Published on December 10, 2013
HCV research: Recent findings and future challenges Max Moldovan Bioinformatics Division, WEHI firstname.lastname@example.org Bioinformatics Seminar September 28, 2009
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
HCV infection by genotype
HCV infection by genotype Genotype 1: 77%
HCV genotype 1 progression HCV infection Clearance (~20%) Source: Based on NIH information Chronic HCV (~80%) No treatment response (~50%) Treatment response (~50%)
Approved treatment Pegylated interferon-α plus ribavirin (PEGIFN-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 PEGIFN-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 PEGIFN-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 (PEGIFN-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 casecontrol 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) Ge et al. Genotyping platform Case/Control Associated SNPs Cauc/Afric/Hisp Illumina 610(N=1615) Quad R/NR rs12979860 (OR=3.10) Suppiah et al. Cauc (N=293) Illumina CNV370Quad 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 Control NO-SVR SVR SVR – Sustained Virological Response: Absence of HCV RNA in blood 6 months after treatment
Tanaka et al. case-control split Case Control NO-SVR 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: rs8099917 rs12980275 Source: Tanaka et al. Nature Genet
LD structure of IL28B genomic region Source: Suppiah et al. Nature Genet
LD structure of IL28B genomic region rs12979860 Source: Suppiah et al. Nature Genet
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
Acknowledgments Bioinformatics, WEHI Melanie Bahlo Millenium Institute & Westmead Children’s Hospital, Sydney AGRF Vijay Suppiah Rust Turakulov David Booth Jacob George Math and Stats, UniMelb Hugh Miller Funding MBS, UniMelb Chris Lloyd ARC Linkage Grant
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