Phambili Sieve Analysis

1. Phambili Sieve Analysis Tomer Hertz Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center

2. Acknowledgements Dept Microbiology, University of Washington Brendan B. Larsen Hong Zhao Jill Stoddard Philip Konopa SnehalNariya Airin Lam James I. Mullins Vaccine and Infectious Disease Institute, Fred Huthcinson Cancer Research Center Paul T. Edlefsen, Allan DeCamp Craig Magaret Hasan Ahmed Michal Juraska Youyi Fong Nicole Frahm John Hural Lawrence Corey James Kublin Juliana McElrath Peter Gilbert Clinical Trial sites Glenda Gray Gavin Churchyard Linda-Gail Bekker MopashaneNchabeleng KolekaMlisana Div Medical Virology University of Cape Town Carolyn Williamson Murray Logan Cecelia Rademeyer Jinny Marais RuwayhidaThebus FloretteTreurnicht NobubeloNgandu US Military HIV Research Program Morgane Rolland

3. Two Types of Potential Selective Effectsfor a T-Cell Based Vaccine • Acquisition Sieve Effect • The vaccine selectively blocks (or enhances) acquisition with specific HIV variants • Post-Infection Selective Effect • The vaccine drives HIV sequence evolution • Longitudinal HIV sequences are needed to distinguish these two types of effects • Currently we only have one time-point per subject

4. Sieve Analysis Framework Goal: Compare sequences of breakthrough infections of vaccine and placebo recipients and search for statistical evidence of a vaccine sieve effect Alternative formulation – learn to predict treatment assignment using labeled sequence data as training data

5. Sieve Analysis methods • Two types of approaches for comparing insert and breakthrough sequences: • Local (site-specific): Evaluate individual site and sets of sites (K-mers) separately for evidence of specific vaccine induced signatures in breakthrough sequences Pros: Localization – can point to specific signature sites in which there is statistical evidence of vaccine-induced effects Cons: Loss of statistical power due to multiplicity correction • Global: Summarize overall ‘distance’ with a single number Pros: statistical power - subjects with different HLA can all contribute signal Cons: Cannot point to specific sites in which sieve effects take place

6. Sieve Analysis Framework Notations: • Sinsert– Vaccine insert sequence (e.g. Gag, Pol, Env) • Spbreakthrough – p’thparticipant’s breakthrough infection sequence • d(Sinsert,Spbreakthrough)– Global distance between p’th participant’s breakthrough infection sequence and the vaccine insert • d(Siinsert,Sp,ibreakthrough) – local distance between the amino acid at site i of the p’th participant’s breakthrough infection and the vaccine insert sequence • Dvac= { p єVaccine recipients: d(Sinsert,Spbreakthrough) } • Dpla= { p єPlacebo recipients: d(Sinsert,Spbreakthrough) }

7. Global Sieve Analysis Framework • For each vaccine insert Sinsert • For each study participant p • Computed(Sinsert,Spbreakthrough) • Define H0 : {Dvac == Dpla} H1 : {Dvac != Dpla} • Test if H0 can be rejected with p < 0.05 (q-value < 0.2) • Sequences are first aligned and translated into amino acids • Comparisons can be done with one sequence per individual, or using multiple sequences per individual

8. Local Sieve Analysis Framework • For each vaccine insert Sinsert • For each position i • For each study participant p • Computed(Siinsert,Sp,ibreakthrough) • Define H0 : {Dvac == Dpla} H1 : {Dvac != Dpla} • Test if H0 can be rejected with p < 0.05 (q-value < 0.2) • Sequences are first aligned and translated into amino acids • Comparisons can be done with one sequence per individual, or using multiple sequences

9. Maximizing statistical power • Achieving high statistical power requires: • Large n - # of infected subjects with sequence data nVaxgen = 336 nStep = 66 nPhambili = 43 nRV144 = 121 • Therefore current sieve analyses can only detect relatively large sieve effects

10. Maximizing statistical power • Compare sequences to the vaccine insert • Pre-filter based on treatment-blinded data • Fewer analyses  greater power • Focus analysis on relevant subsequences • Epitopes: CTL epitopes differ by subject’s HLA type • Variability, accessibility masks • Plan ahead

11. Example: Step - Positions 3, 6, 8 in SLYNTVATL (Gag 77-85) • Iversen et al. (2006, Nat Immun, 7:179-189) found that, for A*02 individuals, SYLNTVATL often acquires CTL escape mutations at positions 3, 6, and 8 • For all 29 A*02 infected subjects, Gag 77-85 in their majority consensus sequence is a known or predicted epitope (w/ prob >.8) • Gag 77-85: Mean distance to StepVx: Numbers of A*02 Subjects with StepVx AA or Mismatch (% Mismatch) PlaceboVaccine 0.089 0.273

12. SCHARP’s 503 Sieve Analysis PlanLocal Sieve Analysis • Hypothesis Testing • Site-specific hypothesis testing • Hamming distance to insert • Model-based (Bayesian) • K-mer-specific hypothesis testing • Physio-chemical properties of K-mers • Classification • K-mer classification • Physio-chemical properties of K-mers All of these analyses are conducted on aligned amino acid sequences (or their properties)

13. SCHARP’s 503 Sieve Analysis PlanGlobal Sieve Analysis • Hypothesis Testing • T-cell predicted epitope distance-to-insert • % mismatch (Hamming distance) • Predicted escape mutations • Classification accuracy • K-mer based features using physio-chemical properties of AAs

14. Step Results: AA Site Scanning Phambili Results: AA Site Scanning

15. Step Results: AA Site Scanning by Physicochemical Properties Phambili Results: AA Site Scanning by Physicochemical Properties Conducted by Craig A. Magaret

16. Global Hypothesis Testing ApproachPercentEpitope Mismatchconducted by AllanDeCamp • Epitope-based distance: • For each participant: • Identify all predicted epitopes (9mers on Vx insert based on HLA alleles • Compute the % of epitopes in which at least one mismatch exists between the Vx insert and the breakthrough sequence (Hamming distance > 0) • HLA predictions obtained using netMHC (Step), netMHCpan (Phambili) and EpiPred (Step,Phambili)

17. Global hypothesistestingapproachPercent Epitope Mismatch- Step results conducted by AllanDeCamp Gag Nef Pol Rolland et al. Nature Medicine 2011

18. Global Hypothesis Testing ApproachPercentEpitope Mismatch- 503 results conducted by AllanDeCamp

19. Global Hypothesis Testing ApproachPredictedEpitope Escape Distancesconducted by Tomer Hertz • HLA mediated escape – reducing the binding affinity of a vaccine-induced epitope response • Cleavage mediated escape – 2 alternatives: • Introducing novel cleavage sites in existing epitopes • Reducing the cleavage propensity on the C-terminus of exisitingepitopes • Indel mediated escape – eliminate responses by introducing an insertion or deletion within existing epitopes

20. Global Hypothesis Testing ApproachPredictedEpitope Escape Distancesconducted by Tomer Hertz • HLA mediated escape: • For each participant: • Identify all predicted epitopes (9mers/10mers) on Vx insert sequence based on participants HLA alleles • Compute binding affinities of breakthrough 9mers/10mers matching predicted insert epitopes • Count number of cases were mutations on breakthrough peptides are predicted to reduce binding affinity in a significant manner (>0.5 difference in the log(IC50) values)

21. Global Hypothesis Testing ApproachPredictedEpitope Escape Distancesconducted by Tomer Hertz Step Phambili

22. Global Hypothesis Testing Approach Step - PredictedCleavage Escape Distancesconducted by Tomer Hertz

23. Sieve Results Summary

24. Conclusions • Evidence for MRKAd5 sieve effects were previously demonstrated in the Step trial • Our results demonstrate much weaker MRKAd5 sieve effects in the Phambili trial, which may be explained by • Few infection endpoints and thus limited statistical power • Incomplete vaccination courses • Use of a clade B immunogen in a predominantly clade C region

25. ELISpot Response Rates for Clade B and Clade C PTE peptides pools (day 56) Gray et al., 2011