integrative models of the hepatitis c virus infection: modeling wicked problems

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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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.


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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


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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


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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.


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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.


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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

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.

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."

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?

integrative models of the hepatitis c virus infection: modeling wicked problems

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