HCV research: Recent findings and future challenges
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Transcript of HCV research: Recent findings and future challenges
Max Moldovan Bioinformatics Division, WEHI
Bioinformatics Seminar September 28, 2009
HCV research: Recent findings and future
challenges
Presentation plan
Introduce Hepatitis C Virus and give information about treatment and related problems
Summarize the recent findings reported in the literature
Indicate the remaining questions to answer
Hepatitis C Virus (HCV) Known from 1970s, but officially discovered in
1989 Around 200mln people affected worldwide Affects liver Often asymptomatic or with mild symptoms e.g.
fatigue, poor appetite, joint pains etc. Chronic infection can lead to fibrosis, cirrhosis
and liver cancer The standard, only approved and currently most
effective treatment is pegylated interferon-α plus ribavirin
HCV infection transmission by source (USA)
Source: Center for Disease Control and Prevention
Source: www.hepatitisctreatmentcenter.com
HCV infection by genotype
HCV infection by genotype
Genotype 1: 77%
HCV genotype 1 progression
HCV infection
Clearance (~20%)
Chronic HCV (~80%)
No treatment response (~50%)
Treatment response (~50%)
Source: Based on NIH information
Approved treatment
Pegylated interferon-α plus ribavirin (PEG-IFN-α/RBV) for 24 to 48 weeks
It is assumed that interferon mobilizes body’s natural defense against viral infection
The mechanism of action of ribavirin is not completely understood
Treatment leads to a number of side effects
Common side effects of PEG-IFN-α/RBV treatment
fatigue muscle aches headaches nausea and vomiting skin irritation at the
injection site low-grade fever
weight loss irritability depression mild bone marrow
suppression hair loss (reversible)
Uncommon side effects of PEG-IFN-α/RBV treatment (~2%)
autoimmune disease (especially thyroid disease) severe bacterial infections marked thrombocytopenia marked neutropenia seizures depression and suicidal ideation or attempts retinopathy (microhemorrhages) hearing loss and tinnitus
The problem summary
The viral infection (HCV) potentially leading to life-threatening liver damage
There is only one approved treatment (PEG-IFN-α/RBV) with a number of side effects
Only about 50% of infected people respond to treatment
It is not known who is gong to respond
Genome-wide association study
(GWAS) Among chronic HCV genotype 1 affected
individuals, treatment non-responders are taken as cases and responders as controls
Individuals are genotyped using a high throughput technology i.e. SNP Chips
Genotypes are assessed with respect to association with the case-control status
Expected GWAS outcomes
Identification of a genetic marker or a set of genetic markers truly associated with the phenotype
This can point to markers specific to cases (controls) and further assist with the case-control status prediction
The location of detected markers can point to specific genes and potentially reveal underlying biological mechanisms
Recent findings reported in the literature within a single month
Nature: online 16th of August, 2009
Nature Genetics online 13th of September, 2009
Comparison of the three GWA studies
Study Ancestry (sample size)
Genotyping platform
Case/Control Associated SNPs
Ge et al. Cauc/Afric/Hisp (N=1615)
Illumina 610-Quad
R/NR rs12979860 (OR=3.10)
Suppiah et al. Cauc (N=293) Illumina CNV370-Quad
R/NR rs8099917 (OR=1.98)
Tanaka et al. Jap (N=154) Affymetrix 6.0 NVR/VR rs8099917 (OR=12.10)
R/NR – sustained virological response/no sustained virological response NVR – null virological responders VR – viralogical responders: subject who respond to treatment, but do not necessary clear the virus)
Ge et al. and Suppiah et al. case-control split
Case NO-SVR
Control SVR
SVR – Sustained Virological Response: Absence of HCV RNA in blood 6 months after treatment
Tanaka et al. case-control split
Case NO-SVR
Control SVR
TVR – Transient Virological Response: Substantial reduction but not absence of HCV RNA in blood
Tanaka et al. case-control split
Case NVR
Control TVR+SVR
NVR – Null Virological Response: No reduction of HCV RNA in blood
The main common finding:
Source: Tanaka et al. Nature Genet
rs8099917
rs12980275
LD structure of IL28B genomic region
Source: Suppiah et al. Nature Genet
LD structure of IL28B genomic region
Source: Suppiah et al. Nature Genet
rs12979860
Ge et al. specific findings Rates of treatment response vary across
populations together with frequency of specific genotypes
Suppiah et al. specific findings Expression levels of IL28A and IL28B in
healthy individuals vary with genotype frequencies at rs8099917
Tanaka et al. specific findings
Alternative study design: NULL virological responders (cases) vs. virological responders (controls)
This more distinct phenotypical discordance leads to much higher odds ratios
IL28B Location: Chromosome 19, q13.13 (44426112
to 44427451: 1339 bp) Protein: interferon-λ3 (interleukin 28B) One of three genes (the other two are IL28A and
IL29) known as type III of λ interferons Type III interferons are shown to be unregulated
by viral infection and other interferons IFN-λ has effects similar to IFN-α, but more
selective (i.e. it can produce less side effects)
The gap between genetic information and medical innovation Size of GenBank database
The number of new SNPs
The number of new drug applications submitted to FDA
The number of new drugs approved by FDA
Source: Nature
Remaining challenges Precise identification of antiviral
mechanism (mostly biological challenge)
Accurate prediction of PEG-IFN-α/RBV treatment response
Pharmacogenomics
Composite discipline covering the range of applied biomedical research areas, from bioinformatics to molecular chemistry
Opens entirely new opportunities such as treatment personalisation and genomic information assisted clinical trials
Attractive from the clinical research point of view, as well as from the financial viability point of view (e.g. given $10bln size of HCV treatment market)
Identification of antiviral mechanism
This is achieved mainly through a series of biological experiments
Likely to lead to new treatments Even if the discovery process is successful, the
way to clinical practice acceptance can take years
Prediction of PEG-IFN-α/RBV treatment response
Types of relevant predictors:
Genomic information (SNPs, CNVs etc.) Clinical baseline factors (age, BMI, viral load
etc.) Unknown/unmeasurable environmental factors
e.g. stress-level
Approaches to building a clinically functional prediction model
More sensitive statistical significance testing procedures, e.g. efficiency robust tests (to identify additional association signals missed otherwise)
Identification of non-genetic predictive variables e.g. age, BMI etc. (account for confounding!)
Finite sample model selection procedure e.g. penalised regressions and cross-validation
Biologically motivated variable selection Out-of-sample validation
Work in progress
Bioinformatics, WEHI Melanie Bahlo
AGRF Rust Turakulov
Math and Stats, UniMelb Hugh Miller
MBS, UniMelb Chris Lloyd
Acknowledgments
Millenium Institute & Westmead Children’s Hospital, Sydney
Vijay Suppiah
David Booth
Jacob George
Funding
ARC Linkage Grant