Sieve analysis of the Step trial: Evidence for vaccine-induced antigenic pressure on HIV

1. Sieve analysis of the Step trial:   Evidence for vaccine-induced antigenic pressure on HIV Allan deCamp and Peter Gilbert Statistical Center for HIV/AIDS Research and Prevention Fred Hutchinson Cancer Research Center

2. Conclusions of the Sieve Analysis • The analysis of HIV sequences shows significant differences (vaccine vs placebo) in T cell epitope regions, suggesting that the vaccine induced immune responses that in turn put antigenic pressure on the virus • The analysis of post-infection T cell responses shows significant anamnestic responses to MRKAd5 proteins deriving from vaccination and subsequent infection • The analysis of acute VL data suggests that the vaccine transiently and modestly suppressed acute VL, which may have been caused by these anamnestic responses

3. Two Types of Potential Selective Effects for 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 • But at the moment we only have one time-point per subject

4. What is Sieve Analysis? Assess the genetics of the HIVs that infected the trial participantsAre the viruses different depending on whether a subject got vaccine or placebo?

5. Sieve Analysis Plan • Compare a subject’s sequences to the MRKAd5 insert sequence in 2 ways: • Local: Evaluate each site and sets of sites separately (i.e., ‘antigen scanning’) • Global: Summarize overall protein distance with a single number • Results shown by Morgane Rolland were Global

6. Local Sieve Analysis: Methods and Results

7. Antigen Scanning • Test each amino acid (AA) site as a signature site: • Signature site is a site where the frequency of AA mismatch to the MRKAd5 AA differs vaccine vs placebo • Several statistical methods applied* MRKAd5 sequence Vaccinee observed sequences Placebo observed sequences *Including Gilbert, Wu, Jobes (2008, Biometrics)

8. AA Site Scanning: Departures From 0 Indicates Signature* *Site where the frequency of AA mismatches to the MRKAd5 AA differs in vaccine vs placebo sequences (q-value < 0.20) 8

9. Describe the ‘strongest signature’: Gag 84

10. In several A-list epitopes including position 8 in SLYNTVATL A*0201 A*0202 A*0205 LANL B (N=324): 65% T 34% V

11. Gag 84 by A-List Epitope-Restricting Alleles P-value 44% 56% 17% V 79% V Other A-List Other A-List Placebo Vaccine

12. Additional Signature Sites

13. 5 of 6 Vacc w/ D potentially have an allele restricting an epitope with 211-E Position 9 of ETINEEAAEW A*2501 LANL B (N=324): 93% E 6% D

14. Elite-controller B57+ protective epitope (Walker and colleagues) Position 1 of HTQGYFPDW B57 Only 1 B57+ vaccinee (H at site 116) LANL B (N=824): 84% H 14% N

15. LANL B (N=210): 72% T 24% I 1% V 0% -

16. 9-Mer Scanning • For each 9-mer in Gag, Nef, and Pol, we tested for a difference (vaccine vs placebo) in protein distances to MRKAd5 • Found 4 regions with a q-value<0.2 with 3 of the 4 regions showing a greater distance to the vaccine in among the vaccinees. * in this region vaccinee sequences are closer to the vaccine than placebo sequences ** this region overlaps the B-57 restricted HW9 epitope

17. Global Sieve Analysis: Methods and Results

18. Summary Measure Sieve Analysis • Compute distance v from a subject’s set of sequences to the MRKAd5 sequence • For simple and valid statistical tests, use one number per infected subject • Wilcoxon tests of whether the distributions of summary measures differ between infected vaccine vs infected placebo

19. Complementary Distance Measures 1) Previous results presented by Morgane Rolland: Distance = Average of protein distances across all epitopes that are predicted in both the MRKAd5 sequence and in a subject’s set of founder sequences Distance x MRKAd5 All epitopes identified in the cohort Mismatch rate 2/6 X X 2) New results presented next: Percent Epitope Mismatch Distance = Estimated percentage of predicted epitopes in the MRKAd5 sequence that are mismatched in at least one of a subject’s observed sequences

20. Percent Epitope Mismatch: MRKAd5Gag/Pol/Nef NetMHC Epipred The corresponding p-values based on the distances shown previously by Morgane Rolland were both significant (0.02 and 0.007 respectively)

21. Percent Epitope Mismatch: Gag, Pol, Nef Epipred Nef Pol Gag NetMHC Gag 0.005 Nef Pol The corresponding p-values based on the distances shown previously by Morgane Rolland were significant for Epipred/Nef (0.03) and NetMHC/Gag (<0.0001)

22. Percent Epitope Mismatch: HXB2non-insert proteins Epipred Env-Rev-Tat-Vif-Vpr-Vpu NetMHC Env-Rev-Tat-Vif-Vpr-Vpu

23. Summary of Results • Local sieve analysis of ‘signature’ sites • Statistical evidence for 10 AA signature sites in Gag, Nef, Pol; none in Env • One particularly strong signature (Gag 84) • Interpretation: There was greatest statistical power to detect site 84 as a signature, because of the large sample size (n=36 subjects with a restricting allele). Vaccine-induced selection pressure may have operated on many other sites, but there is low statistical power for sites in epitopes restricted by rare alleles. • Global sieve analysis • Statistical evidence that vaccinee sequences had greater epitope-based distances to MRKAd5 than placebo sequences for Gag and Nef (not Pol)

24. Challenges to Interpretation of Global Sieve Analysis • While the results are statistically valid, what do they mean? • The extent to which the global sequence differences are driven by a small number of epitope regions is not yet clear • The interpretation of the sequence differences depends on the epitope prediction method (Epipred or NetMHC), which do different things • Epipred predicts CTL epitopes based on all known epitope sequence motifs found in Brander’s A-list and IEDB. It uses 2-digit, 4-digit, and supertype HLA information • NetMHC predicts CTL epitopes based on experimental binding affinity of peptides using 4-digit HLA information • Not surprising that the results differ by algorithm

25. What are the Functional Consequences of the Observed Sequence Selective Effect?

26. Functional Consequences: T-Cell Response • Were there anamnestic responses to MRKAd5 proteins deriving from vaccination and subsequent infection?

27. Post-infection CD8 T-cell Responses to Proteins Contained in the Vaccine are Stronger in Vaccinees* p = 0.021 positive responses negative responses % CD8 T cells producing IFN-g and/or IL-2 Gag/Pol/Nef All other proteins Plac Vacc Plac Vacc *Nicole Frahm presented these data at AIDS Vaccine 2009 • CD8 T-cell responses were measured by ICS in 87 participants (33 placebo and 54 vaccine recipients) • Samples were obtained 1 week (8 participants) and 2 weeks (79 participants) post HIV diagnosis

28. Functional Consequences: Viral Load • Did the boosted vaccine-induced T-cells suppress viral load? • At set-point: No (except possibly for some HLA alleles) • During acute infection: Possibly

29. Acute Viral Load Acute Viral Loads N = 29 infected subjects have acute-phase VL (out of 87 cases) Acute = sample that is HIV RNA+ and HIV Ab Negative (ELISA Neg and WB Neg or Indeterm) (n = 15) (n = 14) Estimated mean difference: 0.27 (95% CI -0.28 to 0.83) *Analysis by Holly Janes

30. Combined Viral Load and Signature Analysis • The antigenic selection pressure may have caused a transient suppression of viral load, with the suppressive effect lost within weeks or months after HIV acquisition • Approach • At the identified signature sites, do subjects with matched signature sequences have lower acute VL vaccine vs placebo?

31. VL: Insert Matched Residue at Gag 211 31

32. Viral Load Vaccine vs Placebo for Subjects with Insert Matched Residue at Nef 116

33. VL: Insert Matched Residue at Pol 541 33

34. Follow-Up Studies • Additional sequence data: • Mullins lab measuring HIV sequences at 2-3 time-points over the first 12 months of infection • Will allow direct assessment of whether and how vaccination alters HIV evolution and in particular the pattern or rate of escape mutations • Additional T cell response data: • McElrath lab is measuring post-infection T cell responses to an array of peptide targets, which will allow evaluation of whether vaccination accelerated the development of T cell responses • Step ancillary studies

35. Conclusions (Sequence Data) • The analysis of HIV sequences shows significant differences in breakthrough viruses for vaccine vs placebo recipients • The nature of the differences supports that the vaccine selected against viruses with certain amino acids in T cell epitopes, suggesting that the vaccine induced immune responses that put antigenic pressure on the virus • While the MRKAd5 vaccine is not clinically useful, this result may be a milestone in T-cell based vaccine research, providing guidance for the development of improved T-cell based vaccines

36. Conclusions (Acute Viral Load Data) • The analysis of acute VL data suggests (nonsignificant trend) that the vaccine transiently and modestly suppressed acute VL • The sequence analysis suggests the hypothesis that this suppression was due to a vaccine-induced acceleration of T cell evolution

37. Conclusions (T Cell Response Data) • The analysis of post-infection T cell responses shows significant anamnestic responses to MRKAd5 proteins deriving from vaccination and subsequent infection, which is consistent with a transient vaccine-induced suppression of VL • However, few vaccinees had measurable pre-infection T cell responses to the protein regions or signature sites that contributed most to the sequence differences, raising open questions • The forthcoming additional sequence data and T cell response data are expected to provide additional insights into the vaccine effects

38. Acknowledgments • McCutchan lab • Francine McCutchan* • Sodsai Tovanabutra • Eric Sanders-Buell • Meera Bose • Andrea Bradfield • Annemarie O’Sullivan • Jacqueline Crossler • Teresa Jones • Marty Nau • Jerome Kim • Merck • Danilo Casimiro • Michael Robertson • HVTN • Susan Buchbinder • Ann Duerr • John Hural • David Chambliss • Patricia Dodd • Nicole Frahm • David Friedrich • Dan Geraghty • Julie McElrath • Larry Corey • Mullins lab • Dana Raugi • Stefanie Sorensen • Jill Stoddard • Kim Wong • Hong Zhao • Laura Heath • Morgane Rolland • Jim Mullins • SCHARP • Craig Magaret • Holly Janes • Tomer Hertz • Fusheng Li • Steve Self • Plus thanks to David Nickle & David Heckerman • *Now at the Gates Foundation

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