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