database of correlation of variants
with phenotypes
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Prognostic Tool