Transcript Slide 1

Trajet d'une expatriée : de la phylogénie du VIH
au traitement de la grippe, et de Paris à San
Francisco
Colombe Chappey
DEA 1986, PhD 1992
Statistiques
Cliniques
Personalized
Health Care
(Soins
personnalisés)
Bioinformatique
DEA 1986
Reconnaissance
de Formes
(These ’92)
Essais
Cliniques
Modélisation
Transmission
de la grippe
Analyse
Exploratoire
Bio-marqueurs
predictifs
Epidémiologie
Analyse
d’images
Epidémiologie
Moleculaire
Programmation
(Computer Science)
Statistiques
Cliniques
Au cours de mon ‘trajet’…
Personalized
Health Care
(Soins
personnalisés)
Bioinformatique
DEA 1986
Reconnaissance
de Formes
(These ’92)
Essais
Cliniques
Epidémiologie
Modélisation
Transmission
de la grippe
Analyse
Exploratoire
Bio-marqueurs
predictifs
Epidémiologie
Moleculaire
VIH
Analyse
d’images
Programmation
(Computer Science)
Partager mon experience
• Transitions
– de la recherche publique en France aux Etat-Unis
– De l’’Academic’ au ‘privé’
– de la petite Biotech a la grosse ‘Pharma’
• Données: Explosion des données genetiques disponibles
– Nouvelles technologies de sequencages
• L’importance du ‘to think outside the box’ (en dehors de sa bulle)
– Position unique du bioinformaticien/biostatisticien entre
données et idées
• “Opportunities is often missed because it is dressed in overalls
and looks like work” (Thomas Edison}
Reconnaissance de motifs
appliquée a la comparaison de
sequences biologiques
Comparaison de séquences nucleiques/proteines
-> Alignement des éléments/motifs en commun
-> pondérer les différences/mutations et les insertions/deletions
…A G G T T G C…
…A G G T C…
Comparaison de séquences biologiques
de Virus d’immunodéficience
• Comparaison de
– 9 séquences de VIH type 1
– 1 séquences VIH type 2
– 5 séquences de VIS
1988
• Le nombre de sequences de
VIH a tres vite augmente.
• Certaines séquences sont plus
similaires que d’autres
MASH : Algorithme d’alignement de plusieurs
séquences
Chappey C, Danckaert A, Dessen P, Hazout S. MASH an interactive multiple alignment and consensus sequence construction. Comp. Applic. Biosci. 1991;
7:195-202.
Applications
Distance entre séquences
Classification
time
Homogénéité et hétérogénéité par region
Chappey C, Danckaert A, Dessen P, Hazout S. MASH an interactive multiple alignment and consensus sequence
construction. Comp. Applic. Biosci. 1991; 7:195-202.
Cas du Dentiste - 1990
Prediction de Structure/function de la Proteine
d’Enveloppe du VIF
Profile of structural constraints=
based on quantification of amino
acid replacements
Selection for change =
Profile of the ratio of
nonsynonymous to
synonymous change
proportions (nsi/si, si)
Pancino G, Chappey C, Saurin W, Sonigo P. B epitopes and selection pressures in feline immunodeficiency virus envelope glycoproteins. J. Virol. 1993; 67:664-672.
Pancino G, Fossati I, Chappey C, Castelot S, Hurtrel B, Moraillon A, Klatzmann D, Sonigo P. Structure and variations of feline immunodeficiency virus envelope glycoproteins. Virology 1993;
192:659-62.
Bilan des années de These
(+) Tremplin pour les collaborations
•
Institut Pasteur, France
•
Agence Nationale Recherche Sida (ANRS)
•
Institut Cochin de Genetique Moleculaire (ICGM)
•
HIV database de Los Alamos National Laboratory, NM
(+) Publications
•
Méthodes
•
Application du logiciel d’alignement
–
Human immunodeficiency virus type 1
–
Transmission HIV mother-infant
–
Simian / human T-cell lymphotropic virus type 1
–
Simian immunodeficiency virus
–
Feline immunodeficiency virus FIV
#
2
4
5
3
1
2
(-) Occasions manquées
•
Commercialisation du logiciel d’alignment (alors que CLUSTAL…)
•
Analyses non-publiées
National Institutes of Health
National Center
Biotechnology Information
(GenBank)
Histoire de GenBank et NCBI
Human EST
BLAST (Basic
Local Alignment
Search Tool)
Human
Genome
GenBank
demenage a NIH
international
computer database
of nucleic acid
sequence data –
Los Alamos Natl
Lab, NM (NSF)
1979
Wilbur and Lipman
Algorithme de
recherche de similarites
entre sequences
Programmation d’un outil d’annotation
et de Soumission de Séquences
Biologiques a GenBank
La publication de nouvelles séquences biologiques nécessite de les
rendre publiques
-Avant, elles etaient publier dans les journaux scientifiques
-Avec GenBank, elles sont envoyées par email au service qui faisait les
annotations et leur associait un numéro d’Acces (Accession Number)
-Besoin d’outil informatique permettant aux biologistes d’annotater leur
séquences avant de les envoyer
-Types de séquences
-Gene codant (CD) -> simple soumission
-EST (Expressed Segment T) -> soumission en batch
-Population de Séquences -> soumission des séquences alignées
Sequin: Soumission de Sequence
aux DB genetiques
1995
http://www.ebi.ac.uk/Sequin/QuickGuide/sequin.htm
Editeur d’Annotation de Sequences
Editeur d’Annotation de Sequences
Alignees
•Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA,
Rapp BA. Database resources of the National Center for Biotechnology
Information.Nucleic Acids Res. 2000 Jan 1;28(1):10-4.
“PopSet” de GenBank
CN3D Viewer
de Structure
de Protéines
Wang Y, Geer LY, Chappey C, Kans JA, Bryant SH. Cn3D: sequence
and structure views for entrez. Trends Biochem Sci. 2000 Jun;25(6):3002.
Marchler-Bauer A, Addess KJ, Chappey C, Geer L, Madej T, Matsuo Y,
Wang Y, Bryant SH. MMDB: Entrez's 3D structure database. Nucleic
Acids Res. 1999;27(1):240-3.
Bilan des années NIH
(+) Acquisition de connaissances dans un institut de renommée
internationale
• Data format: ASN-1 (Abstract Syntax Notation One)
– Format de répresentation de données ISO permettant
l’interoperabilité entre plateformes et représentation de données
hétérogenes.
– Convertie en XML
•
Programmer en C/C++, Web server,
•
Travailler dans le milieu ‘academic’ américain
– Données et programmes sont disponibles au public (QC)
ftp.ncbi.nih.gov
(-) Occasion manquée (ou non)
• l’opportunité de travailler sur le Génome Humain
1998 NCBI - What’s Next?
•
Phénotype: caractères
observables d'un organisme
–
–
–
•
Gene expression profiling:
(par Microarray Affymetrix,
Stanford) sur RNA,
comparaison de
l’expression de génes,
dans différents types
cellulaires (traités nontraités…)
SNPs / DeCode…
HIV Drug Resistance
Database in Stanford
Données cliniques: occurrence
et évolution de maladies
–
–
–
dbGaP: SNPs et maladies
genetiques
Allele mutants et (partial)
resistance a l’infection par
le VIH
Reponse clinique aux
antiviraux et la presence
de virus resistance
ViroLogic Inc
2000-2009
• Mission: "The right therapy to the right patient at the right
time.“
• ~10 antiviraux anti-VIH
• Business Model simple:
Laboratoire
d’Analyses
Hopital
+
Patient
Resistance
Report
•
DB
Algorithm
~100 employes, 80 dans la laboratoire d’analyse, 20 dans la recherche,
l’administration…
Test de Résistance du VIH aux antiviraux
2 approches : Phénotype-Génotype
Translation
Polyprotein
Test de Genotype
determine la sequence
de la proteine cible de
l’antiviral
Un algorithme reconnait
les mutations cles qui
diminue la function de la
proteine
Clivage
Processing
Folding
Test de Phenotype
teste la capacite’ de chaque
antiviraux de diminuer la
FONCTION de la protein
virale cible de l’antivirale.
Database de ViroLogic
Génotype
Small studies
(n ~ 100’s)
Response Clinique
Reduction de la
charge virale
PT-GT database
(n > 100,000)
Phénotype
IC50 fold change
Small studies
(n ~ 100’s)
clinical cut-off pour le phenotype
Identification de mutation associees a la resistance du VIH aux antiviraux
Calling Bases and Mixtures from Raw
Sequence (ABI Chomatogram) Data
codon 184 R(=A/G)TG -> M/V
Fréquences des Mutations par
Réponse virologic apres 2 semaines
Zolopa, A. R. et. al. Ann Intern Med 1999;131:813-821
Régles d’interprétation du Genotype
Resistance Collaborative Group (DeGruttola et al., 2000)
Initially used in GeneSeq assay, with some modifications
Expert Consensus, derived for meta-analysis (not intended for
clinical use)
UK Drug Resistance Database (2006) http://www.hivrdb.org.uk/
Stanford (R. Shafer), HIVResistance.com
Comprehensive, updated frequently, good notes
International AIDS Society IAS (Hirsch et al., JAMA 2000; 2008 updates)
http://iasusa.org Expert consensus; updated frequently
Interprétation du Génotype viral
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Wild-type: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF
Patient
PQIALWQRPLVTIKIGGQLKEALLDTGADNTILEEMNLPGRWKPKMVGGIGGFVKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF
Patient virus genotype
D30N
V32I
T4A
I47V
I54V
Drug Resistance associated
Mutations (RAMs)
D30N I50V
M46I
G48V
A71V
V32I
V82A
I54V
I47V
L90M
N88S I84V
V82F
Regles
d’interprétatio
n du Génotype
D30N
Resistance to NPV
I47V, I54V
Intermediate resistance
to fAMP, TPV
How are Drug Resistance Mutations
Identified?
• In vitro selection, clinical studies, site-directed
mutagenesis
BUT…
• Drug resistance mutations identified during drug
development (esp. in vitro) may not be the most
relevant mutations in clinical settings
• Mutations that are sufficient to cause drug resistance
may not be necessary to effect drug resistance
• Cross-resistance due to mutations selected by
related drugs
Mesure de Résistance Phenotypique
IC50: Concentration of drug required to inhibit viral replication by 50%.
Reference: wild-type reference strain NL4-3
% inhibition
Fold Change = _IC50 patient_
IC50 reference
Log concentration of drug
Chappey 02/23/09
Analysis Univariée des mutations
Wild-type
Mutant, mixed
Mutant
To determine which mutations
are associated with High or Low
TPV IC50 Fold Change
Fold-change
- Fisher’s Exact test
with the Benjamini correction
for multiple tests (for each
mutation)
-Wilcoxon–Mann–Whitney test
For comparison of median FC
Trade off between Model Complexity, Predictive accuracy
and Biological Descriptive Meaning
Genotype Rules and
Mutation Score
Genotype Rules
ML Regression
SVM
MLR: Multiple Linear
Regression
Neural
Network
Model Predictive Accuracy
SVM: Non-linear
Support Vector Machine
2.5
R² = 0.858
2
1.5
1
Series1
0.5
Linear (Series1)
0
-1
-0.5
0
-0.5
Biological Descriptive Meaning
-1
-1.5
39
0.5
1
1.5
2
2.5
De la bulle des Dot-Com … aux
Subprimes
Embauche
March 10, 2000
licenciement #2
licenciement #1
NIH
Grant
400K
Grant
2m
NIH
Grant
400K
Introduction en bourse
2009
Chart of NASDAQ closing values from 1994 to 2008
Small Business Innovation Research
Grants
NIH Grants
Title
SBIR
Phase I
“HIV Phenotype/Genotype
Database Resources”
SBIR
Phase I
“HIV-1 Envelope
phenotype/genotype
database resources”
Dates
Resume
$
Aug. 2003 –
July 2005
This grant supported the development
of a relational database populated
with phenotypic and genotypic drug
resistance data collected from a large
number (>80,000) of HIV-1 patient
isolates. Statistical and analytical
query tools were developed to derive
highly accurate genotypic-phenotypic
correlations.
400.000
May 2004 –
Apr. 2006
The goal of the project was to create,
populate and exploit an HIV-1
envelope database comprised of high
quality data derived from genotypic
and phenotypic assays recently
developed at Monogram Biosciences
to characterize and evaluate entry
inhibitors and vaccines
400.000
June 2007 –
May 2010
The goal of the project was to
implement a web-based database
retrieval system to search the
Monogram HIV drug resistance
database to support clinical
management of HIV/AIDS patients
and development of novel
therapeutics.
.
SBIR
Phase II
“The Development of a
Web-based Data Retrieval
System for HIV Therapy
Guidance”
2.000.000
Bilan
• (+) Organisation du travail dans un societe privee
– Respect des délais
– Coaching des collaborateurs
– Concrétisation de projets i.e. rédiger des projets aboutissant
a un financement, et donc a une réalité
• (!) Application des connaissances acquises
– Utilisation de R, Perl …
• (-) Occasions manquées
– Insuffisante priorité accordée a ma carriere au sein de la
société (a la rue vs. promue)
Genentech Roche
Senior Biostatistician
• Genentech : 11 000 employes
– Produits : les anticorps
therapeutiques
–
Protropin®
founded
1976
1987
1993
1996
1997
1998
2000
2001
2003
2004
®
tablets
1990
Actimmune
2006
2010
Histoire de la collaboration entre
Genentech et Roche
Page 20
Roche exercises its option to cause Genentech to redeem
its outstanding special common shares not owned by
Roche.
At the Roche Institute of Molecular Biology
a pure interferon alfa is isolated. Roche
Nutley and Genentech start work on a joint
project to produce a genetically engineered
version of the substance.
1980
1990
Roche announces its intent to publicly sell up to 19
percent of Genentech shares and continue Genentech as
a publicly traded company on the NYSE (symbol: DNA)
with independent directors.
Roche signs license agreement to sell Genentech’s
products in ex-U.S. markets.
1999
Genentech and Roche complete a
$2.1 billion merger, and Genentech
continues to trade on the NYSE.
2009
Pour maladies virales
-HIV: Saquinavir SQV
-HCV: Inhibiteurs de
polymerase et de
protease en Phase 2
-Grippe: Tamiflu (postmarketing)
Roche and Genentech announce
that they have signed a merger
agreement, and Genentech
becomes a wholly owned
member of the Roche Group.
Personalized Health Care
- Are We there Yet?
Mark Lackner
What is our role as Statisticians?
How/when do we get involved?
46
The Drug/Diagnostic Co-development
Early stage
research
•Assess
need for Dx
•Initiate
selected
programs
Late stage
research
•Establish Dx
hypothesis
•Identify Dx marker
candidates
•Preclinical
validation
Developmental
Research
•Develop clinical
Dx Strategy
(DxST)
•Develop in house
assays in Ph I
Phase I/II/III
• Dx Biomarker
validation
•Develop validated Dx
assay with partner
•Phase III strategy and
implementation
•Risk mitigation plans
Research/Research Dx
Development Dx/PDB
Companion Dx
Drug
+
Companion
Dx
Test
Ce qui me reste a faire…
• Epouser un milliardaire americain
– George Soros
– Warren Buffet
– Donald Trump
• Monter une start-up Biotech
– Et la revendre a Pfizer pour 18 mds d’Euros
– Ensuite racheter l’UPMC
• Chirurgie esthetique
• GIS
ArcGIS – Epidemie de grippe
Back-up Slides
Mark Lackner
52
The Drug/Diagnostic Co-development
Early stage
research
•Assess
need for Dx
•Initiate
selected
programs
Late stage
research
•Establish Dx
hypothesis
•Identify Dx marker
candidates
•Preclinical
validation
Developmental
Research
•Develop clinical
Dx Strategy
(DxST)
•Develop in house
assays in Ph I
Phase I/II/III
• Dx Biomarker
validation
•Develop validated Dx
assay with partner
•Phase III strategy and
implementation
•Risk mitigation plans
Research/Research Dx
Development Dx/PDB
Companion Dx
Drug
+
Companion
Dx
Test
Virus susceptibility to antiretroviral drugs
allows for the control of the infection
Antiviral drug susceptibility correlates with virologic outcome
HIV resistance: occurs when HIV changes or mutates
so it can escape the effect of an antiretroviral drug
-> choosing an ART regimen in light of resistant HIV
-> resistance testing
Deeks S. JID, 1999;179:1375–81
Agenda
•
Phenotype (PT) and genotype (GT) assays require bioinformaticsbased interpretation algorithms to interpret a patient virus as resistant
(R) or susceptible (S) to a drug
•
Phenotype assay
measure of the ability of a virus to replicate in presence of a drug
– Cut-offs are used to categorize the PT measure as drug Resistant
or Susceptible
•
Genotype assay
– provides the list of mutations present in a virus pool and differing
from the wild-type drug-sensitive virus
– An algorithm is used to recognize the key mutations associated
with resistance from patient-specfic polymorphism
Application using RESIST trial for
tipranavir TPV
•
Boehringer Ingelheim Protease Inhibitor Aptivus® (tipranavir)
•
The RESIST trial evaluated Aptivus® (tipranavir) in treatmentexperienced HIV-1 infected patients
•
Baseline samples selected were:
1. The study regimen did not include enfuvirtide
2. Where the study PI/r was not a continuation of the prestudy PI/r
•
Endpoint: Viral Load reduction at week 4
Phenotype Assay: Technical Process
1. Isolating the viral RNA for Protease and Reverse Transcriptase
2. Constructing the test vector
3. Producing and testing the virus
RESISTANCE TEST VECTOR DNA
PR
RT
IN
Patient-Derived Segment
LUCIFERASE
Indicator Gene
Petropoulos CJ, ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Apr. 2000, p. 920–928
Phenotype Resistance Interpretation
-drug level at which a patient’s probability of
treatment failure increases.
-Based on outcome data from clinical trials.
Biological cut-off
-based on natural variability of wild-type viruses
from treatment-naïve HIV-1 infected patients
- 99th percentile of the IC50 FC distribution
-Requires a large number of wild-type samples.
Assay/technical cut-off
-Based on assay variability with repeated testing of
patient samples
Clinical Relevance
Clinical cut-off
Highest
Moderate
HIGHLY CONFIDENTIAL -- NOT FOR
58
Conclusion 1
• 2 week process that may fail in case of viruses with low
replication capacity
• PT may not capture the resistance in case of minor
populations of resistant variants that are selected by the
drug pressure
• Phenotypic Cutoffs caveats
– Biological cutoffs are assay specific
– Clinical cutoffs are method dependent
Genotype assay and
Rule-based interpretation
•
PROTEASE (1-99) and REVERSE TRANSCRIPTASE (1-305)
•
Validated for samples with viral loads  500 copies/mL
•
Use of multiple primers : Redundancy of 2 to 5 sequence fragments
•
Detects all mutations and mixtures from co-existing populations of virus
(as minor as 10-30%)
Patient virus population (quasispecies)
Clone ID
E04_101157_c07
E04_101157_c08
E04_101157_c09
E04_101157_c13
E04_101157_c19
E04_101157_c21
E04_101157_c23
E04_101157_c25
E04_101157_c26
E04_101157_c30
E04_101157_c34
Virus
tropism
Peptide sequence
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC
X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC
X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGNIRQAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHC
R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC
HIGHLY CONFIDENTIAL -- NOT FOR
61
HIGHLY CONFIDENTIAL -- NOT FOR
62
HIGHLY CONFIDENTIAL -- NOT FOR
63
HIGHLY CONFIDENTIAL -- NOT FOR
64
HIGHLY CONFIDENTIAL -- NOT FOR
65
HIGHLY CONFIDENTIAL -- NOT FOR
66
Conclusions 2
- Genotype algorithms evolve over time with increased clinical experience
and more clinical data on cross-resistance and reverse susceptibility
-Use of large database combining phenotype and genotype results to
generate more accurate genotype interpretive algorithms
-Minimizing PT-GT Discordance : tradeoff between false negatives
(PT-S GT-R) and the false positives (PT-R GT-S)
-PT-R GT-S
-New mutations
-Cross-resistance
-PT-S GT-R
-Suppression of resistance or “re-sensitization”
-Presence of mixtures
-Use of more complex prediction models yield to more accurate
algorithms but with less biological descriptive meaning
Monogram Technologies for
Resistance
Testing
Patient virus
RT-PCR
PR-RT DNA
Vector Assembly
GeneSeq™
PhenoSense™
RESISTANCE TEST VECTOR DNA
PR
RT
IN
LUCIFERASE
Indicator Gene
Patient-Derived Segment
Transfection
Sequencing
Resistance Mutations
Recombinant Virus
Infection
Measure of Drug Susceptibility
Rules for
genotype
Interpretation
Prediction of Drug
Susceptibility
Pheno-Geno
Database
Categorize
R if FC > cut-off
S if FC < cut-off
Categorization of Drug
Susceptibility
Discussion
• Interpretation of phenotypic (cutoffs) and genotypic
(algorithms) resistance assays is an evolving science
• Large databases of phenotypic and genotypic
information are essential tools to understand and
improve discordance rates
• The use of both types of assay in many cases
provides the most complete picture of an individual
patient’s virus resistance profile
Acknowledgements
Increasing Genetic Complexity
Phenotypic testing
Utility
Genotypic testing
Treatment rounds
•
•
All my colleagues at Monogram Biosciences (Clinical Reference
Laboratory and Research and Development)
And my collaborators (Steve Deeks, UCSF, Andy Zolopa, Stanford,
Sebastian Bonhoeffer, Swizerland, R. Shafer ,Stanford..)
Biological Cut-off: Definition
-Biological cut-off: based on natural variability of wild-type viruses
from treatment-naïve HIV-1 infected patients (infected by patient
who is also drug naïve)
-When the treatment history is not known, wild-type virus “WT” is
defined by the absence of any drug-selected mutation in PR or
RT:
-PR: 23, 24, 30, 32, 33F, 46, 47, 48, 50, 54, 82 (not 82I), 84, 90
-RT: 41, 65, 67, 69 (incl. ins.), 70, 74, 75, 100, 101E or P, 103N or
S, 106A or M, 151, 181, 184, 190, 210, 215F or Y, 219, 225, 227,
230, 236
Biological Cut-off for TPV
Natural Variation of TPV FC Among “Wild-type” Samples
70
60
Count
50
40
30
20
99th percentile = 2.1
10
0
-.8
0.16
-.6
0.25
-.4
0.40
-.2
0
.2
0.63
1.0
1.6
TPV fold change
N=2848 , no PI or RTI ‘recognized‘ resistance mutations
.4
2.5
.6
4.0
Genotype Interpretation
for Tipranavir (TPV)
•TPV susceptibility based on genotype uses an algorithm that
counts mutations associated with reduced in vitro
susceptibility or in vivo virological response.
•The “TPV mutation score” was derived from analysis of a
limited number of patient samples collected during phase 2
and 3 clinical trials and considers the following mutations:
L10V, I13V, K20M, R, or V, L33F, E35G, M36I, K43T, M46L,
I47V, I54A, M, or V, Q58E, H69K, T74P, V82L or T, N83D,
I84V1.
Kohlbrenner et al., HIV DART, 2004
Mutations Associated with PT-R GT-S
Mutation
N mut
Odds ratio†
P-Value
I54A*
16
15.1
0.00253
A71L
18
8.0
0.00497
V11L
20
4.0
0.03667
V82T
65
2.8
0.00076
I47V
122
2.8
<0.0001
G73T
66
2.5
0.00329
L89V
105
2.3
0.00034
I84V
356
2.2
<0.0001
V32I
169
2.0
0.00008
M36L
77
2.0
0.02024
I66
94
1.9
0.01722
D60E
217
1.6
0.00265
K55R
169
1.6
0.01546
L90M
787
1.3
<0.0001
M46I
495
1.3
0.00424
L10I
625
1.2
0.02199
*underlined
mutations in
existing TPV
mutation score
† the ratio of %
H samples with
the mutation to
% L samples
with the
mutation
Phenotype-Clinical:
Week 4 HIV-1 VL Change
Change to TPV
vs. Baseline IC50 Fold
HIV RNA reduction (log10)
(N= 176)
-0.3
-0.3log10 c/mL
R²=0.22, p<0.0001
0.1
1
2
3
FC Tipranavir (log10)
10
100
Clinical Cutoffs: Definitions
Probability of response
Lower clinical cutoff: The IC50 fold change at
which the HIV RNA response first begins to
decline
Upper clinical cutoff:
The fold change above
which a clinically meaningful
HIV RNA response (>0.3
log10) is unlikely
Zone of
Intermediate
Response
Fold Change
Clinical Cutoffs: Methods
Lower clinical cut-off
Comparison of HIV RNA responses between two
adjacent groups across a moving IC50 FC cut-off
(Kruskal-Wallis test)
Upper clinical cut-off
1. Phenotypic susceptibility scoring to account for
background effect
2. Define the HIV RNA change attributable to the PI/r
3. Define the fold change associated with an HIV RNA
reduction of -0.3 log10 copies/mL
Chappey 02/23/09
LCCO: First difference from reference
Expanding Window method
Cutoff=1.0, p=0.65 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Expanding Window method
Cutoff=1.1, p=0.18 (n=36)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Expanding Window method
Cutoff=1.2, p=0.095 (n=41)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Expanding Window method
Cutoff=1.3, p=0.92 (n=44)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Expanding Window method
Cutoff=1.4, p=0.16 (n=49)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Expanding Window method
Cutoff=1.5, p=0.0006 (n=59)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window Method
Cutoff=1.0, p=0.65 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window Method
Cutoff=1.2, p=0.97 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window method
Cutoff=1.3, p=0.64 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window method
Cutoff=1.4, p=0.89 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window method
Cutoff=1.5, p=0.23 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window method
Cutoff=1.6, p=0.085 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
LCCO: First difference from reference
Fixed Window method
Cutoff=1.7, p=0.003 (n=31)
HIV RNA reduction (log10)
Median HIV RNA
0.1
1
2
3
FC Tipranavir (log10)
10
100
Comparing LCCO with the Biological Cut-off
Natural Variation of TPV FC Among “Wild-type” Samples
70
In order to minimize
misclassification of
wildtype isolates as
resistant a TPV/r LCO
at 2.0 was chosen
60
Count
50
40
LCCO = 1.5
30
20
99th percentile = 2.1
10
0
-.8
0.16
-.6
0.25
-.4
0.40
-.2
0
.2
0.63
1.0
1.6
TPV fold change
N=2848 , no PI or RTI ‘recognized‘ resistance mutations
.4
2.5
.6
4.0
Clinical Cutoffs: Methods
Lower clinical cut-off
Comparison of HIV RNA responses between two
adjacent groups across a moving IC50 FC cut-off
(Kruskal-Wallis test)
Upper clinical cut-off
1. Phenotypic susceptibility scoring (PSS) to account
for background effect
2. Define the HIV RNA change attributable to the PI/r
3. Define the fold change associated with an HIV RNA
reduction of -0.3 log10 copies/mL
Chappey 02/23/09
UCCO Determination:
Calculate the proportion of HIV RNA change attributed to PI/r
% HIV RNA reduction attributable to each drug:
2 NRTI
2 NRTI
50%
TPV/r
PSS=0
PSS=1
PSS=1
PSS=1
TPV
100%
TPV/r
Adjust HIV RNA change attributable to TPV/r
TPV
50%
Phenotypic Susceptibility Scoring
(PSS)
FC=0.4
Hypersusceptible
Lower CCO
Susceptible
Upper CCO
Intermediate
Resistant
PSS score by Category
HS*
Susceptible
Intermediate**
Resistant
PI
1.5
1
<1  >0
0
NNRTI
1.5
1
<1  >0
0
NRTI
0.75
0.5
<0.5  >0
0
*HS=hypersusceptible (FC <0.4), ** PSS in the intermediate zone is calculated on a continuous scale
Drugs continued from the pre-study regimen were not scored
Scatter plots of drug susceptibility
versus week 4 HIV RNA change
TPV FC (log10) versus unadjusted Week 4
HIV-1 RNA (log10) change,
N=176, (R²=0.22, p<0.0001)
-
0.3log10c/mL
Regimen phenotypic susceptibility score (PSS)
versus HIV RNA change (R²=0.19, p<0.0001)
TPV FC versus Adjusted
Week 4 HIV RNA Change
Adjusted log HIV RNA reduction
LCO=2.0, PSS 0 FC=15, censoring data >15
R²=0.27, p<0.0001
-0.3 log
0.1
1
2
3
8 10
log-transformed FC TIPRANAVIR
15
30
Adjusted Week 4 HIV RNA outcomes
by TPV susceptibility category
TPV FC category
Mean (median) HIV
RNA (log10) change
Range
N
P
Susceptible
Intermediate
Resistant
<2.0
2.0-8.0
>8.0
-1.3 (-1.2)
-0.6 (-0.3)
-0.1 (0.0)
-2.8, -0.3
-2.6, +0.6
-1.6,+0.3
78
72
26
<0.0001
0.002
What is our role as
Statisticians?
How/when do we get
involved?
What is Our Responsibility
• We are strategic partners
– PHC strategy is part of the Development Plan
• Embrace the PHC strategy
• Engage the DST in strategic/prioritization/timelines discussions
related to PHC
– Raise the right issues
– Plan for resources
• Work with DST and your manager
• Network with the Biomarker Experts/Dx sub-teams
– Be proactive/Stay informed
• Get Involved!
What is our role as Statisticians?
How/when do we get involved?
Mark Lackner
100
The Drug/Diagnostic Co-development
Early stage
research
•Assess
need for Dx
•Initiate
selected
programs
Late stage
research
•Establish Dx
hypothesis
•Identify Dx marker
candidates
•Preclinical
validation
Developmental
Research
•Develop clinical
Dx Strategy
(DxST)
•Develop in house
assays in Ph I
Phase I/II/III
• Dx Biomarker
validation
•Develop validated Dx
assay with partner
•Phase III strategy and
implementation
•Risk mitigation plans
Research/Research Dx
Development Dx/PDB
Companion Dx
Drug
+
Companion
Dx
Test
PHC strategy
Development Strategy
PHC Strategy
• Strong Dx hypothesis
• No activity in Dx-
• Strong Dx hypothesis
• Some activity in Dx-
• No strong Dx
hypothesis
• Exploratory Stage
Development Strategy
• Patient selection
through all phases of
development
• Complex, larger phase IIs
with stratification
• Complex phase IIIs
• No selection or
stratification
• Possible data mining
trap
Impact on components of CDP
• Target product profile
– Parallel development of companion diagnostic
• Phase I trials
– Selection for quick signal seeking
• Phase II trials
– Complex issues become more complex
– More unknowns, more questions to answer
• Phase III trials
– Clinical Validation of Dx
– Design depends on Phase II outcome
•
Selection, stratification or all-comers
Phase II Considerations
•
•
•
•
Objective: simultaneous Rx/Dx evaluation
Scientific rationale and pre-clinical data - main determinants of the
scenario prior to Phase II
Statistical considerations
–
Co-primary endpoints
–
Value added and feasibility of stratification
–
Defining cut-offs for continuous biomarker
–
Go/No Go decision algorithm
Dedicated studies to investigate assay or biomarker properties
– Reproducibility, prevalence, prognostic value
Phase III Considerations
•
•
•
Study Objective
– Assess/determine risk/benefit
– Clinical Validation of Dx
Implementation issues
– Analytically validate Dx assay before applying it to specimens in pivotal
trials
– Accruing / prospective stratification based on non-final assay – can
result in discordance
Analysis method
– Test two hypothesis,
• All comers
• Dx positive subgroup
• Appropriately control for type I error
– Clearly define your decision tree – there are no “freebies”
End of Phase III Decision Criteria
Phase III outcome
Not statistically significant in all
comers
Statistically significant in all comers
All comers claim if no diff. b/w Dx- &
Dx+ groups
Statistically significant in
Dx+ group
SELECTION CLAIM
Greater benefit claim if clinically
meaningful diff. b/w Dx- & Dx+
Selection Claim if no improvement in
Dx- group
Old Drugs – New Tests
•
•
•
•
•
Biomarker not known at the time of study initiation
Data not analyzed with that biomarker as part of the hypothesis
New scientific advancements/new technologies
Biomarker discovery – generation of new hypotheses
Prospective-Retrospective Study
Exploratory Analysis
Prospective/Retrospective Study
• Completed or post-interim-analysis trial
– Patient samples collected prior to treatment initiation
– Clinical outcomes data unblinded and analyzed
without the biomarker data
– Diagnostic hypothesis/analysis plan prospectively specified
– Analysis is retrospective
Components of good biomarker analysis plan
•
•
•
•
•
•
Role of randomization - fairness of comparison
Marker availability – impact of convenience samples
– Bias due to missing data
Marker performance
– Marker performance and prevalence may explain study to study
heterogeneity
Statistical control of false positive conclusions –
– How many hypothesis
– How many outcomes
Model selection
– Over-fitting can lead to bias
Validation methods
– Data to generate the hypothesis vs. data to confirm the hypothesis
Summary
• Companion diagnostics are at the heart of personalized health care
– Predictive claims rely on understanding the effect of the drug in
biomarker positive and negative patients
– Optimal approach: Adequate and well-controlled trials,
prospectively designed to assess risk/benefit in biomarker
subgroups
– Late emergence of critical biomarkers for existing drugs revision of drug’s use
• As strategic partners, we need to be involved in all stages of the
co-development process