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

Trajet d'une expatriée : de la phylogénie du VIH au traitement de la grippe, et de Paris à San

Francisco

Colombe ChappeyDEA 1986, PhD 1992

Modélisation

DEA 1986

Essais Cliniques

Bioinformatique

Reconnaissance de Formes(These ’92)

Epidémiologie

Epidémiologie Moleculaire

StatistiquesCliniques

Analyse d’images

Programmation(Computer Science)

Transmissionde la grippe

PersonalizedHealth Care

(Soins personnalisés)

Analyse Exploratoire

Bio-marqueurs predictifs

Modélisation

DEA 1986

Essais Cliniques

Bioinformatique

Reconnaissance de Formes(These ’92)

Epidémiologie

Epidémiologie Moleculaire

VIH

StatistiquesCliniques

Analyse d’images

Programmation(Computer Science)

Transmissionde la grippe

PersonalizedHealth Care

(Soins personnalisés)

Analyse Exploratoire

Bio-marqueurs predictifs

Au cours de mon ‘trajet’…

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 motifsappliquée a la comparaison de

sequences biologiques

…A G G T T G C……A G G T C…

Comparaison de séquences nucleiques/proteines-> Alignement des éléments/motifs en commun-> pondérer les différences/mutations et les insertions/deletions

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

• Le nombre de sequences de VIH a tres vite augmente.

• Certaines séquences sont plus similaires que d’autres

1988

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

Chappey C, Danckaert A, Dessen P, Hazout S. MASH an interactive multiple alignment and consensus sequence construction. Comp. Applic. Biosci. 1991; 7:195-202.

Homogénéité et hétérogénéité par region

Distance entre séquencesClassification

time

Cas du Dentiste - 1990

Prediction de Structure/function de la Proteine d’Enveloppe du VIF

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.

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)

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 2• Application du logiciel d’alignement

– Human immunodeficiency virus type 1 4– Transmission HIV mother-infant 5– Simian / human T-cell lymphotropic virus type 1 3– Simian immunodeficiency virus 1– Feline immunodeficiency virus FIV 2

(-) Occasions manquées• Commercialisation du logiciel d’alignment (alors que CLUSTAL…)• Analyses non-publiées

National Center Biotechnology Information

(GenBank)

National Institutes of Health

Histoire de GenBank et NCBI

BLAST (Basic Local Alignment Search Tool)

international computer database of nucleic acid sequence data – Los Alamos Natl Lab, NM (NSF)

1979

Wilbur and LipmanAlgorithme de recherche de similarites entre sequences

GenBank demenage a NIH

Human EST

Human Genome

Programmation d’un outil d’annotation et de Soumission de Séquences

Biologiques a GenBankLa 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

http://www.ebi.ac.uk/Sequin/QuickGuide/sequin.htm

1995

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):300-2.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 non-traités…)

– 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:

Hopital+

Laboratoire d’Analyses

DB

Algorithm

Patient Resistance

Report

• ~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

Test de Phenotypeteste la capacite’ de chaque

antiviraux de diminuer la FONCTION de la protein virale cible de l’antivirale.

ClivageProcessing

Folding

Database de ViroLogic

Génotype

PhénotypeIC50 fold change

Response CliniqueReduction de la

charge viraleSmall studies(n ~ 100’s)clinical cut-off pour le phenotype

Small studies(n ~ 100’s) PT-GT database

(n > 100,000)

Identification de mutation associees a la resistance du VIH aux antiviraux

codon 184 R(=A/G)TG -> M/V

Calling Bases and Mixtures from Raw Sequence (ABI Chomatogram) Data

Zolopa, A. R. et. al. Ann Intern Med 1999;131:813-821

Fréquences des Mutations par Réponse virologic apres 2 semaines

Régles d’interprétation du Genotype

Resistance Collaborative Group (DeGruttola et al., 2000)Initially used in GeneSeq assay, with some modificationsExpert 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 notesInternational AIDS Society IAS (Hirsch et al., JAMA 2000; 2008 updates) http://iasusa.org Expert consensus; updated frequently

Interprétation du Génotype viral

V82AV32I

L90M

A71V

I47V

I84VV82F

M46IG48V

D30N I50V

I54V

N88S

. | . | . | . | . | . | . | . | . |Wild-type: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNFPatient PQIALWQRPLVTIKIGGQLKEALLDTGADNTILEEMNLPGRWKPKMVGGIGGFVKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF

V32I I47V

D30N T4A

I54V

Patient virus genotype

Drug Resistance associated Mutations (RAMs)

Regles d’interprétation du Génotype

D30NResistance to NPV

I47V, I54VIntermediate resistanceto fAMP, TPV

How are Drug Resistance Mutations Identified?

• In vitro selection, clinical studies, site-directed In vitro selection, clinical studies, site-directed mutagenesismutagenesis

BUT… BUT…

• Drug resistance mutations identified during drug Drug resistance mutations identified during drug development (esp. in vitro) may not be the most development (esp. in vitro) may not be the most relevant mutations in clinical settingsrelevant mutations in clinical settings

• Mutations that are sufficient to cause drug resistance Mutations that are sufficient to cause drug resistance may not be necessary to effect drug resistancemay not be necessary to effect drug resistance

• Cross-resistance due to mutations selected by Cross-resistance due to mutations selected by related drugs related drugs

Mesure de Résistance Phenotypique

IC50: Concentration of drug required to inhibit viral replication by 50%.

Fold Change = _IC50 patient_ IC50 reference Reference: wild-type reference strain NL4-3

Chappey 02/23/09

% in

hibi

tion

Log concentration of drug

Analysis Univariée des mutationsFo

ld-c

hange

Wild-typeMutant, mixedMutant

- Fisher’s Exact test with the Benjamini correction for multiple tests (for each mutation)

-Wilcoxon–Mann–Whitney testFor comparison of median FC

To determine which mutations are associated with High or Low TPV IC50 Fold Change

39

Trade off between Model Complexity, Predictive accuracy and Biological Descriptive Meaning

Incr

ea

sing

Biological Descriptive Meaning

Model Predictive Accuracy

Model complexity

Genotype Rules

ML Regression

SVM

R² = 0.858

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-1 -0.5 0 0.5 1 1.5 2 2.5

Series1

Linear (Series1)

Genotype Rules andMutation Score

MLR: Multiple Linear Regression

SVM: Non-linear Support Vector Machine

Neural Network

De la bulle des Dot-Com … aux Subprimes

Chart of NASDAQ closing values from 1994 to 2008

March 10, 2000

Introduction en bourse

licenciement #1

NIHGrant 400K

NIHGrant 400K

Grant 2m

licenciement #2Embauche

2009

Small Business Innovation Research Grants

NIH Grants Title Dates Resume $

SBIR

Phase I

“HIV Phenotype/Genotype Database Resources”

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

SBIR

Phase I

“HIV-1 Envelope phenotype/genotype database resources”

.

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

SBIR

Phase II

“The Development of a Web-based Data Retrieval System for HIV Therapy

Guidance”

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.

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–

1976 1987 1998 2000 2001 2003 2004 20061993 1996 1997

founded

tablets

®

2010

Protropin®

1990Actimmune

Page 20Histoire de la collaboration entre

Genentech et Roche

1980 1990 1999 2009

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.

Genentech and Roche complete a $2.1 billion merger, and Genentech continues to trade on the NYSE.

Roche exercises its option to cause Genentech to redeem its outstanding special common shares not owned by Roche.

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.

Roche and Genentech announce that they have signed a merger agreement, and Genentech becomes a wholly owned member of the Roche Group.

Pour maladies virales-HIV: Saquinavir SQV-HCV: Inhibiteurs de polymerase et de protease en Phase 2-Grippe: Tamiflu (post-marketing)

Personalized Health Care- Are We there Yet?

46What is our role as Statisticians?

How/when do we get involved?The Drug/Diagnostic Co-development

•Establish Dx hypothesis •Identify Dx marker candidates•Preclinical validation

•Develop clinical Dx Strategy (DxST)

•Develop in house assays in Ph I

•Assess need for Dx•Initiate selected programs

Phase I/II/IIIDevelopmentalResearch

Early stageresearch

Late stage research

• 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

CompanionDx

Test

+

Mark Lackner

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

52

The Drug/Diagnostic Co-development






Early stageresearch

Late stage research



Development Dx/PDB

Companion Dx

Drug

CompanionDx

Test

+

Mark Lackner

Virus susceptibility to antiretroviral drugs allows for the control of the infection

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

Antiviral drug susceptibility correlates with virologic outcome

Deeks S. JID, 1999;179:1375–81

Agenda

• Phenotype (PT) and genotype (GT) assays require bioinformatics-Phenotype (PT) and genotype (GT) assays require bioinformatics-based interpretation algorithms to interpret a patient virus as resistant based interpretation algorithms to interpret a patient virus as resistant (R) or susceptible (S) to a drug(R) or susceptible (S) to a drug

• Phenotype assayPhenotype assaymeasure of the ability of a virus to replicate in presence of a drugmeasure of the ability of a virus to replicate in presence of a drug

– CCut-offsut-offs areare used to categorize the PT measure as drug Resistant used to categorize the PT measure as drug Resistant or Susceptibleor Susceptible

• Genotype assayGenotype assay

– provides the list of provides the list of mutations present in a virus pool and differing mutations present in a virus pool and differing from the wild-type drug-sensitive virusfrom the wild-type drug-sensitive virus

– An algorithm is used to recognize the key mutations associated with An algorithm is used to recognize the key mutations associated with resistance from patient-specfic polymorphism resistance from patient-specfic polymorphism

Application using RESIST trial for tipranavir TPV

• Boehringer Ingelheim Protease Inhibitor Aptivus® (tipranavir)Boehringer Ingelheim Protease Inhibitor Aptivus® (tipranavir)

• The RESIST trial evaluated Aptivus® (tipranavir) in treatment-The RESIST trial evaluated Aptivus® (tipranavir) in treatment-experienced HIV-1 infected patients experienced HIV-1 infected patients

• Baseline samples selected were:Baseline samples selected were:1.1. The study regimen did not include enfuvirtideThe study regimen did not include enfuvirtide2.2. Where the study PI/r was not a continuation of the prestudy PI/rWhere the study PI/r was not a continuation of the prestudy PI/r

• Endpoint: Viral Load reduction at week 4 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 vector3. Producing and testing the virus

PR

Patient-Derived Segment Indicator Gene

RT IN

LUCIFERASE

RESISTANCE TEST VECTOR DNA

Petropoulos CJ, ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Apr. 2000, p. 920–928

Phenotype Resistance Interpretation

Clinical cut-off-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

Clin

ical

Rel

evan

ce

Highest

Moderate

HIGHLY CONFIDENTIAL -- NOT FOR DISTRIBUTION

58

Conclusion 1

• 2 week process that may fail in case of viruses with low 2 week process that may fail in case of viruses with low replication capacityreplication capacity

• PT may not capture the resistance in case of minor PT may not capture the resistance in case of minor populations of resistant variants that are selected by the populations of resistant variants that are selected by the drug pressuredrug pressure

• Phenotypic Cutoffs caveatsPhenotypic Cutoffs caveats– Biological cutoffs are assay specific Biological cutoffs are assay specific – Clinical cutoffs are method dependentClinical cutoffs are method dependent

Genotype assay and Rule-based interpretation

• PROTEASE (1-99) and REVERSE TRANSCRIPTASE (1-305)PROTEASE (1-99) and REVERSE TRANSCRIPTASE (1-305)

• Validated for samples with viral loads Validated for samples with viral loads 500 copies/mL 500 copies/mL

• Use of multiple primers : Redundancy of 2 to 5 sequence fragmentsUse of multiple primers : Redundancy of 2 to 5 sequence fragments

• Detects Detects all mutations and mixturesall mutations and mixtures from co-existing populations of virus from co-existing populations of virus (as minor as 10-30%)(as minor as 10-30%)

Clone IDVirus

tropism Peptide sequence E04_101157_c07 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHCE04_101157_c08 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHCE04_101157_c09 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHCE04_101157_c13 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHCE04_101157_c19 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHCE04_101157_c21 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHCE04_101157_c23 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHCE04_101157_c25 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGNIRQAHCE04_101157_c26 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHCE04_101157_c30 X4 CTRPSNHTRKRVTLGPSRVYYTTGEITGDIRRAHCE04_101157_c34 R5 CTRPSNNTRKSINMGPGRAFYTTGEIIGDIRQAHC

Patient virus population (quasispecies)


61


62


63


64


65


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

GeneSeq™

Sequencing

Resistance Mutations

Prediction of DrugSusceptibility

Rules forgenotype

Interpretation

PhenoSense™

Recombinant Virus

Transfection

Measure of Drug Susceptibility

Infection

Patient virus

PR-RT DNA

RT-PCR

Vector Assembly

Categorization of DrugSusceptibility

Categorize R if FC > cut-offS if FC < cut-off

PR

Patient-Derived Segment Indicator Gene

RT IN

LUCIFERASE

RESISTANCE TEST VECTOR DNA

Pheno-Geno Database

Discussion

• Interpretation of phenotypic (cutoffs) and genotypic Interpretation of phenotypic (cutoffs) and genotypic (algorithms) resistance assays is an evolving science(algorithms) resistance assays is an evolving science

• Large databases of phenotypic and genotypic Large databases of phenotypic and genotypic information are essential tools to understand and information are essential tools to understand and improve discordance ratesimprove discordance rates

• The use of both types of assay in many cases The use of both types of assay in many cases provides the most complete picture of an individual provides the most complete picture of an individual patient’s virus resistance profilepatient’s virus resistance profile

Acknowledgements

Genotypic testing Genotypic testing

Phenotypic testing

Treatment rounds

Utility

Increasing Genetic Complexity

• All my colleagues at Monogram Biosciences (Clinical Reference All my colleagues at Monogram Biosciences (Clinical Reference Laboratory and Research and Development) Laboratory and Research and Development)

• And my collaborators (Steve Deeks, UCSF, Andy Zolopa, Stanford, And my collaborators (Steve Deeks, UCSF, Andy Zolopa, Stanford, Sebastian Bonhoeffer, Swizerland, R. Shafer ,Stanford..)Sebastian Bonhoeffer, Swizerland, R. Shafer ,Stanford..)

-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: Definition

Biological Cut-off for TPV

0

10

20

30

40

50

60

70

Co

un

t

-.8 -.6 -.4 -.2 0 .2 .4 .6

0.16 0.25 0.40 0.63 1.0 1.6 2.5 4.0TPV fold change

N=2848 , no PI or RTI ‘recognized‘ resistance mutations

Natural Variation of TPV FC Among “Wild-type” Samples

99th percentile = 2.1

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

R²=0.22, p<0.0001

0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)

FC Tipranavir (log10)

-0.3log10 c/mL

-0.3

(N= 176)

Phenotype-Clinical:Week 4 HIV-1 VL Change vs. Baseline IC50 Fold Change to TPV

Pro

bab

ility

of

resp

on

se

Fold Change

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

Clinical Cutoffs: Definitions

Clinical Cutoffs: Methods

Lower clinical cut-offComparison of HIV RNA responses between two adjacent groups across a moving IC50 FC cut-off (Kruskal-Wallis test)

Upper clinical cut-off1. Phenotypic susceptibility scoring to account for

background effect2. Define the HIV RNA change attributable to the PI/r3. 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

0.1 1 10 1002 30.1 1 10 1002 3

Cutoff=1.0, p=0.65 (n=31)

HIV

RN

A r

ed

uc

tio

n (

log

10)


Median HIV RNA


0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)


Cutoff=1.1, p=0.18 (n=36) Median HIV RNA


0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)




0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

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uc

tio

n (

log

10)




0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)




0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)



LCCO: First difference from reference Fixed Window Method

0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)



LCCO: First difference from reference Fixed Window Method

0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

10)



LCCO: First difference from reference Fixed Window method

0.1 1 10 1002 30.1 1 10 1002 3

HIV

RN

A r

ed

uc

tio

n (

log

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-.8 -.6 -.4 -.2 0 .2 .4 .6

0.16 0.25 0.40 0.63 1.0 1.6 2.5 4.0TPV fold change

N=2848 , no PI or RTI ‘recognized‘ resistance mutations

Comparing LCCO with the Biological Cut-off

Natural Variation of TPV FC Among “Wild-type” Samples

99th percentile = 2.1

LCCO = 1.5

In order to minimize misclassification of wildtype isolates as resistant a TPV/r LCO at 2.0 was chosen

Clinical Cutoffs: Methods

Lower clinical cut-offComparison of HIV RNA responses between two adjacent groups across a moving IC50 FC cut-off (Kruskal-Wallis test)

Upper clinical cut-off1. Phenotypic susceptibility scoring (PSS) to account

for background effect2. Define the HIV RNA change attributable to the PI/r3. Define the fold change associated with an HIV RNA

reduction of -0.3 log10 copies/mL

Chappey 02/23/09

Adjust HIV RNA change attributable to TPV/r

% HIV RNA reduction attributable to each drug:

TPV50%

50%

PSS=0

UCCO Determination:Calculate the proportion of HIV RNA change attributed to PI/r

PSS=1 PSS=1

PSS=1

2 NRTI

TPV/r

2 NRTI

TPV/r

TPV100%

Phenotypic Susceptibility Scoring (PSS)

Lower CCO Upper CCOFC=0.4

Intermediate ResistantSusceptibleHypersusceptible

Lower CCO Upper CCOFC=0.4

Intermediate ResistantSusceptibleHypersusceptible

0<0.5 >00.50.75NRTI

0<1 >011.5NNRTI

0<1 >011.5PI

ResistantIntermediate**SusceptibleHS*

0<0.5 >00.50.75NRTI

0<1 >011.5NNRTI

0<1 >011.5PI

ResistantIntermediate**SusceptibleHS*

PSS score by Category

*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 4HIV-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

-0.3 log

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log-transformed FC TIPRANAVIR

0.1 1 102 3 308 15

LCO=2.0, PSS 0 FC=15, censoring data >15 R²=0.27, p<0.0001

Adjusted Week 4 HIV RNA outcomes by TPV susceptibility category

267278N

P

Range

Mean (median) HIV

RNA (log10) change

TPV FC category

0.002<0.0001

-1.6,+0.3-2.6, +0.6-2.8, -0.3

-0.1 (0.0)-0.6 (-0.3)-1.3 (-1.2)

>8.02.0-8.0<2.0

ResistantIntermediateSusceptible

267278N

P

Range

Mean (median) HIV

RNA (log10) change

TPV FC category

0.002<0.0001

-1.6,+0.3-2.6, +0.6-2.8, -0.3

-0.1 (0.0)-0.6 (-0.3)-1.3 (-1.2)

>8.02.0-8.0<2.0

ResistantIntermediateSusceptible

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!

100What is our role as Statisticians?

How/when do we get involved?The Drug/Diagnostic Co-development






Early stageresearch

Late stage research



Development Dx/PDB

Companion Dx

Drug

CompanionDx

Test

+

Mark Lackner

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

Statistically significant in Dx+ group

SELECTION CLAIM

All comers claim if no diff. b/w Dx- & Dx+ groups

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

Documents

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