Preclinical Strategies to Support

the Clinical Development of the

Anti-PD-1 Antibody Keytruda

Brian Long

Tumor Models: July 22, 2015

1

Immune Checkpoint Regulation of T cell

Activation

2

Naive

T cell

Primary

Stimulation

Activated

T cell

CD28

CD80/86

TCR

MHC CTLA-4 PD-1

TNFRSF

- - +

APC or

Tumor cell

•CD28

•OX40

•CD137

•CD27

•GITR

•Activation •Inhibition

•Homeostasis

•CTLA-4

•PD-1

•LAG3

•TIGIT

•BTLA

• Immunomodulatory receptors regulate T cell activation

• ‘Immune checkpoints’ serve to limit collateral damage from immune responses

• Immune system plays a critical role controlling tumor growth and progression

• Numerous receptors are deregulated in various human cancers

• Cancer immunotherapy: 2013 Breakthrough of the Year (Science)

• Only 3 FDA-approved immunotherapies to date

The PD-1 Pathway is a Critical

Regulator of T Cell Functional Activity

• PD-1 receptor is expressed

on T cells, B cells, natural

killer (NK), and myeloid cells

• PD-L1 (ligand) expressed by

immune cells as well as non-

hematopoietic lineages

• PD-1 expression on activated

T cells limits effector phase of

differentiation

3

Nguyen LT & Ohashi PS, Nature Reviews Immunology 15, 45–56 (2015)

• T cell ‘exhaustion’ can be reversed after PD-1 blockade

• Tumor cells can ‘hijack’ PD-1 signaling for immune escape

Pembrolizumab (Keytruda) is a Potent

Antagonistic Antibody Against PD-1

PD-L1/L2 engage PD-1 receptor on T-

cells and inhibit T-cell activation

Pembrolizumab blocks PD-L1/L2 binding to

PD-1 and re-activates T-cells so they can

destroy tumor

(muDX400 clone) anti-PD-1 surrogate

antibody for use in preclinical tumor models

Pembrolizumab (Keytruda) Displays Impressive Anti-Tumor

Activity in Multiple Different Cancer Indications

Keytruda is now in more than 110 clinical trials in over 30 different tumor types

Leveraging Preclinical Models to Aid in the

Understanding of Anti-PD-1 Immunotherapy

6

Tumor Models

Subcutaneous Orthotopic Metastatic

GEMMs Humanized

Mouse Models

Human Tumor

Histoculture

Syngeneic Models Model

Mouse Syngeneic Tumor Models

7

Anti-mouse PD-1 Surrogate is Efficacious

in Syngeneic Tumor Models

8

M C 3 8 M o d e l

0 5 1 0 1 5 2 0

0

2 5 0

5 0 0

7 5 0

1 0 0 0

1 2 5 0

1 5 0 0

m Ig G 1 C n t r l 5 m g /k g

m u D X 4 0 0 5 m g /k g

D a y s P o s t T r e a tm e n t

Tu

mo

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me

(m

m3)

C R = 7 / 1 2

0 5 1 0 1 5 2 0 2 5 3 0

0

2 5 0

5 0 0

7 5 0

1 0 0 0

1 2 5 0

1 5 0 0

Tu

mo

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(m

m3)

m u Ig G 1 C tr l 5 m g /k g

m u D X 4 0 0 5 m g /k g

C R = 2 /1 0

D a y s P o s t T r e a tm e n t

C M 3 M o d e l

mIgG1 Isotype Control

Anti-PD-1 (muDX400)

Solid Tumor Model

Hematological

Tumor Model

9

Differential Response to Anti-PD-1

Treatment in Syngeneic Tumor Models M C 3 8 M o d e l

0 5 1 0 1 5 2 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

m u Ig G 1 C n trl 5 m g /k g

m u D X 4 0 0 5 m g /k g

D a y s P o s t T r e a tm e n t

Tu

mo

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olu

me

(m

m3)

C R = 7 /1 2

B 1 6 -F 1 0 M o d e l

0 5 1 0 1 5 2 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

Tu

mo

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olu

me

(m

m3)

m u D X 4 0 0 5 m g /kg

m u Ig G 1 C trl 5 m g /kg

D a y s P o s t T r e a tm e n t

Tu

mo

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olu

me

(m

m3)

0 5 1 0 1 5 2 0

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0m u Ig G 1 C trl 5 m g /k g

m u D X 4 0 0 5 m g /k g

D a y s o f T re a tm e n t

R E N C A M o d e l

High Response

Partial Response

Low Response

Red = Maximum Response

Black = Minimum Response

Characterization of Mouse Syngeneic Tumor

Models: Multi-pronged Approach

Baseline and

Post a-PD1

Treatment

Gene Expression Profiling

Whole tumor

Isolated cell populations

Blood/LN

Whole Exome Sequencing

Cell lines and matched normal tissue

Immunohistochemistry

Cell Markers

Immunomodulators

Flow Cytometry

TIL phenotype

Myeloid cells

Immunomodulator expression

0 4 8 1 2 1 6 2 0 2 4

0

5 0

1 0 0

A ll s e q u e n c e d tu m o r s

M o n th s

Pe

rc

en

t p

ro

gre

ss

ion

-fre

e

T ra n s v e rs io n h ig h (n = 1 6 )

T ra n s v e rs io n lo w (n = 1 8 )

Mutational Load and Neoantigen Burden are Associated

with Response to Pembrolizumab in NSCLC

11

0 4 8 1 2 1 6 2 0 2 4

0

5 0

1 0 0

C o h o r t 1 - D is c o v e r y

M o n th s

Pe

rc

en

t p

ro

gre

ss

ion

-fre

e

H ig h n o n s y n o n y m o u s b u rd e n (n = 8 )

L o w n o n s y n o n y m o u s b u rd e n (n = 8 )

0 4 8 1 2 1 6 2 0 2 4

0

5 0

1 0 0

N e o a n t ig e n b u rd e n

M o n th s

Pe

rc

en

t p

ro

gre

ss

ion

-fre

e

H ig h n e o a n tig e n b u rd e n (n = 1 7 )

L o w n e o a n tig e n b u rd e n (n = 1 7 )High non-synonymous burden

Low non-synonymous burden High neoantigen burden

Low neoantigen burden

Smoking signature (high transversion/transition)

No smoking signature (low transversion/transition)

T Chan et al. Science 2015

Tumor (B) Tumor (A)

Hyper-mutated

Number of genes

with mutations is

very high

Number of genes

with mutations is

low

Many

Abnormal

Proteins

(Antigen Presentation)

Immune

System Gets

Activated

Tumor Adaptive

Immune Resistance

[ up-regulates PD-L1 ]

Mutational Burden and Response to Anti-PD-1

Treatment in Mouse Syngeneic Tumor Models

Nu

mb

er

of

Mu

tati

on

s

High/

Partial

Poor

● Responder models trend towards higher mutational burden

● Characterization of additional models ongoing

Postdose Change in 10-gene Blood IFNγ Signature

Predicts Clinical Response to Pembrolizumab

13

Presented by: Mark Ayers, AACR, 2015

14

Up-regulation of Immune, IFNg, and PD-L1 Signatures

Correlates with Efficacy in Murine Syngeneic Tumors

IFNg Signature

Low Partial High Low Partial High

Immune Signature

Low Partial High

PDL1 Signature

Gene Signature Correlates to Observed In vivo Efficacy:

Post

Treatment

● Tumors were harvested 4 days following 2 doses of anti-PD-1 treatment

● Expression of each gene was normalized to the mean of the isotype control treatment

group and shown as log10 fold change

15

Graphic View of Immune Checkpoint Blocker Combinations

MK-4166

Combining Anti-PD-1 with Chemotherapies, Small Molecule

Therapies and Immunotherapies Results in Enhanced Anti-tumor

Activities

16 • Combinations were well tolerated at each of the dosing regimens shown

(Preclinical Model Data)

Model muDX400

Treatment

Response to

muDX400 Combination Partner

Response

to Partner

Therapy

Response to

Combination

CT26 10 mg/kg

(ip, q5d x 5) 46% TGI

Trametinib

( 1mg/kg, po, qd) 80% TGI

96% TGI

25% CR

MB49 10mg/kg

(ip, q5d x 5) 54% TGI

Trametinib

( 1 mg/kg, po, qd) 60% TGI 89% TGI

RENCA 5mg/kg

(ip, q5d x 5) 44% TGI

Pazopanib

( 100 mg/kg, po, bid) 60% TGI 81% TGI

RENCA 5mg/kg

(ip, q7d x 4) 59% TGI

Carboplatin

(40 mg/kg, ip, q7d x 4)

63% TGI

20% CR

98% TGI

33% CR

RENCA 5mg/kg

(ip, q7d x 4)

43% TGI

8% CR

Gemcitabine

(120 mg/kg, ip, q7d x 4)

90% TGI

17% CR

94% REG

50% CR

MC38 5mg/kg

(ip, q5d x 5)

88% TGI

33% CR

Anti-CD137

(5 mg/kg, ip, q5d x 5)

94% TGI

33% CR 100% CR

CT26 10 mg/kg

(ip, q5d x 6)

54% TGI

16% PR

Entinostat

(15 mg/kg, po, qd) 60% TGI

93% TGI

67% CR, 16% PR

CT26 10 mg/kg

(ip, q5d x 6)

54% TGI

16% PR

Vorinostat

(150 mg/kg, ip, qd) 40% TGI

87% TGI

25% CR, 16% PR

“Disaster” Check: Anti-PD-1 and

Dexamethasone + Carboplatin + Paclitaxel

17

P = Paclitaxel

C = Carboplatin

D = Dexamethasone (High (5mg/kg); Low (1mg/kg))

• The immunosuppressive (dexamethasone) dose not impair anti-PD-1 activity

Combining Anti-PD-1 with Anti-GITR Results in

Synergistic Efficacy

18

D a y s o n S tu d y

Tu

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m (

mm

3)

0 7 1 4 2 1 2 8 3 5

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

Is o ty p e C o n tro ls (0 /1 0 C R )

A n ti-P D -1 + A n ti-G IT R

(1 0 /1 0 C R )

A n ti-G IT R (2 /1 0 C R )

A n ti-P D 1 (2 /1 0 C R )

0 7 1 4 2 1 2 8

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

A n ti-G IT R (0 /1 0 C R )Is o ty p e C o n tro ls (0 /1 0 C R )

A n ti-P D -1 (0 /1 0 C R )A n ti-P D -1 + A n ti-G IT R

(7 /1 0 C R / 2 P R )

D a y s o n S tu d y

Tu

mo

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me

(m

m3)

7 3 % T G I

8 1 % T G I

• Combination benefit was also observed in additional models

MC-38 (Starting Tumor Volume = 300 mm3)

MB-49 (Starting Tumor Volume = 100 mm3)

Genetically Engineered Mouse

Models (GEMMs)

19

Published Data with GEMMs Have Not Reported

Complete Tumor Responses (To Date)

EGFR Del 19 NSCLC EGFR Del 19/T790M NSCLC EGFR L858R/T790M NSCLC

8 weeks 7 weeks 9 weeks

Akbay ES et al. Cancer Discovery 2013;3:1355-63

BRAFV600E

Melanoma

0 Days

Hu-Lieskovan S et al., Sci Transl Med 2015;7:279ra41 Winograd R et al. Cancer Immunol Res 2015;3:399-411

KPC (KrasG12D, Trp53R172H) Pancreatic

~5 Days

The Anti-mouse PD-1 Surrogate DX400 is

Not Active in GEMMs of NSCLC

21

0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7

0

2 5

5 0

7 5

1 0 0

Is o ty p e C o n tro l (N = 1 7 )

A n ti-P D -1 (N = 1 8 )

W e e k s P o s t T u m o r In d u c tio n

Pe

rc

en

t S

urv

iva

l

1 0 m p k , IP , Q 5 D x 6

0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2

0

2 5

5 0

7 5

1 0 0

Is o ty p e C o n tro l (N = 1 1 )

W e e k s P o s t T u m o r In d u c tio n

Pe

rc

en

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urv

iva

l

A n t i-P D -1 (N = 1 2 )

2 0 m p k , IP , Q 4 D x 8

KRASG12D p53 fl/fl NSCLC KRASG12D PTEN fl/fl NSCLC

• The role of GEMMs in modeling responses to tumor immunotherapies

remains to be determined (the jury is still out)

• Each of the models needs to be characterized more extensively for

mutational load and additional immune signatures (e.g., IFNg signature)

• Newer GEMMs being explored include human PD-1 knock-in mice to permit

testing of the anti-human PD-1 antibody pembrolizumab

Humanized Mice

22

Humanized Mice (HuMice) Offer a New Platform for

Prioritizing Pre-clinical Immuno-Oncology Programs

23

• Allow for the evaluation of human clinical antibodies in mice with a human immune system – avoid the need to generate mouse surrogate antibodies

• Tumor models can be selected based on disease indication and genetically defined biomarkers to identify a responder ID

• Can be used with cell line-derived tumor xenografts (CDX) as well as patient-derived tumor xenografts (PDX)

• Allows the testing of SOC & exploratory combination partner drugs to more completely understand mechanisms of synergy and antagonism

Strategy for Selecting The Human CDX Models to

Determine the Anti-Tumor Activity of Pembrolizumab

24

• ~40 models were originally selected based on their ability to grow in immune-

deficient mice (nude mice and/or SCID mice)

• For each of the models PD-L1 expression was evaluated by gene expression

profiling, fluorescent activated cell sorting (FACS) analysis, and

immunohistochemistry

• Models expressing PD-L1 were then further prioritized based on expression

levels of MHC-I, MHC-II, and mutational load

• Prioritized models were HLA-phenotyped to help identify best cohort of mice to

inoculate

LOX Melanoma (High) A375 Melanoma (Medium) UACC-62 Melanoma (Low)

PD-L1

Co

un

t

101

102

103

104

105

106

107

0

54

108

162

216

PD-L1

Co

un

t

102

103

104

105

106

107

0

122

245

367

489

Validation of PD-L1 Expression in the Panc 08.13

Human Pancreatic Cancer Cell Line

25

FACS (cells) FACS (xenografts) IHC

HLA-ABC = A*02:01 / A*02:01

Model Tissue Latency Time

(Days)

Log10

PD-L1

Log10

MHC-I # Mutations HLA-ABC Class I

PANC-08.13 Pancreas

10 2.80 2.54 52 A*02:01 / A*02:01

ISO PD-L1

ISO -IFNg +IFNg

26

Pembrolizumab (Keytruda) Significantly Inhibits the Growth

of Human Panc 08.13 Tumor Xenografts in HuMice

• HLA-score = 1/32

0 4 8 1 2 1 6 2 0 2 4

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

K e y tru d a (5 m p k , IP , Q 5 D )

Is o ty p e C o n tro l (5 m p k , IP , Q 5 D )

7 8 % T G I * *

D a y s o n S tu d y

Tu

mo

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me

(m

m3)

0 4 8 1 2 1 6 2 0 2 4

-3 0

-2 0

-1 0

0

1 0

2 0

D a y s o n S tu d y

Bo

dy

We

igh

t (%

Ch

an

ge

)

0 4 8 1 2 1 6 2 0 2 4

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

D a y s o n S tu d y

Tu

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(m

m3)

Is o ty p e C o n tro l

** P < 0.01

0 4 8 1 2 1 6 2 0 2 4

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

D a y s o n S tu d y

Tu

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(m

m3)

K e y tr u d a

Pembrolizumab Treatment Effectively Targets

PD-1 on Human Lymphocytes (FACS)

27 To

tal C

D45+

To

tal C

D3+

To

tal C

D4+

Th

elp

er

Tre

g

CD

8+

0

2 0

4 0

6 0

8 0

1 0 0

B lo o d

Pe

rc

en

tag

e o

f c

ell

s (

%) Is o ty p e Ig G 4

K e y tru d a

To

tal C

D45+

To

tal C

D3+

To

tal C

D4+

Th

elp

er

Tre

g

CD

8+

0

1 0

2 0

3 0

4 0

B lo o d

Pe

rc

en

tag

e o

f P

D1

+ c

ell

s (

%)

Is o ty p e Ig G 4

K e y tru d a

Little to no PD1 detectable

on lymphocyte populations

Increased lymphocyte

presence after treatment

with pembrolizumab

Panc08.13 tumor bearing HuMice

Determining the Anti-tumor Activity of Pembrolizumab in

Human PDX Tumor Models (Champions Oncology)

28

• All of Champions melanoma and NSCLC models were evaluated for their ability

to grow in immune-deficient mice

– Tumors that grew too slowly or too quickly were excluded

• Champions provided molecular profiling data to Merck Bioinformatics (RNA-seq

& WES) to analyze for expression levels of PD-L1, MHC-I, MHC-II, and

mutational load

• Champions prioritized models that were HLA-phenotyped as HLA-A*02

• Humanized NOG mice (HLA-A*02 phenotype) were supplied by Taconic

Model Tissue Doubling

Time (Days)

Log10

PD-L1

Log10

MHC-I # Mutations HLA-A Class I

CTG-0202 Melanoma 7 2.97 5.62 Not Available A*02

CTG-0743 NSCLC 8 4.53 6.1 231 A*02

Pembrolizumab (Keytruda) Significantly Inhibits the Growth of

CTG-0202 Human Melanoma Tumor Xenografts in HuMice

0 7 1 4 2 1 2 8

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

K e y tru d a (2 .5 m p k , IP , Q 5 D )

Is o ty p e C o n tro l (2 .5 m p k , IP , Q 5 D )

5 2 % T G I *

D a y s o n S tu d y

Tu

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me

(m

m3)

0 7 1 4 2 1 2 8

-3 0

-2 0

-1 0

0

1 0

2 0

D a y s o n S tu d y

Bo

dy

We

igh

t (%

Ch

an

ge

)

* P < 0.05

0 7 1 4 2 1 2 8

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2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

D a y s o n S tu d y

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(m

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Is o ty p e C o n tro l

0 7 1 4 2 1 2 8

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

D a y s o n S tu d y

Tu

mo

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me

(m

m3)

K e y tru d a

3 /1 0 R E G

Effective target

engagement by FACS

Increased T-cell Infiltration Into CTG-0202 Tumors

Following Treatment with Pembrolizumab

30

CD3

CD8

Isotype IgG4 Keytruda

Pembrolizumab (Keytruda) Significantly Inhibits the Growth of

CTG-0743 Human NSCLC Tumor Xenografts in HuMice

** P < 0.01

0 7 1 4 2 1

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

K e y tru d a (5 m p k , IP , Q 5 D )

Is o ty p e C o n tro l (5 m p k , IP , Q 5 D )

6 7 % T G I * *

D a y s o n S tu d y

Tu

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me

(m

m3)

0 7 1 4 2 1

-3 0

-2 0

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2 0

D a y s o n S tu d y

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dy

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igh

t (%

Ch

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0

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4 0 0

6 0 0

8 0 0

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D a y s o n S tu d y

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m3)

Is o ty p e C o n tro l

0 7 1 4 2 1

0

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4 0 0

6 0 0

8 0 0

1 0 0 0

D a y s o n S tu d y

Tu

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(m

m3)

K e y tr u d a

Summary

• Supporting the clinical development of pembrolizumab (Keytruda) was

complex and involved development and characterization of a diverse

panel of preclinical models in immune-competent mice

• Mouse syngeneic tumors remain the primary models of choice to

interrogate the anti-tumor activities of novel cancer immunotherapies

and combinations

• The role of GEMMs to support the development of cancer

immunotherapies remains to be determined but is likely to involve the

development and characterization of a new generation of models

• The emergence of humanized mice offers a promising platform that

could allow the testing of cancer immunotherapies in an indication-

specific manner and best identify the optimal combination therapies

• A novel set of next generation biomarkers are simultaneously being

investigated in both preclinical models and ongoing clinical trials to

better understand which patients and indications are most likely to

respond to cancer immunotherapies

32

Targeting Immunomodulators: The Challenges

33 33

• What are the determinants of

responsiveness?

• What can be done to increase the

representation of tumor-reactive

T cells, and to trigger their activation?

• How do we select patients most likely

to respond to these agents?

• How do we prioritize new targets and

new combinations?

– Which mechanism is best for which

tumor?

Melero I, et al. Clin Cancer Res. 2013;19:997-1008. ©2013 by American Association for Cancer Research.

6 8 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 2 8 3 0 3 2

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

1 6 0 0

1 8 0 0

D a y s p o s t tu m o r im p la n ta tio n

M e an S E M

(n = 1 0 )

Tu

mo

r v

olu

me

(m

m3)

Differential gene

expression correlates to

response in clinic

Verify response prediction in

mouse models

Select appropriate models to

test immunotherapy

Test immunotherapy as mono or

combination

Project into human

tumor data to select

indications for mono

therapy, combo

therapy or need for

additional resistance

biomarker

BALB/c

MOUSE TUMOR MODELS

Genes Expressed In

Responders or Non-responders

Clinical

Trials

Integrated analysis for

preclinical data

Insights from Both Clinical Data and Preclinical Data

Fuel New Hypotheses

Clinical

Trials

Acknowledgments

35

MRL - Boston

Elaine Pinheiro

Heather Hirsch

Michael Rosenzweig

Brian Long

Yanhong Ma

Mingmei Cai

Doug Linn

Brian Haines

Jason Laskey

Marlene Hinton

Samanthi Perera

Long Cui

Nicolas Solban

Melissa Chenard

Sloan Stribling (KW)

Yaolin Wang (KW)

David Hill (WP)

Razvan Cristescu

Mark Ayers (WP)

Lily Moy

Mike Caniga

Kalyan Chakravarthy

Robert Faltus

Ray Moniz

Samik Basu

Tony Mannarino

Kallol Roy

Clinical Oncology

David Kaufman

Eric Rubin

Andrew Denker

Development Teams

MK-3475 Team

MK-4166 Team

Manufacturing - Europe

Marc Bastiaansen

Nachu Chakravarthy

Gargi Maheshwari

Manoj Sharma

Partner Companies

Champions Oncology

Charles River

CrownBio

InvivoTek

JAX

Pharmaron

Pure Transplant

Taconic

VivoPharm

Wuxi

MRL - Palo Alto

Rob Kastelein

Gary Starling

Terri McClanahan

Svetlana Sadekova

Jennifer Yearley

Laurence Fayadat-Dilman

Romina Riener

Selva Sukumar

Doug Wilson

Chris Gibson

Jerelyn Wong

Manjiri Sathe

Jeanine Mattson

Erin Murphy

Ruban Mangadu

Uyen Phan

Wendy Blumenschein

Sripriya Dhandapani

Shuli Zhang

Luis Zuniga

Barbara Joyce-Shaikh

Dan Cua

Rene De Waal Malefyt

Amy Beebe

Danling Gu

Smita Mauze

Venkataraman Sriram

Joe Phillips

Debbie Law