IMMUNOTHERAPY: TRANSFORMATION OF THERAPIES FOR BLOOD CANCER

How much cancer can we cure with the immune system?

Drew M. Pardoll, MD, PhDAbeloff Professor of Oncology, Medicine, Pathology and Molecular Biology and Genetics Co-Director, Cancer Immunology and Hematopoiesis Program The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore, MD

DisclosuresI disclose the following relationships

• Bristol-Myers Squibb o Grant/research supporto Holder of intellectual property rights

• GlaxoSmithKline o Consultant

• Medimmune/AstraZenecao Consultant

• Amgeno Consultant

• Pfizero Consultant

The PerfectAnticancerAgent

DiversityT cells - 1018

Antibodies - 1022

SpecificityCan distinguish asingle methyl group

WeaponryNO, superoxides,HOCl, H2O2,FasL, TRAIL, Perforin,Granzyme B,Myeloperoxidase,Complement,Phagocytes

“Ten years fromtoday, >50% of patients with inoperable cancer will be receiving immunotherapy”-Drew Pardoll January, 2014

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Discovery of antibodies/Magic bullet theory

Monoclonalantibodies

Elucidation oftumor associated

antigens

Elucidation ofImmune checkpoints

Elucidation of howT cells recognize antigen

Coley’s toxins

Approval of IL-2 for melanoma

TIL formelanoma

aCTLA-4 approvedfor melanoma

aPD-1 formelanoma,renal, lung ca

aPD-1 in10 morecancers

CAR Ts

Advances in Cancer Immunology & Immunotherapy

ImmuneResponse

The amplitude of immune responses is determined by a balance of positive signals (antigen+costimulation)

and negative forces (immune checkpoints)

IMMUNE CHECKPOINTS = BRAKESCTLA-4 Treg PD-1 LAG3 … (>25)

Vaccines

IMMUNIZATION = ACCELERATORSignal 1 – Antigen Signal 2 – Costimulation (CD28 …)

Checkpointblockade

Pardoll 2012

Ant

igen

pre

sent

ing

cell

or tu

mor

cel

l

T cell

Targeting immune

regulatory pathways in

cancer therapy

Inhibition of immune

checkpoints, or stimulation of

immune enhancers, with monoclonal

antibodies or small molecules

Activation(cytokines, lysis, prolif.,

migration)

Role of CTLA-4 vs PD-1 in Suppressing Antitumor Immunity

TumorPD-L1PD-1

(-)(-) (-)

Inhibition

PD-1 expressedTraffic to tumor

APC T cellB7.1/2 CD28

TCR Signal 1MHC-Ag

Tumoror Vaccine

B7.1/2 CTLA-4

+

-

CD8

IgG PD-L1

PD-L1ExpressionisanAdaptationtoT-CellAttack:AdaptiveResistance

IFNg high

IFNg low

Long-term Survival of Patients With Melanoma Receiving Immune

Checkpoint-Blocking Drugs

0 1 2 3 4 5 6 7 8 9 10

100

90

80

70

60

0

50

40

30

20

10Ove

rall

Surv

ival

(%)

Years

IPI (Pooled analysis – mixture of 1st & 2nd line)NIVO Monotherapy (Second-Fourth line)NIVO Monotherapy (First line)

At 5 yr,50% willhave died

Duration of Response and Overall SurvivalLung Cancer

NSCLC Respondersa,b by Histology

Time (Week)0 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136 144 152 160

Vertical line at 96 weeks = maximum duration of continuous nivolumab therapy

a Responses were assessed by modified RECIST v1.0bAll efficacy analyses based on data collected as of September 2013

Squa

mou

sN

on-s

quam

ous

Duration of response up to discontinuation of therapyOngoing responseTime to responseResponse duration following discontinuation of therapy

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Months Since Initiation of Treatment

129 111 82 66 48 35 31 28 20 9 4 3 3 3 2 1 1 1 0 0

Subjects at RiskTotal

All Treated Subjects with NSCLC

Died/Treated Median (95% CI)94/129 9.90 (7.80,12.40)

Prop

ortio

n Su

rviv

al Median OS: 9.9 Months (7.8, 12.4)

1 year OS Rate 42% (48 pts at risk)

2 year OS Rate 24% (20 pts at risk)

At 4 yr,85% willhave died

EGFR, Stat3Raf/MEK

TumorDendritic

Cell

Treg

Tumor-specific

CTL

MF/MDSC

PD-L1

PD-L1

Tumor Antigens

The Hostile Immune Microenvironment of the Tumor Is All Druggable

IL10,TGFb

IL-35Neuritin

IDOArginaseiNOS

PD-1LAG-3Tim3TIGIT

T helperCell

Foxp3

Tbet

HLANeuropilinCTLA4TIGIT

GITR

“Help” topromote CTL

Adenosine

A2aR

Nivolum

ab*Solid*Tumors***************

***

(Topalian)et)al.)NEJM)2012)

Nivolum

ab*Melanoma*******************))

(Weber)ASCO)2013)

Nivolum

ab*Melanoma*****************))

(Grosso)et)al.)ASCO)2013)

MPDL3280a*Solid*Tumors))))))))))))))))))))))))

(Herbst)et)al)ASCO)2013)

MPDL3280a*M

elanoma*******************

(Hamid)et)al)ASCO)2013)

MPDL3280a*NSCLC***************************)

(Sorial)et)al)ECC)2013)

Pembrolizumab*M

elanoma**)))))))))))))))

(Daud)et)al)AACR)2014)

Pembrolizumab*NSCLC************))))))))

(Gandhi)et)al)AACR)2014)

MPDL3280a*Bladder****)))))))))))))))))))))))

(Powels)et)al)ASCO)2014)

Pembrolizumab*Head*&

*Neck*

(Selwert)et)al)ASCO)2014)

Pembrolizumab*M

elanoma*****)

(Ribas)et)al)ASCO)2014)

n=) 42 44 34 94 30 53 113 129 65 55 411

Response*Rates

Unselected 21% 32% 29% 22% 23% 23% 40% 19% 26% 18% 40%

PDQL1)+ 36% 67% 44% 39% 27% 46% 49% 37% 43% 46% 49%

PDQL1)− 0% 19% 17% 13% 20% 15%* 13% 11% 11% 11% 13%

Intra-Tumoral PD-L1 Expression Predicts Response to PD-1/PD-L1 Blockade – 4 FDA-Approved Tests

Presented by: Margaret Callahan

PD-L1PD1Sox10CD68FoxP3CD8

Example Multiplex Immunofluorescence Panel in Melanoma

CD68 PD-L1

Sox10 PD-L1

PD-L1 is Expressed Predominantly on Melanocytes

CD8+ Cytotoxic T Cells (Yellow) Touching CD68+ Macrophages (Pink)

Phenotype Total Touching Count %Cytotoxic T Cells 549 macrophages 106 19.3%

Multispectral Imaging of Merkel Cell Carcinoma Patient Treated With Anti–PD-1

Pre-treatment Post-treatment

Stroma Tumor

Green – PD-L1+ Tumor & MF Yellow – PD-1+ CD8 T cells

Dr. Alex SzalayBloomberg Professor of Physics and Astronomy

Dr. Janis TaubeProfessor of Pathology

Can a tumor’s genetics affect its microenvironment

and, as follows, its response to immunotherapy?

**

**

**

*

**

*

Tumor Tumor-specific T cell

Tumor-specific T cell

Genetic mutations in cancer encode altered proteins-Some can be “tumor specific antigens” – the biochemical signature that the immune system uses to recognize the tumor as “foreign”

*

*

****

**

Many mutations Many antigens Stronger immune response

Mutational Heterogeneity in Cancer: Altered Proteins Contain Neoepitopes for Immune Recognition

ØDoes mutational load correlate with responsiveness to immune checkpoint blockade?

Lawrence et al., Nature 2013

*Triplenegative

Colorectal Cancers Are Generally Unresponsive to PD-1 Blockade, but the MSI-High Subset Has a High

Mutational Load

Ø Microsatellite instability (MSI): genetic hypermutability resulting from impaired DNA mismatch repair, present in ~15% CRC, 20% gastric ca, 25% endometrial ca, 4% prostate ca, 2% pancreatic ca … -> HIGH MUTATIONAL LOAD

Lawrence et al., Nature 2013

Objective ResponsesMMR-deficient CRC MMR-proficient

CRC MMR-deficient

non-CRC N 13 25 10

Objective Response Rate 62% 0% 60%

Disease Control Rate 92% 16% 70%

-100

-50

0

50

100M M R -p ro fic ie n t C R C

M M R -d e fic ie n t C R C

M M R -d e fic ie n t n o n -C R C

% C

ha

ng

e f

rom

Ba

se

lin

e S

LD

~50 V segments 6D 6-80J

Adaptive Immunity: Genetic Basis for Diversity Generationin Antibodies and T-Cell Receptors

Rearrangement atthe DNA level

Ø These are the only known examples of somatic DNA recombination in mammals.

Constant

N-Region Diversity

Total # TCRbeta = 10e8Total # TCRalpha = 10e9Total TCR + 10e17

% T cells withindividual clonotypes

T cell Clones:

PBLBaseline

RadiographicResponse

Tumor PBL:0 0.5 1 1.5 2 2.5 3 3.5 Months post anti–PD-1 treatment

1 3 5 6 8 10 11 12 14 16 17 18 20 22 23

No pep, C

EF0

5000

10000

15000

20000

Peptide

SFC

/106

T ce

lls

SFCBackgroundPeptide Stimulated

*

**

**

**

1 3 5 6 8 10 11 12 14 16 17 18 20 22 23

No pep, C

EF0

5000

10000

15000 Activity

Peptide

Act

ivity

BackgroundPeptide Stimulated

***

*****

**

*"

Mul$ple'MANA*specific'T'cell'responses'a4er'an$*PD*1'

MANA-Specific FunctionalActivity

MANA – specific responses potential rapid efficacy biomarker

Predicted MANA peptides:

Patients Have Major Pathologic Responses to Anti–PD-1 PRIOR to Surgery

100 clones with significant changes between pre-tx and pre-op timepoints94 of these were significantly INCREASED following anti–PD-1Of the significantly increased clones, 56 were found in the tumor (59.6%)

Pre-Tx

PBM

C

Pre-Op P

BMC

0.0000

0.0005

0.0010

0.0015

0.0020

Fre

quency

Post-

Tx Tum

or0.0

0.2

0.4

456

Fre

quency

PBMC TissueTumor

Neoadjuvant+an,-PD-1+in+NSCLC++++++++++++++++++–+44%+major+pathologic+response+rate+

Pre$nivo)

Post$)nivo)

Neoadjuvant+an,-PD-1+in+NSCLC++++++++++++++++++–+44%+major+pathologic+response+rate+

Pre$nivo)

Post$)nivo)

Pretreatment

Post-treatment

% R

egre

ssio

n

Clonal Frequencies

ER

TAP

MHCclassI

PD-1PD-L1

TCR

TumorCell T-Cell

Wholeexomesequencing

TumorandmatchedPBLDNA

Somaticsequencevariations

Mutationassociatedneoantigencandidates

T-Cellpurification

Mutation AssociatedNeoAntigen Functional

ExpansionofSpecificTcells

Assayofautologous

T-Cellactivation

TCRsequencing

PBL

SynthesizePeptides

Neoantigenprediction:• Antigenprocessing• Epitopeabundance• MHCbinding

STPSASPLSVPLSVIQCLQKHLEVRCPR

EIDLPRELEYELEY

STPSASPLSVPLSVIQCLQKHLEVRCPR

MANAFEST: MANA Functional Expansion of Specific T Cells

1 2 3 4 5 6 7 8 9 10 11 12 16 21 22 23 24 25 26 27 28 29 30 31 32 36 41 45 56 61

LLLDVPTAAV - shared Ag

No Peptide

Uncultured T cellsTumor

0.0

0.2

0.4

0.6

TCR

V-be

ta fre

quen

cy(m

etasta

tic re

spon

der 3

001)

CASTQGGYGYTFCASSLRSGLGYGYTFCASSLGRWRTQPQHFCSARAVGGEHQPQHFCASSFRGEDTGELFFCATRPGGSFYEQYFCASSLVLQGPSPLHFCASSLQGANSPLHFCASSFQGNYGYTF

pMANA1- inducedTCR-beta CDR3s

Predicted MANA ordered by algorithm

MANAFEST picks up MANA-specific responses NOT seen with ELISPOTeven in patient whose tumor has only 30 exomic mutations

1 2 3 4 5 6 7 8 9 10 11 12 16 21 22 23 24 25 26 27 28 29 30 31 32 36 41 45 56 61

LLLDVPTAAV - shared Ag

No Peptide

Uncultured T cellsTumor

0

1

2

3

4

5

TCR V

-beta f

reque

ncy(m

etasta

tic res

pond

er 300

1)

CASSWGTGLNTEAFFCASSPGRGGNQPQHFCASSPRVGSPQHFCASSHRGGRDGEKLFFCASSLAGLLGYTFCASSYDRGNQPQHFCASSGNRVYSPLHFCAIKESTNEKLFFCASSRTGPHEKLFFCASSPLGSGGEAFF

CSARDPGQNYGYTFCASSPGTGAREQYFCASSVGTVGEQYFCASSQSGVPGTEAFFCSVAFSGANVLTFCASGWTGGYGYTFCASSARLEDTQYFCSARRASGATDTQYFCASSLLRPGQGNIQYF

pMANA25-inducedTCR-beta CDR3s

1) Whole exome sequencing

2) Define antigenic peptides(Algorithm, mass spec of eluted peptides, T-cell assays)

3) Synthesize peptides

3) Formulate into vaccine

3) Clinical trials (+ anti–PD-1)

Personalized Peptide Vaccines

Ø >100 Candidate MANA Ø Algorithms highly imperfectØ T cell assays too insensitive

Which peptides?

Scientific challenge

Conclusions• Patients contain a vast repertoire of T cells

capable of recognizing their tumor• Multiple druggable inhibitory pathways in the

TME account for tumor immune resistance →biomarker-driven combinatorial therapy

• Lots of mutations: higher tumor antigenicity• Sensitive assays suggest that anti-tumor

response is broad and shallow even in patients with lower mutational load

• Identification of these responses → personalized immunotherapy

Blloomberg~Kimmel Institute for Cancer Immunotherapy at Johns Hopkins

Thanks to patients and collaborating clinical trial centers.Supported by BMS, Melanoma Research Alliance, NCI, SU2C-AACR-CRI,

Barney Fdn., Bloomberg Foundation, and others