integrative models of the hepatitis c virus infection: modeling wicked problems
DESCRIPTION
Integrative models of the hepatitis C virus infection: Modeling wicked problems. Presenter: James Lara, Ph.D. Centers for Disease Control and Prevention Division of Viral Hepatitis 1600 Clifton Road Atlanta, GA 30333 [email protected]. History of Epidemiology*. - PowerPoint PPT PresentationTRANSCRIPT
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Integrative models of the hepatitis C virus infection: Modeling wicked problems
Presenter:James Lara, Ph.D.Centers for Disease Control and PreventionDivision of Viral Hepatitis1600 Clifton RoadAtlanta, GA [email protected]
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History of Epidemiology*
* Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
John Snow Broadwick Street cholera outbreak, London 1854
Founding event for Computational Epidemiology. Ability to abstractly recognize a pattern without bias. Predicting the daily weather is easier than predicting disease. Public Health Science has greatly impacted life expectancy.
1990 20100
40
8037.5
67.248.3
78.2
Average Lifespan (years) ‡
Era
WorldwideUSA
‡ Sources: Am J Clin Nutr, 1992; 55: 1196S-1202S; and CIA World Factbook.
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History of CDC
Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
1942: Office of Malaria Control during WWII.
1947: CDC employees purchase campus from Emory for $10 with Robert Woodruff gift.
1957: Inclusion of STD prevention.
1960: Inclusion of TB prevention.
1963: Immunization program is established.
1980: Centers for Disease Control (CDC).
1992: Renamed to: Centers for Disease Control and Prevention.
2010: Total workforce of 15,000 ; 8,500 FTE’s ; FY $6.8B ; 50 states ; 45 countries
Source: www.cdc.gov
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CDC Organization Chart (2010)
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CDC ‘s primary goals: prevention of illness, disability, and death Model of long-term national productivity benefits from reduced daily intake of calories & sodium in the US.†
† Source: Dali et al., Am J Health Promot. 2009 Jul/Aug 23(6): 423-430.
Comorbidities increase probability of limitations that prevent work. The long-term benefit of reduced sodium intake is $108.5B. Facilitate planning by federal agencies. Help inform public health policy and the business case. For every $1 spent on wellness programs, the return is $4.56-$4.73*.
* Source: Ozminkowski et al., Am J Health Promot. 1999 Sep/Oct; 14(1): 31-43.
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Viral Hepatitis
Viral hepatitis is liver inflammation caused by viruses.
Viral hepatitis is the leading cause of liver cancer and the most common reason for liver transplantation.
Specific hepatitis viruses have been labeled A, B, C, D, E, F, and G.
The most common types are Hepatitis A, Hepatitis B, and Hepatitis C.
Hepatitis C is the major cause of chronic liver disease and cirrhosis in the US.
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Viral Hepatitis C
Viral hepatitis C is caused by infection with the hepatitis C virus (HCV).
Clinical manifestation: acute and chronic.
Six HCV genotypes (1–6).
Evolves as quasispecies (QS).
Combinatorial therapeutic treatment: interferon and ribavirin.
Treatment efficacy varies by HCV genotype and patient’s tolerance.
No vaccine is available for Hepatitis C.
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RNA genome: ~9,600 bases Polyprotein: 3011 amino-acids
Mechanisms of HCV infection persistence are not well understood:
Insufficient immune response Virus – host interactions High genetic variability
Hepatitis C Virus (HCV)
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Hepatitis C virus (HCV) infection is the most common chronic bloodbourne infection and a major public health problem in the US
2002 2003 2004 2005 2006 20070
5,000
10,000
15,000
20,000
25,000
30,000
1223
891
758
694
802
849
4,80
0
4,50
0
4,20
0
3,40
0
3,20
0
2,80
0
29,0
00
17,0
00
No. acute clinical cases
Est. No. acute clinical cases
Est. No. new infections
Disease Burden from HCV in the US (2002-2007)*
No. of chronically infected persons: 2.7 – 3.9 millionAnnual No. of chronic liver disease deaths: 12,000
*http://www.cdc.gov/hepatitis/HCV/StatisticsHCV.htm
>15 15–39 40–59 >600.0%
10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%
100.0%
Died from hepatitisHospitalized for hepatitisHad jaundice
Age group
Perc
enta
ge
Clinical characteristics of acute HCV (2007)*
Chronic infection develops in 70%-85% of HCV-infected persons; 60%-70% of chronically infected persons have evidence of active liver disease
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Intravenous drug use (IDU) and multiple sex partners are the major risk factors associated to HCV infection
Trends in epidemiology among patients with acute HCV in the US (2001-2007)*
*http://www.cdc.gov/hepatitis/HCV/StatisticsHCV.htm
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Distribution of genotypes according demographic trends among chronically HCV-infected patients in the US (1988-1994)†*
†Weighted percentages by genotype; ‡Weighted Geometric mean concentrations(GMC); *In: O.V. Nainan et. al. Gastroenterology 2006; 131:478-484
6_29 30_39 40_49 50_59 >600%
20%40%60%80%
100%
AGE
Genotype 1Genotype 2Genotype 3
Male Female0%
20%
40%
60%
80%
100%78.4%
68.8%
GENDERCaucasian Afro-AM Mex-AM
0%
20%
40%
60%
80%
100%
69.9%
90.9%
71.2%
ETHNICITY
1 1a 1b 2 & 30
0.51
1.52
2.5 2.1 2.31.8 1.9
GENOTYPEWei
ghte
d G
MC
(IU/m
l) x
1E+6
HCV RNA concentrations among chronically infected patients by genotype and demographic characteristics (1988-1994)‡*
<40 ≥40 Male Female Caucasian Afro-AM Mex-AM0
1
2
3
4
1.4
3.3
2.2 1.9 2.12.6
1
AGE GENDER ETHNICITY
Wei
ghte
d G
MC
(IU/m
l) x
1E+6
Clinical prognosis and treatment outcome of HCV infection has dependencies to many viral and host factors.
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Integrative Molecular Epidemiology Concept
Viral factors:Pylogenetics, mutation rates,
molecular determinants, genotype, etc.
Host factors:Immunological,
demographical, genetic, and other risk factors
Historical approachIntegrative Epidemiology
HCV infection:Pathogenicity, virulence, clinical outcome, therapy
response, etc.
Linkage
Linkage Assessment of risk factors.
Linkage
VIRUS(Interac-
tions)
SARs
Genome
Quasi-species
Integration of risk factors for outcome prediction
HOST(Interac-
tions)
Genetic
Demo-graphical
Immuno-logical
HCV infection:Predisposition, susceptibility, prognosis, therapy outcome.
Ultimate goal: Accurate quantitative models for outcome prediction
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Viral factors:Pylogenetics, mutation rates,
molecular determinants, genotype, etc.
Host factors:Immunological,
demographical, genetic, and other risk factors
Historical approach
HCV infection:Pathogenicity, virulence, clinical outcome, therapy
response, etc.
Linkage
Linkage Assessment of risk factors.
Linkage
Accounts for trends within a population.
Does not take into account: genetic variability of individuals within a population genetic variability of viral strains within an individual
Unsuitable for individual outcome prediction How will a patient respond to a medication?
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Towards individualized & tailored care and prevention
Integrative Epidemiology
VIRUS(Interac-
tions)
SARs
Genome
Quasi-species
Integration of risk factors for outcome prediction
HOST(Interac-
tions)
Genetic
Demo-graphical
Immuno-logical
HCV infection:Predisposition, susceptibility, prognosis, therapy outcome.
Take into account: genetic variability of an individual within a population genetic variability of viral strains within an individual
Take advantage of high throughput technologies (molecular profiling, proteomics, genetic testing, etc).
Suitable for outcome prediction. The right treatment for the right person at the right time.
Required for effective public health intervention (disease eradication).
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Public Health Intervention: “A double edge sword”
1910’s: Massive vaccination to eradicate sleeping disorder (using 5 syringes). 1966: Programme to eradicate smallpox began in West and Central Africa (using jet injectors). 1970: last case of smallpox is reported. 1966–1772: >28M children (1–6 yr’s of age) received measles vaccination. 1997: The use of jet injectors is stopped. 2010: Models indicate that prevalence of HBV genotype E is due to interventions.
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Public Health Intervention: “A double edge sword”
Egypt has the highest prevalence of HCV in the world. Has the highest morbidity and mortality from chronic liver disease, cirrhosis and hepatocellular carcinoma. High degree of homogeneity of HCV subtypes (4a) probably due to vaccination intervention.
Source: World Health Organization (WHO).
Schistosomiasis life cycle
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Intervention may lead to the selection of more resistant and virulent strains.
Unproportional decreases in incidence and deaths.
Increase in the morbidity and mortality of the disease.
Accurate models (e.g. probabilistic models): estimate long-term effects of intervention on disease burden, and design of optimal strategies for eradication.
Public Health Intervention: “A double edge sword”
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Assessing relationships from a copious amount of features: “curse of dimensionality”.
Modeling HCV virulence, susceptibilities to various factors and predispositions to infection or therapy failure is difficult because:
Underlying mechanisms of are not understood.
Discrepancy among experts. Changes with time.
Modeling HCV Infection
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Genome Sequencing
Genome Assembly
Comparative Genomics
Molecular Evolution
Molecular Evolution of Pathogenicity (study evolutionary changes) Total Viral Population Analysis (disease and outbreak surveillance) Genome Data Mining (factors of virulence) Discovery of new hepatitis viruses Biomarker Discovery (polymorphisms of therapy resistance)
Genome Sequencing for Public Health
Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
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50000 100000 150000 200000
50000 100000 150000 200000
Whole Serum
Fraction 1
Fraction 2
Fraction 3
Fraction 4
Fraction 5
Fraction 6
Viral RNA Mass Spectrometry
Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
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Genome sequencing of HCV virus results in high data generation and special computing requirements
HPC ( High Performance Computing): Systems comprising of very fast resources, typically 100’s or 1000’s of processors, and very fast memory, network, and storage.
Computational Science: Science done by computations rather than by theory and experiment alone, which typically requires HPC resources.
Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
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Requirements for coherent integrative computational epidemiology
Science: (Theory; Experiment) Metrics, data collection, analysis.
Computational Science: (Algorithms) Performing science computationally. Matching the algorithm to the computer architecture.
Computer Science: (O/S, Programming) How to accelerate computational science. How to reduce barriers of parallelization.
Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)
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THE HCV GENOME: IN SEARCH OF EPISTATIC INTERRELATIONSHIPS
Study example:
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Coordinated Evolution of HCV
The complex network of coordinated substitutions is an emergent property of genetic systems with implications for evolution, vaccine research, and drug development.
Such properties as polymorphism or strength of selection, the epistatic connectivity mapped in the network is important for typing individual sites, proteins, or entire genetic systems.
Help devise molecular intervention strategies for disrupting viral functions or impeding compensatory changes for vaccine escape or drug resistance mutations.
May be used to find new therapeutic targets, as suggested in this study for the NS4A protein, which plays an important role in the network.
Source: David Campo et. al. PNAS 2008, 105(28): 9685-9690.
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Coordinated Evolution of HCV
An algorithm for addressing coordinated mutations that evolve with HCV were developed in MatLab (Zoya Dimitrova). Using multiple computational architectures to find optimal solution. Challenge: Having a library of parallelized algorithms for the right computer architecture.
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LINKING HEPATITIS C VIRUS QUASISPECIES GENETIC DIVERSITY TO FEATURES OF VIRAL INFECTION
Study example:
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Genomic StructureNumber of quasi-
species (NQS)
HCV SEQUENCE HOST
Viral titer (VT)
Selection(dN/dS)
HCV SEQUENCE HOST
Sequence of HCV HVR1 quasispecies is linked to virological factors
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Bayesian Network Model Linking Sequences of HCV HVR1 Quasispecies to Viral Parameters
Sequence of HCV HVR1 quasispecies is linked to virological factors
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Evaluation of Models
Target classes
10-fold-CV ‡
(%) Acc.randTest †
(10-fold-CV ‡)
Genotype 99.9% 0.3286
dN/dS^^ (3-bin) (2-bin)
94.4%92.2%
0.4020 0.5120
NQSaa 88.0% 0.3887
NQSnt 87.7% 0.3978
Viral Titer 97.2% 0.6031‡ Avg. accuracies† Random assignment of class labels^^ Based on dNdS 3 class or 2 class grouping
Predictions: Classification Modeling
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Validation of Models
Target classes
10-fold-CV ‡
(%) Acc.TestSet**
Genotype 99.9% 100%
dN/dS^^ (3-bin) (2-bin)
94.4%92.2%
70.3%82.7%
NQSaa 88.0% 70.3%
NQSnt 87.7% 72.4%
Viral Titer 97.2% 52.40%‡ Avg. accuracies† Random assignment of class labels** 10 NHANES-3 patients; 5M and 5F; Genotypes 1a and 1b; 185nt/96aa HVR1 QS^^ Based on dNdS 3 class or 2 class grouping
Predictions: Classification Modeling
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PREDICTIVE MODELS OF DRUG THERAPY OUTCOMES
Study example:
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Coevolution among Genomic Sites of the Hepatitis C Virus during Interferon–Ribavirin Therapy
Only 50% of chronically HCV infected patients demonstrate sustained virological response (SVR) to interferon/ribavirin therapy.
Patients who do not achieve SVR show complete absence of response (NR) or unsustainable response (UR).
UR presents in two forms: patients who relapse (R), and patients who breakthrough (BT).
BT is a special case where drug resistance evolves during treatment.
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Coevolution among Genomic Sites of the Hepatitis C Virus during Interferon–Ribavirin Therapy
CORE E1 E2 P7NS2 NS3
NS4A
NS4B
NS5A
NS5B
0
2
4
6
8
10
12
Importance of the probabilistic relationships between HCV proteins and therapy outcome
Total Forces
Linear Projections of Physicochemical Properties
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Therapy outcome prediction
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Features of HCV infection are imprinted in the viral genome.
Classifier Evaluation Validation (% accuracy)Overall NR class BT class
DTNB 97.5† 72.2 75.0 66.7Linear Projection 95.2* 83.3 83.3 83.3
NS5A model
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Beth Israel Deaconess Medical Center collaboration: Deep sequencing of HCV 1a QS sequences Approx. 13-15 samples/pat., collected over a time span of 48 hrs 10,000-25,000 sequence reads/sample
Ongoing research related to therapy outcome
Atlanta Medical Center collaboration: Deep sequencing of HCV 1a variants Approx. 15-20 samples/patient during & after treatment 5,000-10,000 sequence reads/sample
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Case Study – Hepatitis C VirusContinuing challenges to support prevention and control of HCV
454 sequencing and alignment of hundreds of thousands (>400,000) sequence variants using exact or heuristic algorithms requires high performance computing.
3D structure templates are not available for rational design of peptides and proteins to aid in development of diagnostics.
Compute bound Bayesian networks for Molecular epidemiological studies.
New computational technologies, services and development/application of faster algorithms will be necessary in the very near future to analyze and process these huge amounts of data.
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Three BN models graphically describes above model
Lets say: A & C are dependent on each other regardless of B and/or D.C & D are dependent on each other regardless of A and/or B.
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Disclaimer
"The findings and conclusions in this presentation have not been formally disseminated by [the Centers for
Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry] and should not be construed to represent any agency determination or
policy."
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Acknowledgements
Division of Viral HepatitisBioinformatics and Molecular Epidemiology Laboratory-David Campo-Zoya Dimitrova-Mike Purdy-Guoliang Xia-Gilberto Vaughan-Sumathi Ramachandran-Lydia Ganova-Raeva-Joseph Forbi -Hong Thai-Yulin Lin-Livia Rossi-Johnny Yokosawa-YURY KHUDYAKOV
CDCIT Research & Development-Christopher A. Lynberg
CDCDSR/BCFB Scientific Computing Activity-Elizabeth B. Neuhaus
Corporate R&D-Accelereyes-NVIDIA
Collaborators-Atlanta Medical Center, Georgia, USA -Beth Israel Deaconess Medical Center, Boston, USA-Saint Louis University School of Medicine, Missouri, USA-UT Southwestern Medical Center, TX, USA
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QUESTIONS?