Médecine de précision en oncologie

Antoine Hollebecque, MDDépartement d’Innovation Thérapeutique et Essais précoces (DITEP)

Gustave Roussy

Académie Nationale de PharmacieMercredi 3 octobre 2018

Precision medicine: definition from ESMO

Yates, Ann Oncol, 2018

Which rationale for this concept ?

Genomic characterization of cancers have shown that frequent cancers include large numberof genomic segments

Impact of targeting mechanisms of cancer progression

0 20 40 60 80

VHL mut poor outcome

VHL pro inter

VHL good prono

average

ER expression

ERBB2 amp

EGFR

ALK

AR expression

B-RAF

KIT

Estimated survival of metastatic cancers according to molecular alteration (months)

Identification and targeting molecular mechanisms involved in cancer progression improves outcomeSimilar concept applies with immunotherapeutics (PDL1)

B-RAF inhibitor in NSCLC

(V600E BRAF mutation)ESMO 2014

ALK inhibitor in NSCLC

(ALK rearrangment)NEJM 2010

EGFR inhibitor in NSCLC(EGFR mutation) Lancet Oncol 2012

ROS1 inhibitor in NSCLC(ROS1 rearrangment) NEJM 2014

*

**

*

*

*

Nonsmoker

Smoker, ≤

40 pack

years

Smoker, >

40 pack

years

*

*

*

~4% NSCLC

~10% NSCLC

SDPDNE

PR

Best Confirmed Response

38036034010080

60

40

20

0

-20-40-60-80-

100

Max

imu

m P

erc

en

t R

ed

uct

ion

fro

m B

ase

line

Me

asu

rem

ent

~2% NSCLC

~1% NSCLCCourtesy of B. Besse

Increase efficacy

Crizotinib

4 years

Speed-up drug development

Molecular profiling

Targeted therapy accordingto the molecular profile

Tumor Specimen

Can molecular profiling improve patient outcome ?

Patients with lethal Cancer

Intelling reports

Gustave Roussy molecular screening programMOSCATO

MOLECULAR SCREENING

CGH Array & NGS & WES & RNAseq

CLINICAL DECISION

Max 21 calendar days

FRESH TUMOR

BIOPSY PATHOLOGICAL CONTROL

TREATMENT

• High through-put analysis in a high volume phase I center

• Monocentric

• Target accrual => 1000 patients

Antoine Hollebecque et al., ASCO 2013; Charles Ferte et al, AACR 2014

Design of MOSCATO

• Primary Objective: To show that broad molecular screening improves outcome Stastistical hypothesis: > 25% of patients treated according to their genomic alteration

will experience a clinical benefit defined by a PFS ratio > 1.3

• Secondary Objectives To assess the feasability of this approach

To improve tumor response

To assess the percentage of patients treated with a selected therapy

To assess the frequency of genomic alterations

To speed-up drug development through enrichment of trials in biomarker-defined patients (stratified medicine)

PFS 1 PFS 2

PFS1

PFS 2

> 1.3Relevant Molecular Targeted Agent(MOSCATO)

Standard Therapy

TumorProgression

TumorProgression

Objectives of MOSCATO

FGFR1 amplification

Ion Torrent PGM – Life Technologies(Ampliseq CHP2 + custom

n=75 genes, Dec 2013)

CGH array Agilent(180K, Whole genome coverage)

+

Torrent_Suitev2.2Confim

ed bySangerSequencing

DNAextrac on10–50ng

Mul plexPCR1450ampliconsIonTorrent/PGM(>500Xcoverage)

StandardizedReport

Analysis Improvement: 30 genes then 50 then 75 genes screened

then moved partially in may 2014 to WES + RNASeq

MOLECULAR SCREENING

CGH & NGS & RNAseq & WES

CLINICAL DECISION

TREATMENTFRESH TUMOR

BIOPSY PATHOLOGICAL CONTROL

Methods of MOSCATO

• Clinical Decision– Weekly Tumor Board discussion

– Clinicians / Biologists / Bioinformaticians

• Treatment– Phase I/II trial compounds

• > 60 phase I trials @ Gustave Roussy

• > 20 Actionable Targets

– Off label use of EMA approved MTA

Hanahan D, Weinberg R.A. Cell 2011.

CLINICAL DECISON

TREATMENTMOLECULAR SCREENING

CGH & NGS & RNAseq & WES

FRESH TUMOR

BIOPSY PATHOLOGICAL CONTROL

Methods of MOSCATO

adults1000

RESULTS of MOSCATO

Biopsy and decision in MOSCATO 01

are performed in clinically relevant timing

Median 25 daysIC95 12-56 days

Median 24 daysIC95 0-59 days

No Actionable Target based on NGS or CGH: n=434

Molecular portrait (NGS or CGH) n=844

NGS+CGH: n=740 / NGS alone: n=98 / CGH alone: n=6

Actionable Target, n= 411(IHC alone n=1)

Received Matched Treatmentn=199

Patients includedn= 1110

Pediatrics N=74

Screen failure N=87No possible biopsy n=28SAE: n=25Consent withdrawal: n=11 Clinical deterioration or death: n=5 Other: 11Missing: n=7

No NGS nor CGH: n=103

Adults included n= 1036

rapid clinical deterioration: n=62

waiting for treatment: n=37

exclusion criteria n=18

trial not open n=12

absence of progressive disease n=6

patient refusal n=3

Other n=64, unknown n=6:

Primary endpoint completedn= 193

PFS1 missing: n=5PFS2<1.3*PFS1 and not yet progressed n=1

Molecular tumour board (MTB) n=949

Summary of MOSCATO results

49 %

19%

Massard et al. Cancer Discov 2018

Results

PFS ratio > 1.3 = 33%

IC95 PFS ratio = 26%-39%

H0 rejected with a p-value < 0.001

Molecular Targeted Agent(MOSCATO)

Previous Therapy

TumorProgression

TumorProgression

Results for primary endpoint

Massard et al. Cancer Discov 2018

CR + PR = 11% (22/193)

CR + PR + SD = 63% (122/193)

Best percent changefrom baseline

(RECIST1.1)

+ 20%

- 30%

Results for secondary endpoints

Massard et al. Cancer Discov 2018

PatientsN=43 (4%)

BiopsiesN=48

Fit for analysisN=38 (79%)

No biopsy = 3Cellularity < 10% = 6

Not realized = 1

Druggable molecular aberration(s)N=25 (66%)

TreatedN=19 (76%)

MOSCATO-01N=1168

50%

Rebiopsy procedures =1 patient x 3

3 patients x 2

No alteration foundN=5

Winner tumor typee.g. cholangiocarcinoma

Verlingue et al. Eur J Cancer 2017

Median PFS ratio = 1.52

IC95 [0,08-7,1]

PFS ratio > 1.3 = 44%

Response rates Progression free survival

6-month PFS rate = 42%PR+CR = 32%

Winner tumor typee.g. cholangiocarcinoma

ERBB3 alterationsN=33 (3.7%)

NPVN=13

Received therapiesN=28

Other treatmentsN=17

VUPN=13

HotspotsN=6

AmpN=2

HER3 partners inhibitorsN=11

NPV : non pathogenic variantVUP : variant of unknown pathogenicity

Amp: amplification

1 patient with 2 alterations in ERBB3

Winner Pathwaye.g. ERBB3

HER3 partners’ inhibitors:- Trastuzumab and/or lapatinib- Afatinib

Overall survival

Winner Pathwaye.g. ERBB3

PFS < 100 days

PFS > 200 days

HER2 directed monoclonal antibodies

HER2/pan HER directed small molecules

Ongoing treatment

Extracellular domain

Kinase domain

III

III

IV

Docking domain

S846I

E928G

A159TV140L

G661S

Q865H

Transmembrane domain

K329E

ERBB3

Current questions ?

• Should we use large panel of genes ?

• How to develop drugs in rare genomic segments ?

• Prediction of sensitivity ?

• Detecting subclones and monitoring clonal evolution ?

Current questions ?

• Should we use large panel of genes ?

• How to develop drugs in rare genomic segments ?

• Prediction of sensitivity ?

• Detecting subclones and monitoring clonal evolution ?

• Because it allows testing the few relevant genes in a single assay

• Because it allows finding alterations in other genes for which relevance to cancer is shown but there is no drug approval

• Because it allows having « global picture » of cancer genome and mutationalprocesses

• Because it allows modeling disease biology in each patient

Should we use large panel of genes ?

EGFR

ALK

RET

BRAF

MET

ERBB2

ROS1

others

“FDA approved Thermo Fisher Scientific’s Oncomine™ Dx Target Test as the first next-generation sequencing (NGS)-based companion diagnostic that screens tumor samples against panels of biomarkers”

Should we use large panel of genes ?

Femme 30 ans Cholangiocarcinome intra-hépatique (Avril 2011) En progression après:

Mai 2011: Radioembolisation (Yttrium90) Mai-Sep 2011: Folfox Fev-Juin 2012: Folfox Juin-Dec 2012: Gemcitabine-Cisplatine Jan-Avr 2013: Sunitinib Avr-Mai 2013: Paclitaxel Juin 2013: MOSCATO

No mutation

tNGS

Translocation FGFR2-CCAR1

WES et RNAseq

Should we use large panel of genes ?

26/11/2014 10/02/2015

Fusion FGFR2-CCAR1 → FGFR inhibitor

Should we use large panel of genes ?

Current questions ?

• Should we use large panel of genes ?

• How to develop drugs in rare genomic segments ?

• Prediction of sensitivity ?

• Detecting subclones and monitoring clonal evolution ?

Drug development in rare genomic entities

Drug development in rare genomic entities

Drug development in rare genomic entities

Larotrectinib

Drug development in rare genomic entities

Andre F, NEJM, 2018

Basket protocol

1 molécule → 1 ou plusieurs anomalies moléculaires → plusieurs types tumoraux

Drug development in rare genomic entities

Drug development in rare genomic entities

Hyman DM et al. NEJM 2015

Efficacy according to histology ?e.g. BRAF

Hyman DM et al. NEJM 2015

Drug development in rare genomic entities

Mutation BRAF V600E

Mélanome

Mutation BRAF V600E

Cancer du colon

Kopetz et al. J Clin Oncol 2015Chapman et al. N Engl J Med 2011

ORR = 5%

ORR ≈ 50%

Level of evidence for actionability

Current questions ?

• Should we use large panel of genes ?

• How to develop drugs in rare genomic segments ?

• Prediction of sensitivity ?

• Detecting subclones and monitoring clonal evolution ?

Genomic predictors of sensitivity

Anti-PD-1 in NSCLC

Snyder et al NEJM 2014 Rizzi et al Science 2015

Anti-CTLA-4 in Melanoma

Number of non-synonymous

mutations

Generation of neoantigens

Mutational burden

Sensitivity to anti-PD1

Pembrolizumab et MSI-High

Genomic predictors of sensitivity

Le et al. Science 2017

Co-existing mutations could be associated with resistanceto single agent targeted therapies

Each color represent one drug/target couple

driver alone Driver +other mutation

Wild type(no driver)

Sen

siti

vity

to t

arge

ted

age

nts

Lefebvre & Yu, Personal data

What is the clinical impact of multiple genomic alterations ?

Targeted Therapies

Current questions ?

• Should we use large panel of genes ?

• How to develop drugs in rare genomic segments ?

• Prediction of sensitivity ?

• Detecting subclones and monitoring clonal evolution ?

EGFR MET

11 Jul 2012 30 Aug 2012 23 Oct 2012 17 Dec 2012

Erlotinib

PR (-36%) PD (New Lesions)SD (-28%)Baseline

12-Jul-2012C1J1

17-Dec-2012Fin de Traitement

CA

19

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volu

tio

nC

T-s

can

évo

luti

on

CG

H é

volu

tio

n

EGFR

Detecting subclones and monitoring clonal evolution ?

Perspectives

• Big data

• CfDNA

PerspectivesNew generation of software for target prediction & database to train

New generation of software

Database to train software

Trials testing the clinical utilityof large panel of genes

(Watson …)

Big Data« no amount of algorithmic finesse can squeeze out information that is not present » (NEJM, 2017)

Integration of multicomponent predictors: example of IO

Blank, Science, 2016

PerspectivesMultigene panels to detect genomic alterations in plasma

Jovelet, Clin Cancer Res, 2015

Performance of NGS on plasma DNA to detect actionable mutation

Approach to detect tumor-specific proteins and RNA in blood ?