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## Phambili Sieve Analysis

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### Phambili Sieve Analysis

Tomer Hertz

Vaccine and Infectious Disease Division

Fred Hutchinson Cancer Research Center

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

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

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

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

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) }

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

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

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

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

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

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)

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

Step Results: AA Site Scanning

Phambili Results: AA Site Scanning

Step Results: AA Site Scanning by Physicochemical Properties

Phambili Results: AA Site Scanning by Physicochemical Properties

Conducted by Craig A. Magaret

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)

Global hypothesistestingapproachPercent Epitope Mismatch- Step results conducted by AllanDeCamp

Gag

Nef

Pol

Rolland et al. Nature Medicine 2011

Global Hypothesis Testing ApproachPercentEpitope Mismatch- 503 results conducted by AllanDeCamp

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

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)

Global Hypothesis Testing ApproachPredictedEpitope Escape Distancesconducted by Tomer Hertz

StepPhambili

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

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

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