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Jérôme GALON
INSERM, Laboratory of Integrative Cancer Immunology Cordeliers Research Center, Paris, France
LabEx
6th "Innovative therapy, monoclonal antibodies and beyond" Conference
Milan, Italy,
January 22nd 2016
Prognostic and predictive value of Immunoscore
Disclosures
Co-founder and chairman of the scientific advisory board:
HalioDx
Collaborative Research Agreement (grants) :
MedImmune, Janssen
Participation to Scientific Advisory Boards:
BMS, ImmunID, MedImmune, Astra Zeneca, Novartis
Consultant :
BMS, Roche, Ventana, GSK, MedImmune, ImmunID,
Nanostring, Definiens, Compugen, Actelion
Redefining cancer
Primary Tumor
Tis
T1
T2
T3
T4 Distant Metastasis
(M+)
Early stages (N0M0)
Tumor recurrence – Metachronous metastasis
Prognosis – Survival
Cancer treatment - immunotherapy
What drives metastasis ?
What drives tumor progression ?
How to explain metachronous metastasis ? Parameters associated with patients’ survival ?
Early-metastatic Invasion (VELIPI+)
How important is the tumor microenvironment ?
Ref. 6, 7, 8
Ref. 1, 15 Ref. 17, 18, 19, 20, 21
Ref. 11, 12, 20
Ref. 1, 2, 9, 10, 15, 16, 17
Ref. 1, 2, 3, 4, 5
1. Pages F New Engl J Med 2005
2. Galon J. Science 2006
3. Galon J. Cancer Res. 2007
4. Fridman H. Nat Rev Cancer 2012
5. Mlecnik B. Gastroenterology 2011
6. Bindea G. Immunity 2013
7. Mlecnik B. Science Transl Med. 2014
8. Angelova M. Genome Biol. 2015
Ref. 13, 14, 19
16. Galon J. J Pathol. 2014
17. Camus M. Cancer Res 2009
18. Berghoff A. OncoImmunol. 2016 in
press
19. Galon J. Immunity 2013
20. Church S. Immunity 2015
21. Mlecnik B. Immunity 2016 in press
9. Pages F. JCO 2009
10. Mlecnik B. JCO 2011
11. Tosolini M. Cancer Res 2011
12. Maby P. Cancer Res. 2015
13. Anitei G. Clin Can Res. 2014
14. Stoll M. OncoImmunol. 2015
15. Mlecnik B. Science Transl Med. 2016
in press
Cancer is one of the most complex biological system of all
“The whole is greater than the sum of its parts”, Aristotle
-> Systems biology in human cancer
Molecule X
Pathway Y
Cell Z
Hanahan & Weinberg, Cell 2001
Hallmarks of cancer
3) Our hypothesis: cancer is heterogeneous microenvironment, dynamic and
communicating with the immune system
Hanahan & Weinberg, Cell 2011
1) A tumor cell DNA disease – Cell-centric paradigm
2) Due to the acquisition of secondary key behavioral characteristics following
tumor genomic changes
-> Tumor aggressiveness, progression, invasion and recurrence define early and late
stage cancers, and the severity of the disease
Definition of cancer
1) A tumor cell DNA disease – Cell-centric paradigm
2) Due to the acquisition of secondary key behavioral characteristics following
tumor genomic changes (Hanahan & Weinberg, Cell 2001, 2011)
Tis T1 T2 T3 T4
T-Stage
N-Stage
M-Stage
Tumor invasion
Tumor progression
-> Tumor recurrence
Early-metastasis (venous emboli)
Tumor grade differentiation
Tumor aggressiveness
(driver mutations, CIN, MSI, CIMP…)
Tumor progression, invasion and recurrence are dependent on pre-existing immunity
and on Immunoscore
Pre-existing immunity is determining the fate and survival of the patient
Pre-existing immunity is determining the likelihood of response to immunotherapy
Novel paradigm
Tis T1 T2 T3 T4
T-Stage
N-Stage
M-Stage
Tumor invasion
Tumor progression
-> Tumor recurrence
-> death
Early-metastasis (venous emboli)
Tumor grade differentiation
Tumor aggressiveness
(driver mutations, CIN, MSI, CIMP…)
Immunoscore Immune contexture
Neutrophils
Eosinophils
Basophils
Mast cells
Red cells
T-lymphocytes
Plasma
B cells
B-lymphocytes
NK cells
NKT cells
Macrophages
mDC
TH1
TH2
TH17
TH3
Treg
TEM
Tmemory
TEMRA
T-cytotoxic
iDC
Tumor cells
pDC
Lymph vessels
Blood vessels
Tumor microenvironment
Quantification of immune cell densities (n=415 Patients, 6640 IHC) revealed the
major positive role of cytotoxic and memory T cells for patient’s survival
Galon J et al. Science 2006
Immunoscore Immune contexture
The foundation a new concept
Dis
ease F
ree S
urviv
al
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140 160 180
Survival (months)
I
II
III
AJCC/UICC-TNM
Current prognosis classification
Tumor Histopathologic Findings
NS
I II
III
High-CD45ROCT/IM High-CD3CT/IM
Dis
ease F
ree S
urviv
al
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80 100 120 140 160 180
Survival (months)
CD3CT/CD3IM evaluation
plus
CD45ROCT/CD45ROIM evaluation
Immune cells analysis
II
III
Low-CD45ROCT/IM Low-CD3CT/IM
IV IV
**
NS
I
Coordinated adaptive immune reaction more than tumor invasion predicts
clinical outcome ->
Importance of the distribution of the adaptive immune reaction compared to tumor invasion
Galon J et al. Science 2006
Parameter
• T-stage
• N-stage
• Differentiation
• Immune
HR
1.2
1.4
1.1
1.9
P value
0.25
0.15
0.84
0.00001
COX multivariate analysis (OS) in all stages I, II, III patients
Novel Paradigm
Galon J et al. Science 2006
Galon J et al. Cancer Res. 2007
“Immune Contexture” : nature, functional orientation, density, and location
within distinct tumor regions, of a natural in situ immune reaction
“Contexture: the act of assembling parts into a whole; an arrangement of interconnected parts”
Invasive margin (IM)
immune
tumor
Center of the tumor (CT)
immune
tumor
• Evolution of the tumor microenvironment with tumor progression?
Understanding the evolution of the immune response
with tumor progression using systems biology
-> Spatio-temporal dynamics
of the immune response with tumor progression
Tis T1 T2 T3 T4
∆ ∆ ∆ ∆
T-Stage
• Immune escape mechanisms in human tumors?
Bindea G et al. Immunity, 2013
Implications for cancer classification and therapies
To the Immunoscore
From the Immune contexture
(A simple and powerfull Immune Test)
(Complexity of intratumor immune reaction)
T-STAGE N-STAGE M-STAGE Tumor cell extension and invasion
CD3+ T cells CD8+ T cells Density Location (CT, IM) Immunoscore Host immune response
Mucinous CCS1
CCS2 Medullary
Adeno. NOS
Serrated
Signet ring cell
Enterocyte
Goblet-like
Transit-amplifying-S
Inflammatory
Stem-like
CIN
MSI
CIMP CCS3 Transit-amplifying-R
BRAF
APC
KRAS
TP53
Morphology Cell of origin Molecular pathway Mutation status Gene expression
Tumor cell characteristics
Ways to classify
CTNNB1
Micropapillary
Cribriform comedo -type
Galon et al. J Pathol. 2014
Colorectal cancer classifications
Patient 1 (weak) Patient 2 (moderate) Patient 3 (strong)
How to explain “Hot” and “Cold” immune infiltrated tumors ?
CD3 Tumor
Median OS (death)
< 2 years 4.9 years > 15 years
Im0 Im2 Im4 Immunoscore CD3/CD8 Center/Margin
Prolonged survival in patients with high Immunoscore (Im)
based on the evaluation of CD45RO-CT/IM and CD8-CT/IM
Mlecnik et al. J Clin Oncol 2011
Survival (months)
P<0.0001
Dis
ease-F
ree S
urv
ival (%
)
0
20
40
60
80
100
0 20 40 60 80 100 120 140 160 180
Im4
Im2
Im1
Im0
Im3
AJCC/UICC-Stage I-III
Dis
ease-F
ree S
urv
ival (%
)
Im4
Im2
Im1
Im0
Im3
0
20
40
60
80
100
0 20 40 60 80 100 120 140 160 180
Survival (months)
P<0.0001
AJCC/ UICC-Stage I-IV
Prognostic importance of the in situ immune reaction in
patients with early-stage (Stage I/II) colorectal cancer
Pagès F et al. J. Clin. Oncol. 2009
Stage I cancer Stage II cancer
Dis
ease-F
ree S
urviv
al
(%
)
0
20
40
60
80
100
0 25 50 75 100 125 150 175 200 225
Survival (months)
Stage I patients
P< .0001 CD45ROCT/IM CD8CT/IM
Im4
Im3
Im1-2
Im0
n=602 Stage I/II
Dis
ease-F
ree S
urviv
al
(%
)
0
20
40
60
80
100
0 25 50 75 100 125 150 175 200 225
Survival (months)
Stage II patients
P< .0001 CD45ROCT/IM CD8CT/IM
Im4 Im3
Im1-2
Im0
THE IMMUNOSCORE
AS A NEW POSSIBLE
APPROACH IN THE
CLASSIFICATION OF
CANCER
Naples, Italy, Feb 2012
Organizer: P Ascierto, J. Galon,
Principal investigator: J. Galon
Galon J et al. J. Transl. Med. 2012
Galon J et al. J. Pathol. 2014
The IMMUNOSCORE
Faisability IHC automate High-resolution scanner
whole slide quantification
Digital pathology
Immunoscore
-> Standardized Operating Procedure
-> Today’s tools for modern pathologists
Galon J et al. J. Transl. Med. 2012
Galon J et al. J. Pathol. 2014
Immunoscore (I) using whole slide FFPE
Routine whole slide stainings & precise image quantification
CT
IM
Tissue
Immunostaining Definition of Tumor Regions
Density plots
I
CD3
World Immunotherapy Council inaugural meeting (Feb 2012)
Support (moral) from the World Immunotherapy Council (WIC), and support from societies including, EATI, BDA, CCIC, CIC, CRI, CIMT, CSCO, TIBT, DTIWP, ESCII, NIBIT, JACI, NCV-network, PIVAC, ATTACK, TVACT…
Worldwide Immunoscore consortium (PI: J Galon)
The Immunoscore as a New Possible Approach for the Classification of Cancer
Assay harmonization
Immunoscore meetings : - Feb 2012, Italy - Dec 2012, Italy - Nov 2013, SITC, USA - Dec 2013, Italy - Jan 2014, Qatar - Jul 2014, Paris, France - Nov 2014, SITC, USA - Nov 2015, SITC, USA
Switzerland
Australia
Netherland
Belgium
Qatar
Canada
Japan
China
Immuno score
Italy
Austria
Germany
Sweden
France USA
UK Czech R
India
Switzerland
(23 Centers, 17 countries: >3000 patients)
Worldwide Immunoscore consortium (PI: J Galon)
Study design
Clinical data
Center
External
Statistician
(Mayo)
All
Centers
All
Centers
Referent
Center
Immunoscore Raw data
Clinical data
TS IVS EVS
>1000 >1000 >1000 Pts.
Encrypted data
Analysis
QA/QC
Impacting all cancers
Colorectal cancer
Bladder cancer Brain cancer Breast cancer Cervical cancer
Melanoma cancer Ovarian cancer Pancreatic cancer Prostate cancer
Kidney cancer Lung cancer Liver cancer
Metastasis analysis
Other cancers
Brain Metastasis
Breast cancer Kidney cancer Lung cancer Melanoma
Multiple primary tumors
One metastatic site
Immunoscore within brain metastasis
Berghoff A. et al. OncoImmunol. In press
50% OS 25% OS
Immunoscore predicts overall survival and long-term survival in patients
with Brain Metastases
Immunoscore in brain metastasis and survival
Immunoscore quantification (CD3, CD8, in CT and IM regions) within
Brain Metastases (n=116 patients)
Berghoff A. et al. OncoImmunol. In press
Primary Tumor
Tis
T1
T2
T3
T4 Distant Metastasis
(M+)
Early stages (N0M0)
What drives metastasis ?
Adenoma
Dysplasia
Hyperplasia
Carcinoma
APC
KRAS
18q LOH
TP53
17p LOH
SMAD2/3
BRAF
??
Genetic alteration Carcinoma sequence
The carcinoma sequence and tumor development
Metastasis (M1) ?
?
Early-Metastasis (venous emboli, perineural invasion)
Synchronous Metastasis (M1)
Metachronous Metastasis (recurrence)
What drives metastasis?
Mlecnik et al. Science Transl Med. accepted
What drives metastasis?
CIN are similar in M0 and M1 patients
Chromosomal Instability (CIN) ? Cohort 1: n=276
Cohort 2: n=205
M0
M1
M0
M1
M0 M1
All
no
t sig
nific
an
t
48
ma
in c
an
ce
r ge
ne
s
Chromosomal instability pattern (CIN) Chromosomal instability pattern in M0/M1 M1 M0
Mutation pattern Tumor gene expression pattern
Genomic alterations in tumors
-> No significant difference between M0 and M1 cancer patients
ma
in c
an
ce
r ge
ne
s
M1 stage
What drives metastasis?
Mlecnik et al. Science Transl Med. accepted
FBXW7mut
Metastasis risk depending on lymph vessels and GZMB densities
Risk of metastasis increases:
- from 0% (blue) for tumors with High lymph vessels (IM) + high High GZMB (CT)
- to 49% (red) for tumors with Low lymph vessels (IM) + Low High GZMB (CT)
Quantification of blood and lymph vessel densities and GZBM+ cell densities
within primary tumors (CT and IM regions)
Blood vessels
(CT & IM)
Lymph vessels (IM)
GZMB+ (CT)
0%
8%
Decreasing density of 2 markers
Analysis of metastasis frequency
Specific genotype and gene profiling in MSI-H patients
Chromosomal instability, mutation patterns, and gene expression profiling
in 270 MSI-H and MSS patients
Patients with MSI-H have multiple Frameshift mutations (FSmut)
MSS MSI-H
cohort 1 cohort 2
0
ExomeSeq Multiplex FSmut validation
Mlecnik et al. accepted
MSI
MSS
Anti-TGFBR2mut
Specific T-cells
MSI-H patients with TGFBR2 FSmut have
anti-TGFBR2-FSmut T-cells in their tumor
MHC-dextramer
Specific stainings
Mlecnik et al.
accepted
- +
MSI-H patients with TGFBR2 FSmut have
anti-TGFBR2-FSmut T-cells able to kill APCA2.1/FSmutP2 cells
Mlecnik et al. accepted
Immunoscore high (I3, I4) patients have prolonged
survival regardless of the MSI status
Mlecnik et al. accepted
HR
1.00
1.32
0.56
0.44
P-value
0.99
0.27
0.024 *
0.001 *
markers
MSI
N stage
VELIPI
Immunoscore
Cox multivariate analysis for DSS
Functional
orientation
IFNG
IL12
TBX21
IRF1
STAT1
MADCAM1
ICAM1
VCAM1
ITGAE
Quantification (cells/mm2)
Adaptive immunity, cytotoxic, memory T cells
Tumor center, Margin, Tertiary lymphoid ilets
Immune contexture
Immunologic
Constant
of Rejection (other diseases)
CX3CL1
CXCL9
CXCL10
CCL5
CCL2
GZMA
GZMB
GZMH
PRF
GLNY
Type
Density
Location
Immunoscore
The overlap between the immune contexture, the immunologic constant of rejection and the Immunoscore
CXCL13
Galon J et al. Immunity 2013
IL21, IL15
NON-Immune signatures
Prognostic Predictive
Mechanistic
The overlap between prognostic, predictive and mechanistic immune signatures
Prognostic
Mechanistic
Predictive
Immune
contexture
IMMUNE signatures
Galon J et al. Immunity 2013
Church S & Galon J Immunity 2015
Immunoscore
Th1
Cytotoxicity
Chemokines
Cytokines
Adhesion
Tumor
regression
Immune-
response
intensity Tumor growth
Recurrence
Tumor growth slowed
No recurrence
The continuum of cancer immunosurveillance: predictive, prognostic and mechanistic signatures
Galon J et al. Immunity 2013
Stratification of cancer based on the immune status
MSI-H MSS^ MSS/CIMP.hi MSS MSS-CIMP.lo
Tumor classification
Immune classification
IMMUNE
-> Importance of having standardized immune Assays
Franck Pagès
Tessa Fredriksen
Stéphanie Mauger
Florence Marliot
Lucie Lafontaine
Amélie Bilocq
Amos Kirilovsky
Marie Tosolini
Helen Angell
Sarah Church
Pauline Maby
Maximilian Waldner
Bernhard Mlecnik
Gabriela Bindea
Anne Berger
Anna Obenauf
Michael Speicher
Tchao Meatchi
Christine Lagorce
Patrick Bruneval
Galon lab. INSERM, CRC, Paris, France
Dpt. of General and Digestive Surgery, HEGP, Paris, France
Institute for Genetics, Graz, Austria
Dpt. of Pathology, HEGP, Paris, France
Philippe Wind
Dpt. Digestive Surgery and Pathology, Avicenne, Bobigny, France
Team 13, Cordeliers Research Center, France
Hervé Fridman
Martin Asslaber
Dpt. Pathology, Graz hospital, Graz, Austria
Dpts. of Pathology from the 23 Centers Worldwide
SITC and all supportive societies Definiens, PathForce, MedImmune
Mihaela Angelova
Pornpimol Charaoetong
Zlatko Trajanoski
Institute for Bioinformatics, Innsbruck, Austria
University Clinic, Erlangen, Germany
Christopher Becker Maximilian Waldner
Jean Baptiste Latouche
Rouen University, France
Jérôme GALON
INSERM, Laboratory of Integrative Cancer Immunology Cordeliers Research Center, Paris, France
LabEx
6th "Innovative therapy, monoclonal antibodies and beyond" Conference
Milan, Italy,
January 22nd 2016
Prognostic and predictive value of Immunoscore