Use of correlates of immunity in vaccinology Adam Penn-Nicholson South African Tuberculosis Vaccine Initiative 11 th November 2014 10th Annual African Vaccinology Course (AAVC): Developing Vaccinology Expertise for Africa
What is the point of vaccination? • Protection from disease • Immunogenicity ≠ protection
Characteristics of Different Types of Vaccines • Live attenuated • Whole Killed / Inactivated • Subunit – Recombinant protein, peptide – Polysaccharid e • Viral Vector • DNA Vaccines
Currently licensed vaccines Vaccines Vaccine type Serum IgG Mucosal IgG Mucosal IgA T cells Diphtheria toxoid Toxoid ++ (+) Hepatitis A Killed ++ Hepatitis B (HBsAg) Protein ++ Hib PS PS ++ (+) Hib glycoconjugates PS-protein ++ ++ Influenza Killed, subunit ++ (+) Influenza intranasal Live attenuated ++ + + + (CD8+) Japanese encephalitis Killed ++ Measles Live attenuated ++ + (CD8+) Meningococcal PS PS ++ (+) Meningococcal conjugates PS-protein ++ ++ Mumps Live attenuated ++ Papillomavirus (human) VLPs ++ ++ Pertussis, whole cell Killed ++ Pertussis, acellular Protein ++ +?(CD4+) Pneumococcal PS PS ++ (+) Pneumococcal conjugates PS-protein ++ ++ Polio Sabin Live attenuated ++ ++ ++ Polio Salk Killed ++ + Rabies Killed ++ Rotavirus VLPs (+) (+) ++ Rubella Live attenuated ++ Tetanus toxoid Toxoid ++ Tuberculosis (BCG) Live mycobacteria ++(CD4+) Typhoid PS PS + (+) Varicella (chickenpox) Live attenuated ++ +?(CD4+) Varicella (zoster) Live attenuated ++(CD4+) Yellow fever Live attenuated ++ Modified from Vaccines (6 th Ed.), Plotkin
Evolution of vaccine development Rappuoli R, Aderem A. A 2020 vision for vaccines against HIV, tuberculosis and malaria. Nature 2011;473:463–469.
Technologies for vaccine development Rappuoli R, Mandl CW, Black S, De Gregorio E. Vaccines for the twenty-first century society. Nat Rev Immunol 2011;11:865–872.
Correlates of Vaccine Induced Immunity Vaccines Vaccine type Serum IgG Mucosal IgG Mucosal IgA T cells Diphtheria toxoid Toxoid ++ (+) Hepatitis A Killed ++ Hepatitis B (HBsAg) Protein ++ Hib PS PS ++ (+) Hib glycoconjugates PS-protein ++ ++ Influenza Killed, subunit ++ (+) Influenza intranasal Live attenuated ++ + + + (CD8+) Japanese encephalitis Killed ++ Measles Live attenuated ++ + (CD8+) Meningococcal PS PS ++ (+) Meningococcal conjugates PS-protein ++ ++ Ab response accounts Mumps Live attenuated ++ Papillomavirus (human) VLPs ++ ++ for protection elicited by Pertussis, whole cell Killed ++ most current vaccines Pertussis, acellular Protein ++ +?(CD4+) Pneumococcal PS PS ++ (+) Pneumococcal conjugates PS-protein ++ ++ Polio Sabin Live attenuated ++ ++ ++ Polio Salk Killed ++ + Rabies Killed ++ Rotavirus VLPs (+) (+) ++ Rubella Live attenuated ++ Tetanus toxoid Toxoid ++ Tuberculosis (BCG) Live mycobacteria ++(CD4+) Typhoid PS PS + (+) Varicella (chickenpox) Live attenuated ++ +?(CD4+) Varicella (zoster) Live attenuated ++(CD4+) Yellow fever Live attenuated ++ Modified from Vaccines (6 th Ed.), Plotkin
Correlates of Protective Immunity Immunity Rappuoli R, Aderem A. A 2020 vision for vaccines against HIV, tuberculosis and malaria. Nature 2011;473:463–469.
Passive Vaccine A preparation of antibodies that neutralizes a pathogen and is administered before or around the time of known or potential exposure.
Not all antibodies are equal Plotkin SA. Vaccines: Correlates of Vaccine ‐ Induced Immunity. Clinical Infectious Diseases 2008;47:401–409.
Yellow Fever Vaccine YF-17D The gold standard of vaccinology
gp120 trimeric structure View from target membrane perspective Adapted from Wyatt et al . NATURE VOL 393 18 JUNE 1998 pp. 705-711
Broadly Neutralizing Antibody Development Limitations: Most antibodies elicited are non-neutralizing Monomeric gp120 or strain-specific Inner domain Outer domain From Wyatt et al. NATURE VOL 393 18 JUNE 1998 pp. 705-711
HIV Phase III Vaccine Trial RV144 31% protective efficacy
Correlates of protection: Non-neutralizing V1/V2 binding Abs and ADCC
Systems Immunology Li S, Nakaya HI, Kazmin DA, Oh JZ, Pulendran B. Systems biological approaches to measure and understand vaccine immunity in humans. Semin Immunol 2013;25:209–218.
Need the right model
Need the right model
Need the right model
Need the right model
Mycobacterium tuberculosis (M.tb)
1/3 of global population is estimated to be M.tb infected 9 million develop TB disease / year 1.5 million deaths / year M.tb M.tb infected 90% latent uninfected infection 25-50% of exposed 10% active individuals disease What distinguishes M.tb infected adolescents who progress to TB disease from the M.tb infected adolescents who do not?
M.tb containment in a granuloma
Holes in the lungs is exactly what M.tb needs to spread
Key transition stages of the TB timeline TB Exposure Exposure diagnosis Reinfection TB TB M.tb infection TB disease treatment Relapse 1 3 4 2 Cure Risk of M.tb Risk of TB Treatment Risk of infection disease success recurrent TB
Validated biomarkers of protection against TB 0
G Poste. Nature 2011;459:156
Determinants of progression from infection to TB disease are unknown • Classical Th1 responses do not associate with risk of TB disease. What distinguishes M.tb infected individuals who progress to TB disease from M.tb infected individuals who do not? IL-2 IL-17 IFN- γ TNF- α
T cells are important
Different Approaches to TB Disease Biomarker Discovery Unbiased Biomarker discovery Hypothesis- driven “Validation” of candidates
Systems-level analyses have compared active TB to LTBI
Predictive Gene Expression Signatures • Goal : Delineate and validate signatures of risk of TB disease following natural infection with M.tb. • Biomarkers for TB disease would: - Facilitate screening of new TB vaccines - Facilitate treatment to prevent disease - Stimulate development of new vaccines and drugs - Guide approaches to identify correlates of protection
Adolescent Cohort Study 11 high schools in Worcester and surrounding towns 6,363 adolescents (age 12-18) enrolled 87 adolescents developed TB disease during the ACS 46 “per protocol” TB cases met inclusion criteria
Study design 364 PAXgene whole 46 Cases Sample Collection blood samples over 2 107 years of follow up Controls 2 years All enrolled participants are healthy, M.tb -infected Adolescents enrolled adolescents No TB Disease (Controls) HIV negative TST+/QFN+ Carefully matched : TB Disease (Cases) Age, gender, ethnicity, school, prior TST+/QFN+, episode of TB Microbiological confirmation + > 6 months Controls Cases RNA-Seq + qRT-PCR Training Set Test Set
2340 genes significant Red = Higher expression Blue = Lower expression
Network visualization (Gene-level PCR classifier) Examples of samples predicted case: case > 50% classifier votes Control Case
Simple PCR Test for risk of TB Assays Fluidigm Dynamic Array System 96 x 96 = 9216 reactions Samples per plate
Correlate of risk of TB disease helps us limit the number of participants recruited into trials Clinical Model Sensitivity (%) Specificity (%) Enrichment PSVM1 70 61 1.79 PSVM2 43 89 3.91 Combined PSVM 38 89 3.45 Combined PR 52 80 2.60
THE BIOLOGY - Predicted innate immune response network associated with TB disease progression Interactions Colors: Progressors vs. Non-progressors (0.5-0yrs before diagnosis)
Systems Vaccinology Pulendran B. Systems vaccinology: probing humanity's diverse immune systems with vaccines. Proc Natl Acad Sci USA 2014;111:12300–12306.
Pulendran B. Systems vaccinology: probing humanity's diverse immune systems with vaccines. Proc Natl Acad Sci USA 2014;111:12300–12306.
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