Les essais cliniques actuels et la

Recherche Translationelle

Prof. Eric Raymond, MD, PhD

Chair of the Pharmacology And Molecular Mechanism

PAMM – EORTC group

Personalized Health Care – Fantasy or Reality?

“Here’s my tumor’s DNA sequence”

Gracie Lieberman - Genentech

« A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty. »

Sir Winston Churchill 1874-1965

Focus on cancer cells Achilles’ heels

Step 1

Knowing better the enemy

to use the adapted weapons

Treatment of Cancer – Historical Overview

Organs

Tissues/Cells

Proteins/Enzymes

RNA/DNA

1800

2000

Anatomical Diagnosis

Molecular Diagnosis

Pathological Diagnosis

1900

Surgery

Radiotherapy

Chemotherapy

Hormone therapy

Tumor stroma

Phylogenic drifts of tumors

Trageted Therapies

Anti-angiogenic

Epigenetic drugs

immunotherapy

Stem cell therapies

Stroma-modulating agents

Tumor metabolism

2010 Tumor

Ecosystem recognition

Organ origins

Pathological subtypes

Differentiation

Proliferation (mitoses/Ki67)

Hormone dependency

Tumor analysis Treatment selection

Higher probability of efficacy

Minimal approaches for treatment selection

Scientifically-driven treatment selection

Cells

Stroma

Angiogenesis

Proteins/kinases Expression/activation Oxidation/metabolism

RNA/DNA: Caryotype Expression Mutation Methylation

Molecular Biology

Cellular Biology

Analyses de la tumeur Choix du traitement

Treatment A

Treatment B

Neither A nor B

Challanges • Evaluation of novel targets and treatment • Novel treatments need repeated evaluation of relevant

biological targets • Samplings shall be:

– repeatable – Minimally invasive – Give access to sufficient amount of any markers for accurate

analysis

• Types of evaluation – Biopsy – Cytology – Blood biopsy – CTC – Imaging

Companion Biomarkers

Prognostic Biomarkers

Predictive Biomarkers

Monitoring Biomarkers

Challenges

• Cellular diversity

• Molecular diversity

• Tumor heterogeneity

• Epigenetic changes

Tumor cells

Immune cells

Lymphocytes

Macrophages

Stellate cells

Angiogenic stroma cells

Endothelial cells

Pericytes

Supportive stroma cells

Fibroblasts

Tumors are made of heterogeneous cellular

populations that coexist in a dynamic ecosystem

Genomic patterns of Lung Cancer

KRAS; 30%

EGFR; 15%

EML4-ALK; 5%

HER 2; 2% BRAF; 2% FGFR4; 2%

PIK3CA; 1% MEK; 1%

ROS1, 1%

RET; 1%

Unkn; 40%

Sequist LV, et al. Sci Trans Med. 2011;3:75ra26. Oxnard GR, et al. Clin Cancer Res. 2011;17:1616-1622.

Ohashi K, et al. Proc Nat Acad Sci USA. 2012;109:E2127-E2133. Takezawa K, et al. Cancer Discov.

2012;2:922-933.

HER2 amplification (12%)+

BRAF mutation (1%)+

Mechanism of Acquired Resistance to EGFR TKIs

PIK3CA (5%)

SCLC transformation (14%)

Re-biopsy

First-line Treatment With EGFR TKIs vs

Chemotherapy in EGFR-Mutated Patients

Study Treatment N Median PFS, Mos Median OS, Mos

Maemondo[1] Gefitinib vs carboplatin/ paclitaxel

230 10.8 vs 5.4 (P < .001)

30.5 vs 23.6 (P = .31)

Mitsudomi[2,3] Gefitinib vs cisplatin/docetaxel

177 9.2 vs 6.3

(P < .0001) HR: 1.19

OPTIMAL[4,5] Erlotinib vs carboplatin/gemcitabine

165 13.1 vs 4.6 (P < .0001)

HR: 1.065

EURTAC[6]

Erlotinib vs platinum-based chemotherapy

174 9.7 vs 5.2

(P < .0001) 19.3 vs 19.5

(P = .87)

LUX-Lung 3[7] Afatanib vs

CDDP/pemetrexed 345

11.1 vs 6.9 (P = .001)

Not yet reached

LUX-Lung 6[8] Afatinib vs

cisplatin/gemcitabine 364

11.0 vs 5.6 (P < .0001)

Not yet reached

1. Maemondo M, et al. N Engl J Med. 2010;362:2380-2388. 2. Mitsudomi T, et al. Lancet Oncol. 2010;11:121-128. 3. Mitsudomi

T, et a. ASCO 2012. Abstract 7521. 4. Zhou C, et al. Lancet Oncol. 2011;12:735-742. 5. Zhang C, et al. ASCO 2012. Abstract

7520. 6. Rosell R, et al. Lancet Oncol. 2012;13:239-246. 7. Sequist LV, et al. J Clin Oncol. 2013;31:3327-3334. 8. Wu YL, et al.

Lancet Oncol. 2014;15:213-222.

Crizotinib

(n = 173)

Chemotherapy

(n = 174)

Events, n (%) 100 (58) 127 (73)

Median, mo 7.7 3.0

HR (95% CI) 0.49 (0.37-0.64)

P < .0001

Pro

bab

ilit

y o

f S

urv

ival

Wit

ho

ut

Pro

gre

ss

ion

(%

)

100

80

60

40

20

0 0 5 10 15 20 25

Time (Mos) At Risk, n

Crizotinib

Chemotherapy

Shaw AT, et al. N Engl J Med. 2013;368:2385-2394.

Crizotinib vs Standard Chemotherapy in ALK+

NSCLC (PROFILE 1007): PFS

173

174

93

49

38

15

11

4

2

1

0

0

Cancer cell ecosystems adapt to therapeutic pressure using complex

signal interactions, local immunosuppression, interstitial oxygen

pressure, and metabolic alterations

Translational research shall address the complexity of cancer system biology to help

developing novel drugs in this context

20

Hétérogénéité tumorale: Variations clonales temporelles spontanées et induites

Eric RAYMOND – Kinases oncogéniques et thérapies ciblées – Académie des sciences – 23 avril 2013

Population initiale Anomalie oncogénique directrice

Hétérogénéité inter-patient

Progression Hétérogénéité intra-tumorale

Mise en place des traitements

Rechute Sélection de sous-clones

Anomalies oncogénique induites

Traitement 1 Traitement 2

Traitement 1 Traitement 2 Cellules cancéreuses

Microenvironnement tumorale

Rechute: Clones avec

mutations minoritaires

Stratégies de thérapies ciblées anticipatoires

Focus on cancer supportive cells

(angiogenenic and stroma cells)

Step 2

Inhibiting supportive foes

FOLKMAN: 1971

The story began with a simplistic but quite realistic

cartoon on the role of tumor angiogenesis

Somatic

Mutation

Small

Avascular

Tumor

Tumor Secretion of

Proangiogenic

Factors Stimulates

Angiogenesis

Rapid Tumor Growth and

Metastasis

Angiogenic Inhibitors

May Reverse this Process

Angiogenic

Switch

Endothelial cell Tumor cell

PDGF VEGF

Apoptosis

PDGF VEGF

IGF

IGF1-R

P

HIF-1 HIF-2

Survival Proliferation

P Ras

P AKT

P

Nucleus

Mitochondria

Mitochondria

P

Nucleus

Angiogenesis: Differentiation Proliferation Migration Tubule formation

Apoptosis

PDGF-BB

PI3k

x

P P P

VEGF-A

VEGF-C Paracrine

stimulation

PI3k

S6K1

P mTOR

P AKT

VEGFR-2 VEGFR-3 PDGFR-

Hypoxia

PDGF-BB

PDGFR-

Nucleus P

PI3k

S6K1

P mTOR

P AKT

mTOR

SSR

Somatostatin

Hypoxia

Pericytes Neuroendocrine

Tumors Tumor vessel

permeability

Autocrine

& paracrine

stimulation

Faivre S, et al. Endocrinol Metab Clin N Am 2010;39:811–826

EMT

TGF-beta

MET

EMT as a generic concept was shown to play a role in various form of resistance to cell signaling inhibitor and chemotherapy

Various forms of EMT can be recognized

CXCR4

HCC

HCC NET

Head & Neck ?

25

Control

Biopsy under local anesthesia

Fresh tumor-tissue cultured for 48h To test targeted therapies

Biomarkers analysis by immunofluorescence

Evaluation ex vivo des médicaments: prédiction/validation

Drug A

Drug B

Eric RAYMOND – Kinases oncogéniques et thérapies ciblées – Académie des sciences – 23 avril 2013

Médecine prédictive Expression protéique et nucléique

Antibiogramme Effets sur l’expression de biomarqueurs

PROCESS FROM SURGICAL SPECIMEN

De Graaf IAM et al., Nat Protocols. 2010

Tumor cores are prepared using a surgical punch and transferred into core holder of the Tissue Slicer

Slices of 8mm are performed and transferred to 6-well plates using a spatula to avoid tissue damages

Study H9H-MC-JBAK (NCT01246986)

↑ proliferation

↑ invasion/

metastasis

Role of TGF-β Signaling in HCC Role of Modulation in E-cadherin, AFP and T Regulatory Cells

↑ sE-cadherin

↑ T regulatory cells

EMT

AFP

LY2157299

↓ E-cadherin

MIGRATION/PROGRESSION

Abbreviations: AFP, alpha-fetoprotein; EMT, Epithelial-mesenchymal transition; sE-cadherin, soluble E-cadherin; TGF-β1, transforming growth factor-beta 1.

LY2157299 monohydrate target plasma exposures: 3 -10.96 mg*h/L

Adapted from Neuzillet, et al. Oncotarget 2013 [Epub ahead of print]

Neuzillet C et al. Oncotarget 2013

Serova M, et al. NCI-EORTC-AACR meeting 2013

Study H9H-MC-JBAK (NCT01246986)

TTP and OS in AFP Responders vs. Non-responders

29

TTP OS

n/N (%) Median 95% CI

AFP responders 25/103 (24%) 18.6 wks

(4.3 mo) 12.1, 67.7

AFP non-

responders 78/103 (76%)

6.6 wks

(1.5 mo) 6.1, 12.1

n/N (%) Median 95% CI

AFP responders 25/103 (24%) 93.1 wks

(21.4 mo)

40.4,

ongoing

AFP non-

responders

78/103 (76%)

29.6 wks

(6.8 mo) 18.3, 38.4

Abbreviations: AFP, alpha-fetoprotein; CI, confidence interval; OS, overall survival; TTP, time-to-tumor progression.

Faivre S. ASCO GI 2014

Focus on restoring immune T cell

capacities

Step 3

Facilitating the help of allies

PD-L2–mediated inhibition of TH2 T cells

Stromal PD-L1 modulation of T cells

Adapted from Sznol M, et al. ASCO 2013. Abstract CRA9006.

PD-1 Blockade: Binding to PD-L1 (B7-H1)

and PD-L2 (B7-DC) Revives T Cells

• PD-L1 expression on

tumor cells induced by

interferon-γ

• Activated T cells

that could kill

tumors are

specifically disabled

PD-1 PD-L1 PD-L2 T-cell receptor MHC-1 CD28 Shp-2 B7.1

IFN-γ–mediated upregulation of

tumor PD-L1 PD-L1/PD-1–mediated inhibition of tumor cell killing

Priming and activation of

T cells

Immune cell modulation of T cells

Tumor Cell

IFN-γR

IFN-γ

Tumor-associated fibroblast M2

macrophage

Treg cell

TH2 T cell

Other NFκB P13K

CD8+ cytoxic T lymphocyte

T-cell polarization

TGF-β

IL-4/13

Can you generate tumor-killing T cells?

Dendritic Cell

Antigen priming

Can the T cells get to the tumor?

T-cell trafficking

Can the T cells see the tumor?

Peptide-MHC expression

Can the T cells be turned off?

Inhibitory cytokines

Can the T cells be turned off?

PD-L1 expression on tumor cells

Pembrolizumab (MK-3475)

In all evaluable patients, regardless of dose or PD-L1 status • ORR (confirmed and unconfirmed): 20% by RECIST v1.1, 18% by irRC • DCR (confirmed and unconfirmed): 40% by RECIST v1.1, 52% by irRC

PDL1 +: RR 23%, PFS 11 wks

PDL1 -: RR 9%, PFS 10 wks

Herbst, ASCO 2014 26-30 September 2014, Madrid, Spain

esmo.org

Maximum Percent Change From Baseline in Tumor Sizea (RECIST v1.1, Central Review)

aEvaluable patients were those with measurable disease at baseline per central review who had ≥1 post baseline tumor assessment.

Analysis cut-off date: March 3, 2014.

-100

-80

-60

-40

-20

0

20

40

60

80

100

Ch

ange

Fro

m B

ase

line

in S

um

of

La

rge

st D

iam

ete

r o

f Ta

rget

Le

sio

n, %

Treatment naive

Previously treated58%

Use large networks of investigators

and scientists to strength power

Step 4

Join intelligences and forces

Intégration de données cliniques et

biologiques

Échantillons

humains

Imagerie

clinique

Tumeur + Stroma

Cellules

Tumorales isolées

Sang

Avatars

Tissue slicers + cultures

Control

Biopsyunderlocalanesthesia

Freshtumor- ssueculturedfor48hTotesttargetedtherapies

Biomarkersanalysisbyimmunofluorescence

DrugA

DrugB

Single cell sorting

Analyse proteique +

Acides nucleique (ARN, ADN)

Essais

cliniques

Oncologie de précision

Thérapie individualisée

Intégration des données

Clinique

Imagerie

Biologie

Analyses des données (data center)

RCP oncologique intégrant la biologie tumorale

L’imagerie et l’offre de soins cliniques

Screening

pharmacologique ciblé

Analyse de

l’hétérogénéité tumorale

Analyse des cellules

tumorale et du stroma

EORTC: Networking

DOG Imaging

Pathology

PAMM QoL

Elderly

Conclusions

• Modern Oncology identifies paradigm drugs for molecular oriented patient selection

• Molecular diagnosis as part of modern pathology allows to narrow down patient who may (or not) benefit of certain novel therapies

• ‘Stroma’ (immunological and supportive ecosystems of cancer cells) is essential and shall be better understood as an important support for tumor plasticity and drug resistance

• EORTC as a large network of international investigators and scientists can speed up ambitious and large translational programs in various tumor types