Genomics : Coming to a clinical theater near you! Philip E. TARR, Infectious Diseases Service Kantonsspital Bruderholz, University of Basel, Switzerland.
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Genomics: Coming to a clinical theater near you!
Philip E. TARR, Infectious Diseases ServiceKantonsspital Bruderholz, University of Basel, Switzerland
29 May 2010
« More that 130.000 members of Kaiser Permanente have
volunteered to have their DNA scanned »
« The goal of the study is to (…) help uncover the genetic roots
of chronic disease and, perhaps, to find out why some people
live longer than others »
Candidate gene(limited
genotyping)
1$ per SNP*
1 to 100 SNPs
Genomics: Extraordinary Technical Progress and Decreasing Cost
* SNP, single nucleotide polymorphism
2007
Candidate gene(limited
genotyping)
1$ per SNP*
1 to 100 SNPs
Genome-wide genotyping (GWAS)
250$
1 millionSNPs
Genomics: Extraordinary Technical Progress and Decreasing Cost
* SNP, single nucleotide polymorphism
2007
Candidate gene(limited
genotyping)
1$ per SNP*
1 to 100 SNPs
2011
Genome-wide genotyping (GWAS)
250$
1 millionSNPs
2014 ?
Exome sequencing
1000$
60 million nucleotides
Whole genome sequencing
< 1000$
3000 million
nucleotides
Genomics: Extraordinary Technical Progress and Decreasing Cost
* SNP, single nucleotide polymorphism
Until 2007: Candidate Gene Studies aiming at Prediction of Toxicity of Antiretroviral Therapy
Marsh Hum Mol Genet 2006
The toxicogenetic paradigm: HLA-B*5701 Screening
Mallal NEJM 2008
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
Lubomirov JID 2011
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 nephropathyKopp JASN 2011Atta Kidney Intl 2012
Before ART
Genetic markers of HIV-related Lipoatrophy
After d4T/AZT exposure
Hemochromatosis Gene VariantsHulgan JID 2008
APOC3Tarr JID 2005, Zanone Poma (ICONA) AIDS 2008
FASZanone Poma AIDS 2008
TNF -238G>AMaher AIDS 2002, Nolan AIDS 2003, Tarr JID 2005, Capeau 2007
Mitochondrial DNA Haplogroups
Hulgan JID 2008 + CID 2010 Nasi CID 2008, Hendrickson JAIDS 2009
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)
ARβ2Zanone Poma
(ICONA) AIDS 2008
HLA B*4001(Thailand, d4T)
Wangsomboonsiri CID 2010
Before ART
Genetic markers of HIV-related Lipoatrophy
Hemochromatosis Gene VariantsHulgan JID 2008
APOC3Tarr JID 2005, Zanone Poma (ICONA) AIDS 2008
FASZanone Poma AIDS 2008
TNF -238G>AMaher AIDS 2002, Nolan AIDS 2003, Tarr JID 2005, Capeau 2007
Mitochondrial DNA Haplogroups
Hulgan JID 2008 + CID 2010 Nasi CID 2008, Hendrickson JAIDS 2009
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)
ARβ2Zanone Poma
(ICONA) AIDS 2008
HLA B*4001(Thailand, d4T)
Wangsomboonsiri CID 2010
NOT CONFIRMED
After d4T/AZT exposure
Resistant to infection2-3 %
HIV Infection possible97-98 %
Multiple HIV Exposures
CCR5 variants(Δ 32)
Genetic Factors
Genes involved in risk taking, addictive, impulsive behavior
Resistant to infection2-3 %
Long term non progressor
10%
Progressive Immunosuppression
90%
HIV Infection possible97-98 %
Multiple HIV Exposures
without ART CCR5 variants(Δ 32)
Genetic Factors
Genes involved in risk taking, addictive, impulsive behavior
Resistant to infection2-3 %
Long term non progressor
10%
Progressive Immunosuppression
90%
HIV Infection possible97-98 %
Multiple HIV Exposures
without ART CCR5 variants(Δ 32)
Genetic Factors
Genes involved in risk taking, addictive, impulsive behavior
HLA TypeB*5701/03, (HCP), B*27, B*5101, HLA-C, ZNRD1, CXCR6 B*0801, B*4501
Resistant to infection2-3 %
Long term non progressor
10%
Favorable course under ART
70-80%
«Virological Failure» or ART toxicity
20-30%
Progressive Immunosuppression
90%
HIV Infection possible97-98 %
Multiple HIV Exposures
ART
without ART CCR5 variants(Δ 32)
HLA TypeB*5701/03, (HCP), B*27, B*5101, HLA-C, ZNRD1, CXCR6 B*0801, B*4501
Genetic Factors
Genes involved in risk taking, addictive, impulsive behavior
Resistant to infection2-3 %
Long term non progressor
10%
Favorable course under ART
70-80%
«Virological Failure» or ART toxicity
20-30%
Progressive Immunosuppression
90%
HIV Infection possible97-98 %
Multiple HIV Exposures
ART
CCR5 variants(Δ 32)
HLA Type (CD4 Increase)
Genetic Factors
e.g. HLA B*5701 (ABC HSR)CYP 2B6 (EFV + CNS)UGT1A1 (ATV + Hyperbili)
Genes involved in risk taking, addictive, impulsive behavior
HLA TypeB*5701/03, (HCP), B*27, B*5101, HLA-C, ZNRD1, CXCR6 B*0801, B*4501
without ART
Resistant to infection2-3 %
Long term non progressor
10%
Favorable course under ART
70-80%
«Virological Failure» or ART toxicity
20-30%
Progressive Immunosuppression
90%
HIV Infection possible97-98 %
Multiple HIV Exposures
ART
CCR5 variants(Δ 32)
HLA Type (CD4 Increase)
Genetic Factors
e.g. HLA B*5701 (ABC HSR)CYP 2B6 (EFV + CNS)UGT1A1 (ATV + Hyperbili)
Genes involved in risk taking, addictive, impulsive behavior
HLA TypeB*5701/03, (HCP), B*27, B*5101, HLA-C, ZNRD1, CXCR6 B*0801, B*4501
without ART
ART regimens begun in2004-2006:
95 % 5 %
Prolonged survival New concerns
New concerns: “Metabolic complications”, “non-AIDS conditions”, liver failure, aging-related conditions
Tarr + Telenti 2010
IL-28B SNPs contribute to response to Hepatitis C treatment with peg-interferon/ribavirin
Ge Nature 2009, Thomas Nature 2009, Tanaka Nat Genet 2009, Suppiah Nat Genet 2009, Rauch Gastroenterology 2010
Probability of sustained virological
response (SVR):
TT: 15-35%CT: 20-40%CC: 75-80%
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
Rotger CID 2010
≈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
✔ GWAS in general population: 22 common SNPs associated with diabetes
Genetics of Coronary Artery Disease in HIV+ persons✔ GWAS meta-analysis in general population: 23 common SNPs assoc. with CAD
Schunkert Nature Genetics 2011Genome-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
designed by representatives of 7 GWAS meta analysis consortia.‐
a custom array of 196,725 SNPs from gene regions associated with multiple metabolic/cardiovascular traits.
Preuss et al (CARDIoGRAM) Circulation Cardiovasc Genet 2010Buyske et al PLoS One 2012
Genotyping: HumanCardio-Metabo BeadChip® (Illumina)
Risk factors contributing to CAD events in HIV+ individuals
Rotger M, MAGNIFICENT Consortium, manuscript in preparation
Genetic Score
HIV-related factors
Traditional CAD risk factors
Risk factors contributing to CAD events in HIV+ individuals
Rotger M, MAGNIFICENT Consortium, manuscript in preparation
Genetic Score
HIV-related factors
Traditional CAD risk factors
Effect of unfavorable genetic score:
Similar effect size (odds ratio for CAD) as diabetes, hypertension, dyslipidemia
Independent of family history for CAD, and similar effect size
Limitations of GWAS:much of the heritability of common disorders remains unexplained
Maher Nature 2008 Manolio Nature 2009
Heritability = the proportion of the risk that is explained by genetic background
Lusis Nature Rev Genet 2008, Cirulli + Goldstein Nat Rev Genet 2010, www.1000genomes.org
This proportion is likely to increase substantially as many rare genetic variants (with small effect sizes)
are identified
Smoking explains approx. 10-12% of lung cancer risk in different populations
At the level of study population, SNPs typically explain a small proportion of e.g. diabetes or CAD risk
Genome-wide genotyping
(GWAS)
250$
1 millionSNPs
Whole Exome
sequencing1000$
60 million nucleotides
Whole genome sequencing
< 1000$
3000 million nucleotides
Common genetic variants
(minor allele frequency >3-5%)
Metabochip (39$)
Rare genetic variants (minor allele frequency
>0.1%)Exome chip (50$)
Coming soon to a theater near you:Whole exome/genome sequencing
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.)
Institute for Microbiology, University of Lausanne Amalio Telenti, Marga Rotger
Ecole Polytechnique Fédérale, LausanneJacques Fellay, Thomas Junier
Institute for Social and Preventive Medicine, Univ BernThomas Gsponer
Philip.Tarr@unibas.ch
Acknowledgments:
Disclosures: Grants/research support, Consultant/advisory board member, Travel support to attend medical conferences: Abbott, MSD, Gilead, Janssen, BMS, ViiV
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