Transcript Treatment-Experienced, R5-Only HIV: Tropism and Maraviroc

Evoluzione genetica di HIV
ed evoluzione clinica
della malattia AIDS:
due aspetti correlati?
Carlo Federico Perno
Why does a Virus evolve?
A virus needs to evolve to:
• Infect different cell types
• Rapidly become resistant to otherwise
highly effective antiviral drugs
• Evade the immune system
Virus transmission
• Obstacles for virus transmission:
– Natural
•
•
•
•
Host genetics
Host Immune system
Viral replication rate
Etc
– Artificial
• Vaccines
• Passive immunity
• Antiviral drugs
• A continuous evolution allows viruses to achieve new
characteristics able to overcome these obstacles, and
be successful in their replication effort
Why does a Virus evolve?
A virus needs to evolve to:
• Infect different cell types
• Rapidly become resistant to otherwise
highly effective antiviral drugs
• Evade the immune system
…SURVIVE !!!
Consequences
- The most fit virus, with the highest
chances to survive, does not kill the
host or, at minimum, kills the host in a
long run
- To be selected and expanded, it kills
the host at a rate lower than other
viruses of the same species
- Ex: HIV subtype A vs Subtype D
Does HIV-1 genetic diversity have an effect
on clinical progression?
Subtype C vs. subtype A, P at log-rank = 0.2
Subtype D vs. subtype A, P at log-rank = 0.05
Analysision 145 HIV-1 infected Kenyan
women followed from the time of HIV-1
acquisition.
“HIV-1 Subtype D is associated with faster disease
progression than Subtype A in spite of similar plasma
HIV-1 loads”
Baetan JM , JID 2007
How does a virus evolve?
Evolution = genetic variation
The steps in virus evolution are:
• generation
of
diversity
through
mutation,
recombination, and genome segment reassortment
in multipartite genomes
• competition among the generated variants
• selection of those mutants showing the largest
phenotypic advantage in a given environment
“All the organic beings which have ever
lived on this earth have descended from
some one primordial form”
Charles Darwin
From this idea, each characteristic of a species
could be the result of a peculiar evolutionary
history:
•
•
•
•
•
•
Peacock’s ancestors
The number and the sequences of his genes
The catalytic ability of his enzymes
His needs
The structure of his cells
His environmental fitness
... This is his evolutionary history
Evolution is the unifying theory of biology
“Nothing in biology makes sense except in the light of the evolution”
Theodosius Dobzhansky
• In biology a mutation is a randomly derived change to
the nucleotide sequence of the genetic material of an
organism.
• Non lethal mutations accumulate within the gene pool
and increase the amount of genetic variation. The
abundance of some genetic changes within the gene pool
can be reduced by natural selection, while other "more
favorable" mutations may accumulate and result in
adaptive evolutionary changes.
Does occurrence of mutations mean
their necessary selection and
appearance (fixation) in circulating
virus strains?
NO
Same amino acid, same protein
Silent Mutation
Synonymous
Point Mutations
Substitution of a Nucleotide
Early stop of protein
Mutation
Non Sense
A C G T 4 nucleotides
X X X 1 codon = 1a.a.
43 = 64 codons -> 20 a.a.
Mutation
Different amino acid, protein
NON synonymous
mutated
What is the relation between the HIV tropism
and its evolution
Objective
● To examine tropism after ART failure using a clinically validated
genotypic tropism assay in a large sample of treatmentexperienced patients
● HIV-1 tropism was assessed at baseline and virologic failure
over 48 weeks in patients receiving an optimized background
regimen (OBT) with a maraviroc placebo (PBO) in the
MOTIVATE 1 and 2 studies1
1Gulick et
al. N Engl J Med. 2008;359:1429-1441
Svicher et al. 2009
15
16
Rate of successful V3 sequencing
V3 sequencing was successful for 87 (80.5%) out of 108 samples
83 out of 87 have also the trofile result available. For the remaining 4,
Trofile failed to assess viral tropism.
All of them resulted R5 with Trofile at screening
Svicher et al. 2009
~90% of patients with R5 tropism at screening had
an R5 result at treatment failure.
Screening
Tropism
False positive rate
5%
Tropism at treatment failure
R5
X4
R5
78 (89.7)*
3 (3.4)
X4
1 (1.2)
5 (5.7)
Viral tropism has been predicted by Geno2Pheno algorithm at a false positive
rate of 5% using 87 V3 sequences
*P< 0.001
17
18
Analysis of 65 patients where R5-usage is maintained
both at screening and at failure
Svicher et al. 2009
Change in entropy from screening to failure
Despite tropism stability (R5 both at screening and at failure), the majority
(23/35) of V3 positions shows higher entropy at failure than at screening
suggesting viral evolution despite tropism stability
V3 positions
The analysis was performed in the sub set of 65 patients with R5-tropism at baseline and failure (using
geno2pheno at FPR of10%).
The Shannon entropy was calculated for each V3 position following the formula:
H(i) = - Σ P(si) log P(si) (where s=A,S,L,… for the 20 amino acids Ala, Ser, Leu, . . .).
The difference between entropy at screening and at failure at each V3 position is reported in the graph.
Svicher et al. 2009
19
20
Increased genetic diversity between screening and failure
significantly correlates with higher duration of treatment
Spearman Correlation
between Genetic Diversity
and Therapy Duration
Rho
P Value
0.2001
0.003
Rho is the Spearman's rank correlation coefficient. Rho, ranging from -1.00 to 1.00, is a measure
of the strength and direction of the association between two variables. A positive coefficient
indicates that the variables X and Y increase in a correlated manner.
The genetic distance (mean number of substitutions per site) of V3 sequences from screening to
failure for each patient and treatment duration were used to calculate Rho. The analysis was
performed in the sub-set of 65 patients with R5-tropism at baseline and failure (using geno2pheno
algorithm at FPR of 10%).
Svicher et al. 2009
Despite the high natural genetic variability of V3, the
frequency of tropism switches remains limited
21
- An accumulation of synonymous substitutions was observed
from screening to failure in all 87 patients (100%)
- An accumulation of non-synonymous (amino acidic)
substitutions was observed in 26/87 patients (28.9%)
- Tropism switches were observed in only 4 patients (3 from R5
to X4, 1 from X4 to R5) [4/87, 4.6%]
The analysis was performed on all 87 patients on study (using geno2pheno algorithm at FPR of 5%).
Average observation from baseline to virological failure was 150 days
Switches from R5 to X4 usage
is mainly driven by a shift of viral species
R5 at screening
X4 at failure
R5 at screening
X4 at failure
R5 at screening
X4 at failure
The analysis was performed on the 3 samples resulting R5 at screening and X4 at failure using
geno2pheno algorithm at both 5% and 10%. Genetic distance is the mean number of substitutions
per site.
The high genetic distance values and the high number
of amino acid substitutions from screening to failure support
the shift from an R5-using strain to an X4-using strain
Svicher et al. 2009
CONSEQUENCES
C. If a mutation with lower fitness remains fixed, we obtain a
minority species (called quasispecies), that may become
predominant if the environment changes
Ex 1. Antiviral pressure that selects for a viral strain with
lower sensitivity to drugs
2. Immunological pressure by a vaccine that selects for
an escape mutant not neutralized by the immune system
In the case of viruses, this switch in predominance may
take days (not millennia!!)
- Selection of strains resistant to antiviral drugs
Re-emergence of the most fit R5-virus
Tropism
at Failure:
D/M
Baseline Tropism:
Designated R5
Maraviroc
in Suboptimal
Regimen
Re-Emergence
of R5!!
Stop
Maraviroc
X4 HIV
Not Detected at <4%
R5
X4
D/M
Non-functional clone
Lewis M, et al. 16th IHIVDRW, 2007. Abstract 56.
Clinical Case: Id 186 - Patient
infected with HIV-1 B subtype
Age:
46
GRT during therapy interruption
GRT September ‘02
PR: L63P V77I I93L
RT: G333E
GRT May ‘06
PR: L63P V77I I93L
RT: G333E
CDC stage:
C3
Sex:
M
Risk Factor:
Not known
GRT under antiretroviral treatment
GRT March ’02 (ARV: 3TC d4T ABC LPV/r)
PR: L10I M36V M46L I54V L63P A71T V82A N88D
L90M I93L
RT: M41L E44E/D D67N L74L/V V118V/I M184V
G190G/E/Q/R L210W T215Y K219K/N G333E
GRT March ’05 (ARV: 3Tc d4T LPV/r)
PR: L10I K20R L33F M36M/I/V M46I I54V L63P
A71T G73G/A V82A N88D L90M I93L
RT: M41L E44E/D D67D/N V118I M184V L210W
T215Y G333E
GRT January ’08 (ARV: AZT 3TC ABC DRV/r)
PR: L10I K20R V32I L33F M36I K43T M46I I47V
I54V L63P A71T G73A/T I84V N88D L90M I93L
RT: M41L E44D D67N V118I M184V L210W
T215Y G333E
• Virus under drug pressure selects, among
thousands of quasispecies present in the body,
the virus strain with the greatest fitness in that
environment
– Wild type strain (the most fit) without drugs
– Highly mutated (resistant) strain in the presence of
drugs
No chances of winning the battle until viral
replication is sharply decreased/nullified
CONSEQUENCES
• The replication is a necessary prerequisite
for occurrence and appearance of
mutations
• Without replication, no mutation
By decreasing the replication rate of a virus,
we dramatically decrease its ability to
escape immune system and antiviral drugs
BUT……
……If a mutation produces a variant with low fitness, and/or
this mutation is not fixed, this new variant disappears
Ex. Loss of viral species
Allele Frequency
1
0
fixed
mutation
polymorphism
maintained
lost
mutation
Time
Effective sample size: the genetic bottleneck
Population dynamics of alleles
Each different
symbol represent a
different allele.
A mutation event
in the sixth
generation gives
rise to a new allele.
The figure
illustrates fixation
and loss of alleles
during a
bottleneck event,
and the concept of
coalescence time
(tracking back the
time to the most
recent common
ancestor of the
gray individuals).
N: population size.
Coalescence
time
Bottleneck
event
Mutation
event
Adapted from The Phylogenetic Handbook 2009, M Salemi and AM Vandamme
PRACTICAL CONSEQUENCE
• By reducing the sample size of a species (bacteria,
viruses, etc) we dramatically reduce the chance that
the species mutates and thus escapes pressure by
chemotherapy and/or immune system:
- Success of antivirals and antibiotics despite a small
remaining number of microorganisms
- Success of vaccines against viruses with low mutational
rate
- Insuccess of vaccines against highly mutating viruses
- Insuccess of vaccines targeted against genes with high
rate of mutations
- The case of smallpox virus
- The case of influenza virus
Evolutionary abilities of Variola
• Variola has a single linear double stranded DNA genome of 186
kilobase pairs.
• Some studies showed the presence of a low mutation rate.
• A similar situation is present in other component of orthopoxviruses
genus.
Number of SNPs found in different couples of viral isolates
Variola virus
Isolated compared
Year isolation
SNPs* among genomes
ETH72_16 vs ETH72_17
1972
0
AFG70_vlt4 vs SYR72_119
1970, 1972
1
SYR72_119 vs PAK69_lah
1972, 1969
1
SYR72_119 vs IRN72_tbrz
1972
1
Adapted form Li et al., 2007
*Single Nucleotide Polymorphisms
Variola is lacking of great evolutionary potential
Smallpox Vaccine
• Its history is strictly
connected to the
birth of modern
vaccinology.
• Variolation
• 1796: Edward
Jenner.
• 1977: last case of
smallpox.
Such good result was due to…
•
•
•
•
the biological characteristics of the organism,
vaccine technology,
surveillance and laboratory identification,
effective delivery of vaccination programmes and
international commitment to eradication.
• Smallpox virus has no host reservoir outside
humans!!.
The case of influenza virus
The evolutionary power of
antigenic shift
The last known flu pandemics
Name of
pandemic
Subtype
involved
Pandemic
severity
index
Asiatic
(Russian)
H2N2
NA
Spanish
H1N1
5
Asian
H2N2
2
Hong Kong
H3N2
2
“Swine”
H1N1
NA
Genesis of “Swine flu” H1N1 virus
Avian flu virus
(unknown subtype)
Human H3N2 flu virus
Swine H1N1 flu virus
H1N1 “Swine flu” virus
Classical swine flu virus
H1N1
H3N2 swine virus
The reservoir hosts act as
variability source for the new
evolutionary steps of flu virus
The evolutionary novelty of
“Swine flu” virus
… gene sequences collected from the USA for swine flu (subtype H1N1) in the year 2009 are
evolutionarily widely different form the past few years sequences…the 2009 sequences are
evolutionarily more similar to the most ancient sequence reported in the NCBI database collected
in 1918. (Sinha et al., 2009)
CONSEQUENCES
Smallpox: Low rate of polymerase errors + lack
of animal reservoir (even in the presence of
high replication rate)
=
Eradication possible (and obtained indeed!!)
Flu: High rate of polymerase errors + presence
of multiple animal reservoirs (+ high rate of
recombination)
=
Eradication impossible
New vaccine required every year
Resistance to anti-HIV drugs is the most
elegant, and practically relevant, example of
the consequences of viral evolution
What about the effect of resistance
on clinical outcomes?
HIV-1: Drug resistance development
Toxicity
No Adherence
Bioavailability
Patients’ Metabolism
Reservoir
It’s important to detect resistant quasispecies before
the treatment starting or as soon as possible during
treatment
Percentage of patients’ viruses who had RTI resistance mutations at t0 and of
those who acquired such mutations from t0 to t1 by specific mutation/drug class
In patients kept on the
same virologically failing
cART regimen for a
median of 6 months,
there was considerable
accumulation of drug
resistance mutations
RTI resistance at t0
RTI resistance from t0 to t1
Cozzi-Lepri et al., AIDS 2007
Poor survival in drug-class
multi-resistance
1,0
CD4 time-dependent
(per 50 cells
increase)
Multivariate
0.79 (0.66-0.93)
1.14 (0.76-1.71)
Previous AIDS
2.29 (0.86-6.12)
3 drug class multiresistance (DCMR)
1 DCMR
2 DCMR
Plasma HIV-RNA
time-dependent
(per 1 log10 increase)
LPV after GRT
0 DCMR
P
,9
Cumulative proportion surviving
Logistic
regression
0.57 (0.19-1.67)
0.006
,8
P at log-rank <0.001
,7
0.539
,6
0.098
,5
3 DCMR
0.302
,4
0
12.29 (3.00-50.28)
<0.001
200
400
600
800
1000 1200 1400
1600
Days from GRT
Zaccarelli, AIDS 2005
Main Findings
•
In multivariable analyses, patients with drug resistance mutations to ≥ 2 classes
during first 2 years of HAART at significantly higher risk of AIDS progression
or death
Definition of Resistance
Adjusted RH
(95% CI)
P Value
 ≥1 NRTI mutation
1.52 (1.14-2.03)
.004
 ≥1 NNRTI mutation
1.95 (1.28-2.95)
.002
 ≥1 PI mutation (major and minor)
1.50 (1.14-1.97)
.004
Drug-class resistance mutations
1.79 (1.28-2.50)
.0007
 Virologic failure with no resistance
1.32 (0.57-3.06)
.52
 Single-class resistance
1.03 (0.65-1.63)
.90
 Double-class resistance
1.55 (1.15-2.08)
.004
 Triple-class resistance
1.80 (1.20-2.70)
.005
Cumulative drug-class resistance
(major and minor PI mutations counted)
Cozzi-Lepri A, et al. AIDS. 2008;22:2187-2198.
 Virus continues to evolve if kept under pressure of failing
antiviral therapy.
 This may increase cross-resistance, and then decrease
chances of efficacy of subsequent drugs and regimens.
 In the frame of a correct therapeutic sequencing, first
failing therapies should be changed as soon as possible
after definition of virological failure.
Conclusions
• Viruses represent the best model of evolution on the
earth
• They mutate in days faster than what humans have
ever changed in millennia
• Their evolution capacity is function of several factors
• The host represents the most important extrinsic
factor
• Through a proper use of interdisciplinary tools
(mathematics, physics, biochemistry, molecular
biology, biology, pharmacy, medicine) we can
reasonably predict their evolution, and define ways
and consequences of the interaction with humans
Conclusion (II)
• The understanding of viral evolution has major
consequences in medicine, of key practical relevance:
• Identification of targets for viral vaccines
• Definition of potential outcomes of massive
vaccinations
• Eradication, infection containment, functional
cure of infected people
• Setting therapeutic strategies against viral infections
• Definition of the chances of success of antivirals
(resistance testing, antivirograms)
• Select therapies with the greatest chances of
success (Ex. multiple drugs against viruses with
high mutation rate)
ACKNOWLEDGEMENTS
University of Rome “Tor Vergata”
C.F. Perno
F. Ceccherini Silberstein
V. Svicher
M. Santoro
A. Bertoli
D. Armenia
S. Dimonte
L. Fabeni
R. Salpini
C. Alteri
V. Cento
F. Stazi
S. Dimonte
L. Sarmati
M. Andreoni
INMI “L. Spallanzani
A. Antinori
P. Narciso
C. Gori
R. d’Arrigo
F. Forbici
M.P. Trotta
A. Ammassari
R. Bellagamba
M. Zaccarelli
G. Liuzzi
V. Tozzi
P. Sette
N. Petrosillo
F. Antonucci
E. Boumis
E. Nicastri
U. Visco
P. De Longis
G. D’Offizi
G. Ippolito
and the Resistance Study Group
ACKNOWLEDGEMENTS
University of Padova
G. Palu’
S. Parisi
Infectious Diseases,
Bergamo
F. Maggiolo
AP. Callegaro
Modena and Ferrara
Infectious Diseases
C. Mussini
V. Borghi
W. Gennari
L. Sighinolfi
F. Ghinelli
L. Sacco University Hospital
G. Rizzardini
V. Micheli
A. Capetti
Infectious Diseases Unit Florence
S. Lo Caputo
University of Rome Tor Vergata
University of Turin
F. Mazzotta
Dept. of Mathematics
G. Di Perri
Livio Triolo
S. Bonora
University Cergy-Pontoise
Mario Santoro
LPTM
University of S. Raffaele
Thierry Gobron
A. Lazzarin
University of Catanzaro
M. Clementi
Arca
S.
Alcaro
M. Zazzi
A. Artese
Catholic University of
San Gallicano Hospital
Rome, Sacro Cuore
G. Palamara
The I.CO.N.A. Study Group
A. De Luca
A. d’Arminio Monforte
M. Giuliani
R. Cauda
M. Moroni