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HIV-1 Resistance Evolution K. Theys Rega Institute for Medical Research Introduction Rega Institute for Medical Research Treatment Methods Kristof Theys Design Bayesian Network Fitness Landscape Case study Clinical and Epidemiological

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SLIDE 1

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Rega Institute for Medical Research

Kristof Theys

Clinical and Epidemiological Virology Katholieke Universiteit Leuven

April, 24th Arevir Meeting, Bonn

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SLIDE 2

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 3

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 4

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Clinical and Epidemiological Virology

Research interests

Factors that influence therapy response of HIV infected patients

viral resistance against HIV drugs

Molecular evolution and epidemiology of HIV

subtype diversity of HIV tracing the origin of HIV

Combined approach enables to study the evolution

  • f HIV during selective pressure of HIV
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SLIDE 5

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Rega analysis tools

Rega algorithm

genotypic resistance interpretation system

Rega HIV subtyping tool Sequence analysis tools

Alignment Translation to amino acids Resistance interpretation

All accessible at http://jose.med.kuleuven.be All integrated in RegaDB

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SLIDE 6

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 7

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Therapy Response

Many factors influence therapy outcome

adherence dosis drug interactions metabolism - absorption complexity - toxicity antiviral resistance

Keyrole for antiviral resistance

cause and consequence of failure Avoid development of resistance

irreversible proces cross-resistance

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SLIDE 8

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Selecting the optimal combination

Life-long HIV antiviral treatment

Planning successful drug sequencing strategies

Dual requirements for combination Short term

be potent (virological suppression) forgiving (minimally affected by adherene) high genetic barrier** to resistance

Long term

minimally cross-resistance

Sequencing strategies most applicable for drug naive

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SLIDE 9

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Rega’s objectives

Optimalisation is necessary for succesful lifelong therapy Time to failure of combination therapy Optimal therapy options after therapy failure

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SLIDE 10

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Rega’s methods

Better understanding how resistance develops and evolves under the selective pressure of (combination) therapy.

Influences on resistance evolution

Impact of genetic variability (inter/intra) Interactions between mutations

Genetic barrier of a drug/combination

Time to therapy failure

Evolutionary distance to resistance

Genetic barrier

Potency of the combination (activity)

Phenotype, adherence, . . .

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SLIDE 11

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 12

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Applied techniques and applications

Bayesian network learning for resistance development

role of mutations and polymorphisms in treatment failure influence of subtype diversity on resistance improve genotypic interpretation systems

Estimation of fitness landscapes

model of HIV evolution under the selective pressure of treatment predict evolution of HIV

Calculate genetic barrier

define the genotypic correlates which influence genetic barrier

Prediction of therapy response

genotypic predictors based on estimated fitness

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SLIDE 13

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 14

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Bayesian Network Analysis

Probabilistic Graphical Models Finds minimal set of direct dependencies that together explain most observed correlation Represent direct dependencies in a directed graph A Bayesian Network refactors the JPD of multivariable data in product of CPDs: P(A1, . . . , An) =

n

  • i

P(Ai|parents(Ai)) by assuming conditional independencies Refactoring of JPD

P(30N, 71V, 88D, 90M) = P(71V|30N)xP(88D|30N)xP(30N)xP(90M)

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SLIDE 15

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Bayesian Network learning

On amino acid sequence data

A Bayesian Network can be “learned“ from data Can be used as a blue-print for amino acid interactions

for an emperical fitness function

Can be interpreted semantically

mutations connected to drug node

presence is directly influenced by drug major mutation

mutations connected to each other

presence is influenced by other mutation minor mutation in resistance pathway

mutations connected to polymorphisms

presence is influenced by natural variation

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SLIDE 16

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Bayesian Network of Nelfinavir resistance

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SLIDE 17

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 18

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Darwinian Fitness

Defines the ability to replicate in a given environment

dependent on virus (replication capacity) dependent on environment (CTL response, therapy, ...)

concepts

Replication Capacity = Darwinian fitness in patient off treatment

sometimes referred as fitness

Fitness (during treatment) = Darwinian fitness in patients taking treatment

combination of phenotype and replication capacity

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SLIDE 19

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Why fitness?

Fitness is the driving force behind evolution

Selective pressure of therapy influences relative fitness

  • f population

Shift in quasispecies distribution and selection of resistant strains

Viral Resistance is the Outcome of Viral Replication, Mutation and Selection

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SLIDE 20

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Estimating a fitness function

. . . to imagining a fitness landscape

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SLIDE 21

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Fitness Landscape

Objective

A model for HIV evolution during treatment

Mutation, fitness and selection

Main components

An estimated in vivo fitness landscape A mathematical model of evolution

Concept

Observing evolution provides information on fitness High correlation between prevalence and selective advantage

Simulated evolution over the landscape

predict evolution genetic barrier

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SLIDE 22

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Fitness Landscape

Principle

Fitness function

Fitness function structure

using interactions indicated by Bayesian Network learning (structure)

Fitness function parameters

comparing two sequence populations sequences PN from class-naive patients sequences PT from patients treated with a single inhibitor in its class so that simulated evolution of a naive population over the fitness landscape resembles the treated population

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SLIDE 23

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Fitness function structure

Bayesian Network: P(A1, . . . , An) =

n

  • i

P(Ai|parents(Ai)) Fitness function: F(A1, . . . , An) =

n

  • i

F(Ai|parents(Ai))

Bayesian network structure refactors the JPD P(30N, 71V, 88D, 90M) = P(71V|30N)xP(88D|30N)xP(30N)xP(90M) Corresponding relative fitness function F(30N, 71V, 88D, 90M) = F(71V|30N)xF(88D|30N)xF(30N)xF(90M) F(88D|30N) represents contribution of mutation 88D depending on presence of mutation 30N

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SLIDE 24

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Estimating a fitness function

model parameters HIV intra−host evolution Data Fitness landscape parameter estimation Naive sequences mutation biases µ Ne Bayesian Network learning Bayesian Network Fitness landscape Parameter Adjustment Evolution Simulator Fitness function structure Phylogeny Treated sequences

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SLIDE 25

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Fitness Landscape

validation

Once the fitness model has been builded

fixed fitness structure fixed fitness values for each term in the function

Number of ways to validate the model

correlate in vitro resistance versus estimated fitness Predict evolution under treatment for a single sequence

Summarize in a graph nodes corresponds to predicted sequences arcs corresponds to predicted mutations

Statistical test to correlate predicted evolution versus

  • bserved evolution
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SLIDE 26

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Fitness Landscape

validation

Baseline sequence, Treated sequence

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SLIDE 27

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Individualised genetic barrier to resistance

Genetic Barrier

Evolutionary distance of the virus to escape the inhibition of the drugs Simulate evolution from a baseline sequence, over such a fitness landscape, together with a criteria for resistance, allows the estimation of the genotypic barrier to resistance

Simulate evolution until considered "resistant" (N=100) Measure of resistance defined by Rega algorithm Average time as estimate for genetic barrier. number of mutations MR number of simulated generations GR

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SLIDE 28

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Individualised genetic barrier to resistance

Investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure

population of sequences, susceptible to NFV association of MR, GR or log F using regression a higher estimated genetic barrier was associated with lower

  • dds for development of resistance at failure

thus, variation in individualised genetic barrier to NFV resistance may impact options after failure Similar for other drugs??

find genotypic correlates

impact of protease mutations and polymorphisms on estimated genetic barrier to NFV resistance

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SLIDE 29

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Genotypic correlates that influence the barrier

88D (0.1 %) 33F (0.1 %) 36V (0.3 %) 75I (0.2 %) 20T (0.1 %) 71V (3.1 %) 10F (0.4 %) 13V (17 %) 0.0 0.5 1.0 1.5 2.0 41K 12P 89M 37A 69Y 17D 89I (26 %) (3.3 %) (1.3 %) (1.6 %) (2.4 %) (0.3 %) (0.1 %) 10I 12K 45R 36I 64M 35D 10V 20R 77I 70R 71T 62V 64V 72V (8.0 %) (1.0 %) (1.3 %) (1.4 %) (2.3 %) (2.4 %) (3.1 %) (5.8 %) (14 %) (26 %) (29 %) (23 %) (18 %) (9.3 %) Fold change genetic barrier

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SLIDE 30

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Clinical utility of estimated Fitness Landscape for Prediction of virological outcome

To treatment with zidovudine + lamivudine + nelfinavir

Collaboration with EuroSIDA (A. Cozzi-Lepri) Objective: proof-of-principle validation of using fitness functions to predict treatment outcome Prediction of virological outcome at two end-points

VL change at week 12 (176 TCEs)

censored for VL below detection limit of the assays ignoring treatment changes

risk of virological failure at week 48 (90 TCEs)

virological failure defined as a VL >400 copies/ml

  • nly those still on the same treatment

In comparison with expert genotypic IS (GGS)

Rega v7.1, ANRS 2006.07 and HIVDB 4.2.9

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SLIDE 31

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Outline

1

Introduction Rega Institute for Medical Research Treatment of HIV-1 infection

2

Understanding HIV evolution under selective pressure of HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape

3

Current Research Baseline CD4 and VL in naive patients

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SLIDE 32

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

SPREAD

European Commission supported sureillance programme

representative data of newly diagnosesd individuals analyses the spread of drug-resistant HIV and subtype distribution substudy (EHR Budapest 08)

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SLIDE 33

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Study Objective

Finding genetic correlates

baseline CD4 cell count baseline viremia

Hypothesse

CD4 marker for virulence Genetic variation because of epidemiology

subtypes,...

Genetic variation because of treatment

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SLIDE 34

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Background

Disease progression

highly variable rate of disease progression

Impact of TDR on disease progression in newly infected patients is not well understood

different rates of persistence and reversion of TDR complex relationship between fitness cost and persistence due to mutational interactions

Presence of DRMs, associated with an impaired replication capacity, could be beneficial

lower viral load higher CD4 count slower disease progression

Compensatory mutations to restore replication capacity defects

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SLIDE 35

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Method: Influence of Treatment

To study influence of treatment

investigate if and how treatment-selected mutations correlate with VL/CD4

Quantification using estimated "fitness under treatment" (F)

Fitness correlates with selection under treatment Estimation of fitness function

Note

Not only (major) DRMs But ANY treatment-associated mutation (including polymorphisms)

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SLIDE 36

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Results

Fitness Functions

Protease fitness fuction

compare PI naive patients with PI treated patients

Reverse Transcriptase fitness function

compare RT naive patients with RT treated patients

Second type of fitness function

reverting major resistance mutations to wildtype excluding fitness influence of resistance mutations

Data

2706 patients Subtype B (67%) Censoring for VL: > 1000 and above upper detection cut offs Censoring for CD4: < 2000 Excluding patients with indications of recent infection

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SLIDE 37

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Results: Correlation with Log Viral Load

Pearson correlation test

Analysis All Subtypes Subtype B PostSC Log FPI

  • 0.013

0.010 Log FPI−m

  • 0.005

0.005 Log FRT

  • 0.29

0.55 Log FRT−m

  • 0.31

0.30 Log FPI * Log FRT

  • 0.086

0.053 Log FPI−m * Log FRT−m

  • 0.025

0.024 Table: Regression analysis to predict log viral load.

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SLIDE 38

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Results: Correlation with CD4 cell count

Non-parametric test (Kendall Tau)

Analysis All Subtypes Subtype B PostSC Log FPI

  • 0.067

0.019 Log FPI−m

  • 0.058

0.017 Log FRT

  • 0.28

0.113 Log FRT−m

  • 0.55

0.199 Log FPI * Log FRT

  • 0.255

0.12 Log FPI−m * Log FRT−m

  • 0.142

0.08 Table: Regression analysis to predict CD4 cell count

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SLIDE 39

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Conclusions

Estimated higher fitness for PRO sequences from naive patients is associated with lower viral load and higher CD4 count Estimated higher fitness for RT sequences from naive patients is not significantly associated with lower viral load and higher CD4 count Strength of assocations is increased when effect of resistance mutations is excluded

association is mainly due to polymorphic treatment-selected mutations

Correlations coefficients are small

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SLIDE 40

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Discussion

What do we know ...

Protease is less conserved compared to Reverse Transcriptase

purifying selection is more stringent on RT

During PR treatment, selection of

(major) mutations that confer reduced susceptibility (minor) mutations that restore enzymatic efficiency of the protease enzyme

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SLIDE 41

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Discussion

What do we know ...

Compensatory (polymorphic) mutations do sometimes appear before MRMs during PI treatment

may indicate these mutations also increase replication capacity in the absence of MRMs thereby increasing efficiency of protease (Deforche 2008)

After transmission of resistant virus, major resistance mutations tend to revert quickly due to their deleterious effects in absence of therapy.

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SLIDE 42

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Discussion

Hypothesis

Lower conservation of PR versus RT could be an indication that maturation is less a bottleneck, allowing more variation in efficiency (’sloppy protease’) Use of potent PR inhibitors induces a new bottleneck

strength of purifying selection on PR increases deleterious mutations become less tolerated

After transmission, selective pressure to keep resistance associated mutations is absent, thereby allowing their reversal, in contrast to mutations that restore enzymatic efficiency, which remain present, thus resulting in an overall more efficient protease Does treatment pressures the virus to ’optimize’ its enzymes, thereby selecting a new, more virulent virus?

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SLIDE 43

HIV-1 Resistance Evolution

  • K. Theys

Introduction

Rega Institute for Medical Research Treatment

Methods

Design Bayesian Network Fitness Landscape

Case study

Damvil Study

Thanks to . . .

AREVIR-GenaFor-Meeting consortium Interuniversity Attraction Pole, IAP , grant P6/41. Belgian Fonds voor Wetenschappelijk Onderzoek, F .W.O., grant G.0611.09 The research leading to the results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under the project "Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN)" - grant agreement nÂ◦ 223131 Members of the Clinical and Evolutionary Virology group at the Rega Institute, KUL

  • Prof. A.-M. Vandamme, Koen Deforche, Kristel van Laethem,

....

...