les essais cliniques actuels et la recherche translationelle · les essais cliniques actuels et la...
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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