Genomics:
Coming to a clinical theater near you!
Philip E. TARR, Infectious Diseases Service
Kantonsspital Bruderholz, University of Basel, Switzerland
Agenda
1)Genomics everywhere
2)Applications in HIV Medicine
3)|What will be showing at the theater ?
Agenda
1)Genomics everywhere
2)Applications in HIV Medicine
3)What will be showing at the theater ?
29 May 2010
Genomics: Extraordinary Technical Progress
and Decreasing Cost
Candidate gene
(limited
genotyping)
1$ per SNP*
1 to 100
SNPs
* SNP, single nucleotide polymorphism
Genomics: Extraordinary Technical Progress
and Decreasing Cost
2007
Candidate gene
(limited
genotyping)
Genome-wide
genotyping
(GWAS)
1$ per SNP*
250$
1 to 100
SNPs
1 million
SNPs
* SNP, single nucleotide polymorphism
Genomics: Extraordinary Technical Progress
and Decreasing Cost
2007
Candidate gene
(limited
genotyping)
2011
Genome-wide
genotyping
Exome
sequencing
2014 ?
Whole genome
sequencing
(GWAS)
1$ per SNP*
250$
1000$
1 to 100
SNPs
1 million
SNPs
60 million
nucleotides
* SNP, single nucleotide polymorphism
< 1000$
3000
million
nucleotides
Agenda
1)Genomics everywhere
2)Applications in HIV Medicine
3)What will be showing at the theater ?
Agenda
1)Genomics everywhere
2)Applications in HIV Medicine
3)What will be showing at the theater ?
Until 2007: Candidate Gene Studies aiming at
Prediction of Toxicity of Antiretroviral Therapy
Marsh Hum Mol Genet 2006
Haas HIV Clin Trials 2011
The toxicogenetic paradigm:
HLA-B*5701 Screening
HLA-B*5701 negative
 OK to give abacavir
 essentially 100% negative predictive value for abacavir
hypersensitivity reaction
HLA-B*5701 carrier
 Do not give abacavir, risk of hypersensitivity reaction
Mallal NEJM 2008
Toxicity-related changes in antiretroviral
therapy remain common
Slide: Bruno Ledergerber, SHCS
UGT1A1 SNPs and Atazanavir-associated
Hyperbilirubinemia
15% have unfavorable
genotype
 hazard rate 9.13 for ATV
discontinuation
Lubomirov JID 2011
CYP 2B6 SNPs and Efavirenz-associated
CNS toxicity
5% have unfavorable CYP 2B6
genetic score  hazard rate
3.17 for EFV discontinuation
Lubomirov JID 2011
No solid genetic markers for discontinuation
of tenofovir (renal toxicity)
APOL1 gene variants and HIV
associated nephropathy
Kopp JASN 2011
Atta Kidney Intl 2012
Lubomirov JID 2011
Genetic markers of HIV-related Lipoatrophy
After d4T/AZT exposure
Before ART
HLA B*4001
TNF -238G>A
(Thailand, d4T)
Maher AIDS 2002, Nolan AIDS 2003, Wangsomboonsiri CID 2010
Tarr JID 2005, Capeau 2007
ARβ2
APOC3
Tarr JID 2005, Zanone Poma (ICONA) AIDS 2008
Mitochondrial DNA
Haplogroups
Zanone Poma
(ICONA)
AIDS 2008
Hulgan JID 2008 + CID 2010 Nasi CID 2008, Hendrickson JAIDS 2009
Hemochromatosis Gene Variants
FAS
Zanone Poma AIDS 2008
Hulgan JID 2008
Mitochondrial DNA
insertions, deletions,
point mutations:
Shikuma AIDS 2001 (Yes)
White AIDS 2001 (Yes)
Vittecocq JAIDS 2002 (Yes)
Walker JAIDS 2002 (Yes)
McComsey AIDS 2002,
JAIDS 2005 (No)
Martin AJHG 2003 (Yes)
Ortiz JID 2011 (No)
Morse JID 2012 (No)
Genetic markers of HIV-related Lipoatrophy
After d4T/AZT exposure
Before ART
HLA B*4001
TNF -238G>A
(Thailand, d4T)
Maher AIDS 2002, Nolan AIDS 2003, Wangsomboonsiri CID 2010
Tarr JID 2005, Capeau 2007
ARβ2
APOC3
Tarr JID 2005, Zanone Poma (ICONA) AIDS 2008
Mitochondrial DNA
Haplogroups
Zanone Poma
(ICONA)
AIDS 2008
Hulgan JID 2008 + CID 2010 Nasi CID 2008, Hendrickson JAIDS 2009
Hemochromatosis Gene Variants
FAS
Zanone Poma AIDS 2008
Hulgan JID 2008
Mitochondrial DNA
insertions, deletions,
point mutations:
Shikuma AIDS 2001 (Yes)
White AIDS 2001 (Yes)
Vittecocq JAIDS 2002 (Yes)
Walker JAIDS 2002 (Yes)
McComsey AIDS 2002,
JAIDS 2005 (No)
Martin AJHG 2003 (Yes)
Ortiz JID 2011 (No)
Morse JID 2012 (No)
HIV Host Genetics: An integrated view
(beyond antiretroviral toxicity)
Multiple HIV Exposures
Resistant to
infection
2-3 %
HIV Infection
possible
97-98 %
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Resistant to
infection
2-3 %
CCR5
variants
(Δ 32)
HIV Infection
possible
97-98 %
Genetic
Factors
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Genetic
Factors
Resistant to
HIV Infection
infection
possible
2-3 %
97-98 %
CCR5
without ART
variants
Progressive
Long term non
(Δ 32)
Immunosuppression
progressor
90%
10%
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Genetic
Factors
Resistant to
HIV Infection
infection
possible
2-3 %
97-98 %
CCR5
without ART
variants
Progressive
Long term non
(Δ 32)
Immunosuppression
progressor
90%
10%
HLA Type
B*5701/03, (HCP),
B*27, B*5101, HLA-C,
ZNRD1, CXCR6
 B*0801, B*4501
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Genetic
Factors
Resistant to
HIV Infection
infection
possible
2-3 %
97-98 %
CCR5
without ART
variants
Progressive
Long term non
(Δ 32)
Immunosuppression
progressor
90%
10%
HLA Type
B*5701/03, (HCP),
B*27, B*5101, HLA-C,
ZNRD1, CXCR6
 B*0801, B*4501
ART
Favorable course under
ART
70-80%
«Virological Failure» or
ART toxicity
20-30%
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Genetic
Factors
Resistant to
HIV Infection
infection
possible
2-3 %
97-98 %
CCR5
without ART
variants
Progressive
Long term non
(Δ 32)
Immunosuppression
progressor
90%
10%
HLA Type
B*5701/03, (HCP),
B*27, B*5101, HLA-C,
ZNRD1, CXCR6
 B*0801, B*4501
ART
Favorable course under
ART
70-80%
HLA Type (CD4
Increase)
«Virological Failure» or
ART toxicity
20-30%
e.g. HLA B*5701 (ABC HSR)
CYP 2B6 (EFV + CNS)
UGT1A1 (ATV + Hyperbili)
Genes involved in risk
taking, addictive,
impulsive behavior
Multiple HIV Exposures
Genetic
Factors
Resistant to
HIV Infection
infection
possible
2-3 %
97-98 %
CCR5
without ART
variants
Progressive
Long term non
(Δ 32)
Immunosuppression
progressor
90%
10%
HLA Type
ART regimens begun in
B*5701/03, (HCP),
ART
2004-2006:
B*27, B*5101, HLA-C,
Favorable course
ZNRD1, CXCR6
«Virological Failure» or
 B*0801, B*4501
under ART
ART toxicity
70-80%
95 %
20-30%
5%
HLA Type (CD4
e.g. HLA B*5701 (ABC HSR)
Increase)
2B6 (EFV + CNS)
Prolonged survival  NewCYP
concerns
UGT1A1 (ATV + Hyperbili)
New concerns: “Metabolic complications”,
“non-AIDS conditions”, liver failure, agingrelated conditions
Tarr + Telenti 2010
IL-28B SNPs contribute to response to Hepatitis C
treatment with peg-interferon/ribavirin
Probability of
sustained
virological
response (SVR):
TT: 15-35%
CT: 20-40%
CC: 75-80%
Ge Nature 2009, Thomas Nature 2009, Tanaka Nat Genet 2009, Suppiah Nat Genet 2009, Rauch
Gastroenterology 2010
Genetic Prediction of aging-related conditions in
HIV-infected persons
In the general population: these are all complex
metabolic disorders influenced by multiple genetic
variants (and environmental factors)
Diabetes risk in HIV+ increases according
to number of risk alleles
 GWAS in general population: 22 common SNPs associated with diabetes
≈20% of patients have
unfavorable genetic
background
 relative risk of
diabetes = 2.74
At level of study
population:
Genetic background
explains far more of
the diabetes risk
than does ART
…. but less than
does obesity
Rotger CID
Genetics of Coronary Artery Disease in HIV+
persons
 GWAS meta-analysis in general population: 23 common SNPs assoc. with CAD
Schunkert Nature Genetics 2011
Genome-wide association study of 14,000 cases of seven common diseases and 3,000
shared controls, Nature 2007. Samani N Engl J Med 2007. McPherson Science 2007.
Helgadottir Science 2007. Willer Nat Genet 2008. Broadbent Hum Mol Genet 2008. Saxena
Science 2007. Kathiresan Nat Genet 2008 and NEJM 2008
The MAGNIFICENT Consortium
TOTAL: 681 HIV+ CAD cases and 1,691 HIV+ controls
without CAD events
Genotyping:
HumanCardio-Metabo BeadChip® (Illumina)
a custom array of 196,725 SNPs
from gene regions associated with
multiple metabolic/cardiovascular
traits.
 designed by representatives of 7
GWAS meta‐analysis consortia.
Preuss et al (CARDIoGRAM) Circulation Cardiovasc Genet 2010
Buyske et al PLoS One 2012
Risk factors contributing to CAD events in HIV+
individuals
Genetic
Score
HIV-related
factors
Traditional
CAD risk
factors
Rotger M, MAGNIFICENT Consortium, manuscript in preparation
Risk factors contributing to CAD events in HIV+
Effect of unfavorable
individuals
genetic score:
Genetic
Score
HIV-related
factors
Traditional
CAD risk
factors
 Similar effect size
(odds ratio for CAD) as
diabetes, hypertension,
dyslipidemia
 Independent of family
history for CAD, and
similar effect size
Rotger M, MAGNIFICENT Consortium, manuscript in preparation
Agenda
1) Genomics everywhere
2) Applications in HIV Medicine
3) What will be showing at the theater ?
Agenda
1)Genomics everywhere
2)Applications in HIV Medicine
3)What will be showing at the theater ?
Limitations of GWAS:
much of the heritability of common
disorders remains unexplained
Heritability = the
proportion of the risk
that is explained by
genetic background
Maher Nature 2008 Manolio Nature 2009
At the level of study population, SNPs typically
explain a small proportion of e.g. diabetes or CAD
risk
Smoking explains approx. 10-12% of lung cancer
risk in different populations
This proportion is likely to increase substantially as
many rare genetic variants (with small effect sizes)
are identified
Lusis Nature Rev Genet 2008, Cirulli + Goldstein Nat Rev Genet 2010, www.1000genomes.org
Coming soon to a theater near you:
Whole exome/genome sequencing
Whole genome
sequencing
250$
Whole
Exome
sequencing
1000$
1 million
SNPs
60 million
nucleotides
3000 million
nucleotides
Genome-wide
genotyping
(GWAS)
Metabochip (39$)
Common genetic
variants
(minor allele
frequency >3-5%)
Rare genetic variants
(minor allele frequency
>0.1%)
< 1000$
Exome chip (50$)
Whole exome/genome sequencing:
Enormous potential and some challenges
1) Cost will not be main issue (within 1-2 years will be done for
few $$$)
2) Cost-effectiveness, clinical utility ? (need to show improved
outcomes, ideally in randomized clinical trials)
3) Researchers: database of rare mutations, automated
interpretation, integration of gene-gene/gene-environment
interactions
4) Patient: Access to data, protection of personal genome
privacy, avoidance of discrimination, stigma, psychological
distress
Khoury Genet Med 2009, Ashley Lancet 2010 + 2 editorials, Ayday unpublished
5) For physicians: Training (interpret results, how avoid
“cascade effect” of additional testing), communication
with patient
•
•
•
•
•
X % increase/decrease in disease risk
chance of error
chance of finding high risk for serious disease for which no
cure exists
reproductive implications
how find the time and how bill for a “whole genome
discussion” that might take several hours, assuming that each
individual will carry thousands of risk-modifying variants for
multiple conditions
Khoury Genet Med 2009, Ashley Lancet 2010 + 2 editorials
Summary
1. Increasingly complete assessment of cumulative genetic
background: GWAS  whole exome/genome sequencing
2. In HIV medicine: ≈ 100% genetic prediction of abacavir
hypersensitivity (HLA B*5701)
3. For a number of drugs (ATV, EFV, etc.), we have a pretty good
understanding of the genetic determinants of plasma drug levels
and toxicity
4. More complex situations: Susceptibility to infection, HIV
progression (HLA), Success of hepatitis C treatment (IL-28B)
5. Increasing opportunities to apply «general population» genomics
data to complex metabolic conditions in HIV+ individuals:
•
aging-related conditions (diabetes, osteoporosis, coronary
artery disease etc.)
Acknowledgments:
Institute for Microbiology, University of
Lausanne
Amalio Telenti, Marga Rotger
Ecole Polytechnique Fédérale, Lausanne
Jacques Fellay, Thomas Junier
Institute for Social and Preventive Medicine,
Univ Bern
Thomas Gsponer
Disclosures:
Grants/research support, Consultant/advisory board member,
Travel support to attend medical conferences: Abbott, MSD,
Gilead, Janssen, BMS, ViiV
Philip.Tarr@unibas.ch