• Aucun résultat trouvé

Precision Medicine: From stratified therapies to personalized therapies

N/A
N/A
Protected

Academic year: 2022

Partager "Precision Medicine: From stratified therapies to personalized therapies"

Copied!
18
0
0

Texte intégral

(1)

Precision Medicine:

From stratified therapies to personalized therapies

Fabrice ANDRE

Institut Gustave Roussy

Villejuif, France

(2)

Frequent cancers include

high number of very rare genomic segments

Stephens, Nature, 2012 (whole genome sequencing breast cancers)

(3)

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

(4)

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

(5)

Outline

• Stratified medicine

• Personalized medicine

(6)

Stratified medicine

• Drug development or implementation in a strate defined by a molecular alteration

FGFR1 amplification: 10% of breast cancer

(7)

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 trial Testing the FGFR1 Inh in patients with FGFR1 amp BC

(8)

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

(9)

Molecular screening with High Throughput

Genomics Molecular screening with High Throughput

Genomics

IF

Progressi ve

disease IF

Progressi ve

disease

Target identification

Target identification

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

(10)

SAFIR02 lung

Ongoing molecular screening or personalized medicine programs in France

SAFIR01

MOSCATO

(Hollebecque, ASCO 2013)

SAFIR02 breast

MOST preSAFIR

(Arnedos, EJC, 2012)

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

Goal: To generate optimal algorithm for individualized therapy SHIVA

(Letourneau AACR 2013)

Pilot study 1st generation trials No NGS NGS

Randomized trials Sponsor

Gustave Roussy Unicancer

L Berard Lyon Curie Institute

Unified Database:

Pick-up the winner

targets

2nd generation Algorithm for Personnalized

medicine WINTHER

Profiler

(11)

Molecular screening: Challenges

• No research in stratified medicine without

molecular screening programs

(12)

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 development

for this AKT1 mut / FGFR1 amp segment ?

(13)

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

test the algorithm not the drug

How to move there ???

(14)

Her2-negative metastatic breast

cancer

no more than 1 line chemotherapy

Biopsy metastatic site:

Next generation sequencing

Array CGH

Chemotherapy 6-8 cycles

No

alteration

Target defined by 1st generation Virtual cell (CCLE)

Followed up but not included R

10 Targeted therapy According to

51 Molecular alterations

SOC

No PD metastatic NSCLC no

more than 1 line chemotherapy EGFRwt / ALKwt

SAFIR02: Study Design

(15)

SAFIR02 lung

Ongoing molecular screening or personalized medicine programs in France

SAFIR01

MOSCATO

(Hollebecque, ASCO 2013)

SAFIR02 breast

MOST preSAFIR

(Arnedos, EJC, 2012)

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

Goal: To generate optimal algorithm for individualized therapy SHIVA

(Letourneau AACR 2013)

Pilot study 1st generation trials No NGS NGS

Randomized trials Sponsor

Gustave Roussy Unicancer

L Berard Lyon Curie Institute

Unified Database:

Pick-up the winner

targets

2nd generation Algorithm for Personnalized

medicine WINTHER

Profiler

(16)

Long term perspective

1st generation

trials database

2nd

generation algorithm

2nd

generation trials

Targeting oncogenic

drivers

Integration of other systems:

DNA repair Immunology

metabolism

database

2013 2015 2018-2020

(17)

Challenges / Research questions

• Bioinformatic algorithm for treatment

decision, that integrates all biological systems

• Technologies:

– whole exome sequencing – RNA seq

– Protein-based assays

(18)

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

Références