1. 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 jlara@cdc.gov

2. 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.  Worldwide  USA ‡ Sources: Am J Clin Nutr, 1992; 55: 1196S-1202S; and CIA World Factbook. 2

3. 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 3

4. CDC Organization Chart (2010) 4

5. 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. 5

6. 6 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.

7. 7 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.

8. 8  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)

9. Hepatitis C virus (HCV) infection is the most common chronic bloodbourne infection and a major public health problem in the US Disease Burden from HCV in the US (2002-2007)* No. of chronically infected persons: 2.7 – 3.9 million Annual No. of chronic liver disease deaths: 12,000 *http://www.cdc.gov/hepatitis/HCV/StatisticsHCV.htm 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 9

10. 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 10

11. 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  Genotype 1  Genotype 2  Genotype 3 HCV RNA concentrations among chronically infected patients by genotype and demographic characteristics (1988-1994)‡* Clinical prognosis and treatment outcome of HCV infection has dependencies to many viral and host factors. 11

12. Integrative Molecular Epidemiology Concept Viral factors: Pylogenetics, mutation rates, molecular determinants, genotype, etc. Host factors: Immunological, demographical, genetic, and other risk factors  Historical approach  Integrative Epidemiology HCV infection: Pathogenicity, virulence, clinical outcome, therapy response, etc. Linkage Assessment of risk factors. Linkage VIRUS (Interac- tions) SARs Genome Quasi- species Integration of risk factors for outcome prediction Demo- graphical Immuno- logical HCV infection: Predisposition, susceptibility, prognosis, therapy outcome.  Ultimate goal: Accurate quantitative models for outcome prediction 12

13. 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 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? 13

14. Towards individualized & tailored care and prevention  Integrative Epidemiology VIRUS (Interac- tions) SARs Genome Quasi- species Integration of risk factors for outcome prediction 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). 14

15. 15 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.

16. 16 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

17. 17  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”

18. 18  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

19. 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 19 Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission)

20. Viral RNA Mass Spectrometry Chris Lynberg; www.ipdps.org/ipdps2010/ipdps2010-slides/ipdps-presentations.org (with permission) 20

21. 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) 21

22. 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) 22

23. THE HCV GENOME: IN SEARCH OF EPISTATIC INTERRELATIONSHIPS Study example: 23

24. 24 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.

25. 25 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.

26. LINKING HEPATITIS C VIRUS QUASISPECIES GENETIC DIVERSITY TO FEATURES OF VIRAL INFECTION Study example: 26

27. Genomic Structure Number 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 27

28. Bayesian Network Model Linking Sequences of HCV HVR1 Quasispecies to Viral Parameters Sequence of HCV HVR1 quasispecies is linked to virological factors 28

29. Evaluation of Models Target classes 10-fold-CV ‡ (%) Acc. randTest † (10-fold-CV ‡ ) Genotype99.9%0.3286 dN/dS ^^ (3-bin) (2-bin) (2-bin)94.4%92.2%0.40200.5120 NQSaa88.0%0.3887 NQSnt87.7%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 29

30. Validation of Models Target classes 10-fold-CV ‡ (%) Acc. TestSet** Genotype99.9%100% dN/dS ^^ (3-bin) (2-bin) (2-bin)94.4%92.2%70.3%82.7% NQSaa88.0%70.3% NQSnt87.7%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 30

31. PREDICTIVE MODELS OF DRUG THERAPY OUTCOMES Study example: 31

32. 32 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.

33. 33 Coevolution among Genomic Sites of the Hepatitis C Virus during Interferon–Ribavirin Therapy Linear Projections of Physicochemical Properties

34. 34 Therapy outcome prediction

35. 35 Features of HCV infection are imprinted in the viral genome. ClassifierEvaluation Validation (% accuracy) Overall NR class BT class DTNB97.5 † 72.275.066.7 Linear Projection95.2*83.3 NS5A model

36. 36  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

37. Case Study – Hepatitis C Virus Continuing 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. 37

38. 38 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.

39. 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."

40. Acknowledgements Division of Viral Hepatitis Bioinformatics 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 CDC IT Research & Development -Christopher A. Lynberg CDC DSR/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

41. 41 QUESTIONS?