Precision Medicine:From stratified therapies to

personalized therapies

Fabrice ANDREInstitut Gustave Roussy

Villejuif, France

Frequent cancers include high number of very rare genomic segments

Stephens, Nature, 2012

(whole genome sequencing breast cancers)

Working hypothesis

• Targeting mechanisms that lead to cancer progression can improve patient’s outcome

• These mechanisms are individual

• Goal: to identify the mechanism of cancer progression at the individual level, in order to target it

Precision Medicine

Concept: Identify the targets to be treated in each patient

Molecular analysis

Therapy matched to genomic alteration

Andre, ESMO, 2012

Target identification

What is the optimal Biotechnology ?

What is the optimal Algorithm ?

Clinical evidence

Outline

• Stratified medicine

• Personalized medicine

Stratified medicine• Drug development or implementation in a strate

defined by a molecular alteration

FGFR1 amplification: 10% of breast cancer

Translational research to feed stratified medicine

FGFR1 inhibitors present higher sensitivity

on FGFR1-amplified CC

FGFR1: amplification in 10% BC

Set-up genomic test(FISH)

Run phase II trialTesting the FGFR1 Inh in patients withFGFR1 amp BC

Research and medical questions related to stratified medicine

• How to facilitate translation of discoveries ?• Develop translational research units

• How to set-up a molecular assay for stratified medicine ?• Develop genomic units for clinical use

• How to optimally run trials of stratified medicine ?• Set-up molecular screening programs

Molecular screening with

High Throughput Genomics

Molecular screening with

High Throughput Genomics

IF Progressi

vedisease

IF Progressi

vedisease

Targetidentification

Targetidentification

Trial A

Trial F

Trial E

Trial D

Trial C

Trial B

Andre, Delaloge, Soria, J Clin Oncol, 2011

Molecular screening programs: to identify patients eligible for phase I/II trials

SAFIR02 lung

Ongoing molecular screening or personalized medicine programs in France

SAFIR01

MOSCATO(Hollebecque,

ASCO 2013)

SAFIR02breast

MOST

preSAFIR(Arnedos, EJC, 2012)

Overall : >2 000 planned patients (all tumor types), >800 already includedBreast Cancer: > 1 000 planned, >70 already treated

Goal: To generate optimal algorithm for individualized therapy

SHIVA(LetourneauAACR 2013)

Pilot study 1st generation trialsNo NGS NGS

Randomized trialsSponsor

Gustave Roussy

Unicancer

L BerardLyon

Curie Institute

UnifiedDatabase:Pick-up

the winnertargets

2nd generationAlgorithm forPersonnalized

medicine

WINTHER

Profiler

Molecular screening: Challenges

• No research in stratified medicine without molecular screening programs

Evolution:GENOMIC DISEASES ARE BECOMING TO RARE OR COMPLEX TO ALLOW DRUG DEVELOPMENT IN GENOMIC SEGMENTS

How to move forward ?

Stephens, Nature, 2012

Are we going to make a drug developmentfor this AKT1 mut / FGFR1 amp segment ?

Solution to improve outcome with targeted therapies in the genomic era:

test the algorithm not the drug

How to move there ???

Her2-negative metastatic breast

cancerno more than 1 line

chemotherapy

Biopsy metastatic site:Next generation

sequencingArray CGH

Chemotherapy6-8 cycles

No alteration

Target defined by 1st generationVirtual cell (CCLE)

Followed up but not included

R

10 Targeted therapyAccording to

51 Molecular alterations

SOC

No PD

metastatic NSCLC no more than 1 line chemotherapy

EGFRwt / ALKwt

SAFIR02: Study Design

SAFIR02 lung

Ongoing molecular screening or personalized medicine programs in France

SAFIR01

MOSCATO(Hollebecque,

ASCO 2013)

SAFIR02breast

MOST

preSAFIR(Arnedos, EJC, 2012)

Overall : >2 000 planned patients (all tumor types), >800 already includedBreast Cancer: > 1 000 planned, >70 already treated

Goal: To generate optimal algorithm for individualized therapy

SHIVA(LetourneauAACR 2013)

Pilot study 1st generation trialsNo NGS NGS

Randomized trialsSponsor

Gustave Roussy

Unicancer

L BerardLyon

Curie Institute

UnifiedDatabase:Pick-up

the winnertargets

2nd generationAlgorithm forPersonnalized

medicine

WINTHER

Profiler

Long term perspective

1st generationtrials database

2nd generation

algorithm

2nd generation

trials

Targeting oncogenic

drivers

Integration ofother systems:

DNA repairImmunologymetabolism

database

2013 20152018-2020

Challenges / Research questions

• Bioinformatic algorithm for treatment decision, that integrates all biological systems

• Technologies: – whole exome sequencing – RNA seq– Protein-based assays

Conclusion: genomic medicine for cancer patients

• bioinformatic algorithm for treatment decision

• Integration of DNA repair, immunology, metabolism in personalized medicine

• large scale screening and implementation new technologies

• Target identification for stratified medicine

• understanding mechanisms of resistance