Utillisation des signatures dans les essais · 2016-06-28 · Utillisation des signatures dans les essais cliniques en oncologie S. Michiels CESP, INSERM U1018 ... 2 actives 10 actives

Utillisation dessignaturesdans lesessaiscliniques en oncologie

S.Michiels

CESP,INSERMU1018,UniversityParis-Saclay,UniversityParis-Sud,Villejuif,FranceServicedeBiostatistiqueetd'Épidémiologie,GustaveRoussy,Villejuif,France

[email protected]

Today’s talk

• Prognostic gene signatures

• Predictive gene signatures

• Prospective clinical trials

2

Lessons from early breast cancer: prognostic signatures

Available gene signatures for a price of 400-4175$…• IHC4: 4 genes

• Oncotype Dx: 16 cancer genes

• PAM50 (ROR): 50 genes

• Mammaprint Dx: 70 genes

• Endopredict: 8 cancer genes

• Mapquant Dx (GGI) : 97 genes

• …

European consensus panel (Azim, Michiels Ann Onc 2013): no robust evidence of clinical utility … 3

Pooled gene expression analysis of neoadjuvant chemotherapy trials in breast

cancer: flow chart

Ignatiadis et al JCO 2012 4

OR: odds ratio for a 1-unit changein gene module, adjustedfor clinicopathological factors

FDR: false discovery rate

GGIGene70CIN70Stroma1Stroma2Immune1Immune2RASMAPKPTENAKTmTORPIK3CAIGF1SRCMYCE2F3BetaCatenin

OR1.592.111.470.650.661.761.490.770.811.710.84

10.791.021.131.671.12

(0.91,2.81)(1.12,4.03)(0.88,2.47)(0.38,1.08)(0.4,1.08)

(1.13,2.76)(0.96,2.31)(0.5,1.19)

(0.47,1.38)(1.04,2.85)(0.54,1.3)(0.55,1.8)

(0.46,1.34)(0.62,1.65)(0.73,1.75)(1.06,2.67)(0.69,1.82)

1.0E 012.2E 021.4E 019.7E 029.9E 021.3E 027.4E 022.4E 014.3E 013.7E 024.3E 019.9E 013.8E 019.5E 015.7E 012.9E 026.4E 01



OR0.860.850.760.650.467.135.010.341.041.130.391.3

0.791.740.770.651.68

(0.25,2.72)(0.24,2.8)

(0.22,2.41)(0.25,1.62)(0.17,1.19)

(2.38,25.51)(1.77,16.35)

(0.09,1.12)(0.37,2.84)(0.38,3.31)(0.14,0.99)(0.47,3.69)(0.3,1.94)

(0.73,4.33)(0.31,1.82)(0.21,1.97)(0.68,4.35)




OR3.193.433.381.051.33

1.41.711.810.863.061.780.841.780.631.072.560.43

(1.26,8.72)(1.25,9.66)(1.31,9.35)(0.45,2.42)

(0.57,3.1)(0.59,3.15)

(0.7,4.17)(0.64,5)

(0.32,2.16)(1.1,8.94)

(0.66,5.02)(0.38,1.83)(0.65,4.96)(0.23,1.63)(0.41,2.73)(1.14,5.97)(0.17,1.09)



ER /HER2

HER2+ ER+/HER2

(394 pts, 120 pCR)

(116 pts, 42 pCR) (335 pts, 27 pCR)

0.25 1 5 10 20

0.25 1 5 10 20 0.25 1 5 10 20

95% CI P FDR

95% CI P FDR 95% CI P FDR

Figure 4

Odds Ratio

Odds Ratio Odds Ratio


OR1.7

2.021.610.730.741.921.780.820.851.750.841.010.971.02

1.11.6

0.98

(1.12,2.6)(1.29,3.2)

(1.08,2.42)(0.49,1.06)(0.5,1.07)

(1.36,2.73)(1.25,2.53)(0.57,1.18)(0.56,1.27)(1.18,2.62)(0.59,1.19)(0.67,1.53)(0.65,1.45)(0.71,1.47)(0.78,1.56)(1.12,2.3)

(0.68,1.43)



(845 pts, 189 pCR)

95% CI P FDR

ALL

Odds Ratio

0.25 1 5 10 20

A B

C D

Association of gene modules with pathological complete response after

anthracyclines, beyond clinicopathological factors

Ignatiadis et al JCO 2012

Randomized clinical trials and absolute risk

• Absolute risk reductions in large trials are still the best guide

• Absolute gain from a 33% breast cancer mortality reduction from chemotherapy(chemo) in hormone receptor-positive pts depends on the absolute risks with endocrine therapy alone

Rothwell Lancet 2005, Michiels, Rotolo 2015

Estimated absolute 10-year breast cancer-specific survival

No chemoAbsolute gain from chemo

97% +1%92% +3%85% +5%

We cannot necessarily, perhaps very rarely, pass from (the result of a clinical trial) to stating exactly what effect the treatment will have on a particular patient

Austin Bradford Hill, 1952

6

Lasso penalty in Cox model

• Popular approach to perform variable selection and develop a classfifer with high-dimensional data

• Estimation of tuning parameter (λ) usually via the cross-validated loglikelihood cvl : sum across K folds of the difference in penalized log-likelihood between the full sample and the training samples.

Verweij and van Houwelingen 1993

Drawback: Too many false positives selected• Theoretical results Meinshausen and Bühlmann 2006

• Empirical results Benner et al. 2010; Roberts and Nowak 20147

Objectives

- Selection of fewer biomarkers (i.e. λ ↗)- Trade-off between the goodness of fit (small λ) and parsimony (large λ)

Proposed penalty:

λ0 : the smallest λ for which no biomarker is included in the model pλ : number of non-null regression parameters for a given λ

Our empirical extension: lasso-pcvl

Ternès,Rotolo,MichielsStatMed2016

λ" = argmax) 𝑐𝑣𝑙(𝜆) − 𝑝𝑒𝑛(𝜆)

8

Evaluation of 11 alternatives

Penalization methods of the form 𝑝𝑒𝑛 𝜆 = 𝜃)𝑓)(𝑝))

Variants of the lasso penalty• One standard error rule (Friedman 2010)• Adaptive lasso (Zou 2006, Zhang et Lu 2007)• Percentile lasso (Roberts and Nowak 2014)• Stability selection (Meinshausen and Bühlmann 2010)

λ" = argmax) 𝑐𝑣𝑙(𝜆) − 𝑝𝑒𝑛(𝜆)

Estimationofthetuningparameter

9

Simulation studyData generation• Exponential survival times• n: 100, 500, 1000 patients• p: 10, 100, 1000 biomarkers• q: 0 (null), 1 and > 1 (alternative scenarios)• Equal effect size: β = log(0.8) (and varying effect sizes)• Autoregressive correlation between biomarkersEvaluation criteria

• FDR, FNR• Concordance index

Ternès,Rotolo,MichielsStatMed2016 10

Activebiomarkers (+)

Inactivebiomarkers (−)

Selectedbiomarkers

(+)

True Positive(TP)

False Positive(FP)

FalseDiscoveryRate

FP /(TP +FP)Unselectedbiomarkers

(−)

FalseNegative(FN)

TrueNegative(TN)

FalseNon-discoveryRate

FN/(TN+FN)Sensitivity

=TP/(TP +FN)Specificity

=TN/(TN+FP)

FalseNegativeRate

=FN/(TP+FN)

False PositiveRate

=FP/(TN+FP)

Null scenario (q = 0)


Alternative scenario (q > 1)


lasso-cvllasso-pcvl

lasso-cvl(1se)lasso-AIClasso-RIC

adaptivelassostabilityselection

2actives 10actives 20actives

Breast cancer example

Publicly available data from n = 523 patients with early breast cancer

Aim: Identify prognostic genes for distant recurrence-free survival


14

Standardized expression values of p = 1703 genes

Gene Signature DataBase⇒assess whether genes where also included in “known” curated breast cancer gene signatures

GATA3 also highly discussed in the biomedical literature

Median:22Range:14– 53

Median:16Range:6– 31

Wilcoxon P-value=0.02



0.5 0.6 0.8 1 1.3 1.6 2

Gemini/Libra

Other

Aspirin better Placebo better

ISIS-2 trial: effects of aspirin vs control on vascular death in 17,187 patients with acute myocardial infarction

Interaction p-value p=0.002

Odds ratio & 95% CIAstrologicalbirth sign

Whom to treat: subgroups?

Peto et al Lancet 198815

Start of the randomized clinical trial…

TheEconomist,200716

Randomize-all design

Buyse, Michiels et al, Expert Rev Mol Diag 2011

R

Biomarker -

Biomarker

Biomarker +Std

Exp Biomarker -

Biomarker

Biomarker +R:Randomization;Std:standardarm;Exp:experimental arm Testmanybiomarkerxtreatmentinteractions!

17

18

Multiple interaction testing throughpermutation in randomized trials

• Control of family-wise Type I error• Treatment-by-biomarker interactions in a Weibull model • 5 tests, using single-biomarker statistics and composite statistics

• Composite Difference statistic: |g1-g0|g0 = “concordance probability” that a randomly chosen control patient with a scaled biomarker score at 0 will outlive a treated patient with same score

• Permutation rearranges the patients within a treatment arm• In a strict sense not a permutation test of interaction but made

sensitive to detect interactions under the alternative…• Simulation study illustrating Type I error control and power when

there are ≥1 true treatment-effect modifiers and different correlation patterns

Michiels,Potthoff,GeorgeStatMed2011

Stepwise strategy for high-dimensionaldata in a randomized clinical trial (RCT)

• Step 1: Perform a global interaction test for control of the global Type I error at a prespecified α level (e.g. 5%)

• Step 2: Only when global test is significant, developclassifier in a survival model with interaction effects– Use 10-fold crossvalidation to estimate treatment

effects in composite biomarker score defined subsets– Applying survival model on full RCT data: indication

classifier for future patients

Michiels, Rotolo 201519

Step I: Global interaction test by permutation

Michiels, Potthoff, George Stat Med 2011; Michiels, Ternes, Rotolo (in revision)20

Step II: Develop a composite biomarker classifier through 10-fold crossvalidation

21Michiels, Ternès, Rotolo (in revision)

French breast RCT example• Tissue-array from two French breast cancer RCTs of

adjuvant chemotherapy with long term follow-up (798 pts)• 11 biomarkers (ER,PR, HER2, EGFR, p53, p27…)• Disease-free survival, 320 events

OverallHazardRatio:0.78[0.62–0.97]

0.0

0.2

0.4

0.6

0.8

1.0

Dis

ease

-free

sur

viva

l

No chemo Chemo

0 2 4 6 8 10Years

No. At RiskNo chemo 389 335 286 237 195 148

Chemo 409 368 321 266 223 161

ChemotherapyControl

22Michiels, Rotolo 2015

LowHigh

0.25 0.5 0.75 1 1.25 1.5

Hazard Ratio

French breast RCTs exampleStep I: Global test, 2000 permutations

• Composite difference test p= 0.01

Step II: Crossvalidated treatment effects according to composite biomarker score (cut-off at 0.5)

23Michiels, Rotolo 2015

Chemo better Control better

Lasso for many biomarker by treatment interactions…

24Ternès N,Rotolo F,HeinzeG,Michiels S(inrevision)

Thefullbiomarker-by-treatmentinteractionmodel T X1 X2 X3 TX1 TX2 TX3

Modifiedcovariates TX1 TX2 TX3

PenalizedregressionFull-lassoAdaptivelasso(sw:specificweights)Adaptivelasso(gw:groupedweights)Group-lassoRidge+lasso

T X1 X2 X3 TX1 TX2 TX3



T X1 X2 X3TX1 TX2 TX3

T TX1 TX2 TX3𝝋:;<<

DimensionreductionPCA+lassoPLS+lasso

T Z1 Z2 TX1 TX2 TX3

T Z1 TX1 TX2 TX3

Simulation study

Generation of p = 500 unit-variance Gaussian biomarkers for n = 500 patients (250 per arm), exponential survival

ALTERN

ATIVE

SCEN

ARIOS

Std Exp

Treatment

Clinicaloutcome

B+

B-

Std Exp

Treatment

Clinicaloutcome

B+

B-

Std Exp

Treatment

Clinicaloutcome

B+B-

±

(1) (10) (10)

Std Exp

Treatment

Clinicaloutcome

B+B-

Std Exp

Treatment

Clinicaloutcome B+

B-

Std Exp

Treatment

Clinicaloutcome

B+

B-NULL

SCEN

ARIOS (10)

Ternès N,Rotolo F,HeinzeG,Michiels S(inrevision)

HR=0.5

Simulation study: null scenarios


Proportion of models selecting at least one biomarker-by-treatment interaction among 250 replications è type-I error or FDR

Simulation study: alternative scenarios


Publicly available data in breast cancer patients (GEO)

Clinical datan = 614 patientsArm A: anthracycline only (n = 107)Arm AT: anthracycline + taxane-based

chemotherapy (n =507)

Standardized expression datap = 1689 genes

Aim: Identify treatment-modifiers for taxane in tumors biopsies from breast cancer



Trainingset(315patients) Validationset(299patients)

IFIH1 gene already known in the biomedical literature• Associated with recurrence in non-responders to taxane-based

chemotherapy in early breast cancer• Included in two patents that aim at predicting the benefit of taxanes in cancer

lasso

alasso

modifiedcovariates

ridge+lassoPCA+lasso

OAS1ARMCX2

MET

ITGB4

SGCB

AREG F12

NDRG2

AKAP9

GPD1LDDX17

ARMCX1

PDLIM4

SGK1

MTFR1

REN

HSPA1A SLC39A6

ME1

H1FX SOX11

SCGB2A2

CYP2B6DEFB1

KRT81EVL

CHPT1

IFIH1



ClinicalRisk

“DISCORDANT RISK” DESIGN

Low riskSignature

High riskLow risk

High risk

Low riskSignature

High risk

R

ChT

No ChT

Buyse, Michiels et al, Exp Rev Mol Diag 2011; ChT: chemotherapy

“INTERMEDIATE RISK” DESIGN

All patients R

Low risk

Signature

High risk

Intermediaterisk

No ChT

ChT

BENEFITS OF LARGE RANDOMIZED TRIALS FOR THE VALIDATION OF GENE SIGNATURES

• Tumor samples will be collected prospectively • Patients will be selected identically • Patients will receive controlled treatments• Randomization will make it possible to investigate the

predictive value of signatures• Micro-arrays and pathological reviews will be done

centrally• Large numbers will make the results reliable

R2:1

ArmA:Targeted therapy

ArmB:MaintenanceArm

N=650

N=230AZD2014 AZD4547 AZD5363

AZD8931 SelumetinibVandetanib

SAFIR02 Lung : STUDY DESIGN

NSCLCmetastatic orlocaly advanced

NoEGFRmutationNoALKtranslocation

Cis-platinumBasedregimen4cycles

Progression ortoxicity

PRorSDTargetablemolecularalteration

YES

NO

R2:1

MEDI4736

N=180

Pemetrexed Erlotinib

MaintenanceArm

Pemetrexed Erlotinib

Biopsymetastatic site:Next generation

sequencing PGMIontorrent35genesArray CGHAgilent

PI’s:JC.Soria,F.BarlesiOpen-label,multicentric phaseII

Clinicaltrials.gov:NCT02117167

SWOG lung master protocol

https://clinicaltrials.gov/ct2/show/NCT02154490

Estimated Enrollment:10000

Main references

• Michiels S,Potthoff RF, GeorgeSL.Multipletestingoftreatmentmodifyingbiomarkersinarandomizedclinicaltrialwithasurvivalendpoint,StatMed2011;30:1502-1518.

• Michiels S,Rotolo F.Evaluationofclinicalutilityandvalidationofgenesignaturesinclinicaltrials,inDesignandAnalysisofClinicalTrialsforPredictiveMedicine,CRCPress(eds MatsuiS.,Buyse M.,SimonR)2015.

• Ternès N,Rotolo F,Michiels S.Empiricalextensionsofthelassopenaltytoreducethefalsediscoveryrateinhigh-dimensionalCoxregressionmodels.StatMed2016;35(15):2561-73

• Ternès N,Rotolo F,Heinze GandMichiels S.Predictionoftreatmentbenefitinhigh-dimensionalcoxmodelsviagenesignaturesinrandomizedclinicaltrials.Trials 2015;16(Suppl 2),O86.

Documents

Utillisation des signatures dans les essais · 2016-06-28 · Utillisation des signatures dans les essais cliniques en oncologie S. Michiels CESP, INSERM U1018 ... 2 actives 10 actives