lymphoid and myeloid biomarkers for clinical outcome of … · clinical results • psa declines...

Lymphoid and myeloid biomarkers for clinical outcome of ipilimumab and Prostate GVAX treatment

Tanja D. de Gruijl PhD

Dept Medical OncologyVU University medical centerAmsterdam, The Netherlands

2

Tanja de Gruijl-disclosures

Research support from Cell Genesys Inc.

SITC 6 Nov 2011

• Most common malignancy in elderly men• Second leading cause of cancer deaths in western countries• 1 in 6 men affected(www.cancer.org)

Prostate cancer

Clinicallylocalized

Relapsedand

Metastatic

HormoneRefractory

AsymptomaticRising PSA

HormoneRefractory

symptomatic

Local treatment Endocrine Investigational Chemo-

therapy

Progression of disease

Clinicallylocalized

Relapsedand

Metastatic

HormoneRefractory

AsymptomaticRising PSA

HormoneRefractory

symptomatic

Local treatment Endocrine Investigational Chemo-

therapy

Progression of disease

immunotherapy

Drake Nat Rev Immnol 2010

Prostate GVAXVaccine consisting of twoAAV-GM-CSF transduced, irradiated prostate cancercell lines (LNCaP, PC-3)

CELL GENESYSCELL GENESYS

Ipilimumab (Yervoy)*anti-human CTLA-4 Antibody*high affinity and specificity*fully human IgG1k antibody *blocks the binding of CTLA-4 to B7*does not mediate ADCC

Treatment and sampling scheme

* * *

* blood collection forimmunomonitoring

GVAX every 2 weeks for a total of 13 i.d. doses

anti-CTLA4 mAb (Ipilimumab) every 4 weeks for a total of 6 infusions

* ** * *w0v1

w4v3

w8v5

w16v9

w20v11

w24v13

w12v7

fu1

Dose level patient #Cohort 1 1-3 Cohort 2 4-6Cohort 3 7-9Cohort 4 10-12

Cohort 5 13-28

anti-CTLA4 dose0.3 mg/kg1.0 mg/kg3.0 mg/kg5.0 mg/kg

3.0 mg/kg

0

50

100

150

200

250

0

30

60

90

120

150

0

10

20

30

40

50

Clinical results

Partial Response (PR); >50% PSA decline

Stable Disease (SD); No PR or PD

Progressive Disease (PD); >25% PSA increase

Ser

um P

SA (n

g/m

l)

11 / 28

12 / 28

5 / 28

Number of patients

n.a.

85 (82-190)

305 (51-919)

Duration of response (median and range in days)

PSA Progressive Disease (PD)

PSA Stable Disease (SD)

PSA Partial Response (PR)

Category:

>25% on-study PSA increase

No PR or SD

>50% on-study PSA decline

Response

Clinical results

• PSA declines were durable: 6 to 31 months

• Stable disease by bone scan was observed in 11 patients (>5 mns)

• Regressing bone and lymph node metastasis were observed in 2 patients

15-9-2005 29-3-2006

Toxicity: Auto-immune Breakthrough Events (irAE) in 9 patients•7 patients (5/5 PR!) showed hypophysitis with:

- secondary adrenal insufficiencies

- secondary hypothyroidism

• 1 PR patient developed a dose limiting grade 3 alveolitis (5 mg/kg Ipilimumab)

• 2 patients experienced low grade colitis; 1 patient grade 3 hepatitis

• irAE were successfully treated with standard hormone replacement

therapy (endocrinopathies) or steroids.

Clinical results:

Treatment response correlated withsurvival

0 10 20 30 40 50 60 700

20

40

60

80

100

months

Perc

ent s

urvi

val

PR + SDPD

p=0.0034

N=17; med.surv. 41 mths;


0 10 20 30 40 50 60 700

20

40

60

80

100

months

Perc

ent s

urvi

val

PR + SDPD

p=0.0034



0 10 20 30 40 50 60 700

25

50

75

100 actual survivalpredicted survival

monthsPe

rcen

t sur

viva

l

p=0.0099

Med.surv. 31.8 mths; 20 dead, 8 alive

Med.pred.surv. 19 mths

Actual survival was longer thanHalabi-predicted survival

Prostate Cancer as a learning modelCan we identify immune parameters that correlate with clinical activity and may

be useful for clinical response prediction?

Immunomonitoring: principal question

…or treatment resistance prediction? >>avoid autoimmune side effects

NB: Phase I study with non-randomized Phase II study: hypothesis generating.

Further validation of identified immune biomarkers in randomized trials withGVAX and/or ipilimumab required!

Prostate Cancer as a learning modelCan we identify lymphoid and myeloid immune parameters that correlate with

clinical activity and may be useful for clinical response prediction?

Immunomonitoring: principal question

…or treatment resistance prediction? >>avoid autoimmune side effects

1. Serology- tumor-specific antibodies

2. Peripheral blood Teff-/Treg cells- frequency- activation status- effector/memory phenotype

3. T cell Functionality- TAA-specific reactivity- suppression assays- cytokine profiles

4. Peripheral Blood DC (PBDC) and Myeloid Derived Suppressor Cells (MDSC) - frequency- activation status

T cell activation: ICOS, FoxP3, CTLA-4, PD-1

ICOS

w0v1 w8v5 w16v9 w24v13 fu0

25

50

75

100

****

**

*

0 10 20 30 40 50 60 700

25

50

75

100

months

N=11; med.surv. 37 mths


n.s.Pe

rcen

t sur

viva

lCut-off: 2-fold

ICOShi sustained

ICOSlo

0

10

20

30

40

***

w0v1 w8v5 w16v9 w24v13 fu

% C

D4+

/CD

25in

t/Fox

P3+

0 10 20 30 40 50 60 700

25

50

75

100

months

p = 0.030



CD4/CD25int/FoxP3

Cut-off: 50%

Perc

ent s

urvi

val

% C

D4+

/ICO

S+

CD4/ICOS

FoxP3increase

no increase

0

10

20

30

40

**

***

PD-1

% C

D4+

/PD

-1+


% C

D4+

/CTL

A-4+

0

20

40

60

80

****

**


0 10 20 30 40 50 60 700

25

50

75

100

months

ns



0 10 20 30 40 50 60 700

25

50

75

100

months

ns


N=16; med.surv. 31.5 mths

CD4/PD-1

Cut-off: 40%

Cut-off: 40%

CTLA-4

Perc

ent s

urvi

val

Perc

ent s

urvi

val

CD4/CTLA-4

Consistent upregulation of activation markers upon treatment: little association with survival0

25

50

75

100 **

% C

D4+ H

LA-D

R+

w0v1

w8v5

w16v9

w24v13

fuw12v7

w20v11

w4v3

HLA-DRHLA-DR high

HLA-DR low

n.s.0 10 20 30 40 50 60 70

0

25

50

75

100

months

N=22 med surv. 31.5 mths


n.s.Cut-off: 2.0 fold

Per

cent

sur

viva

l

HLA-DR high

HLA-DR low

n.s.0 10 20 30 40 50 60 70

0

25

50

75

100

months




Per

cent

sur

viva

l

n.s.0 10 20 30 40 50 60 70

0

25

50

75

100

months




Per

cent

sur

viva

l

T cell activation: effector/memory phenotype

Increased Th differentiation on treatment: relation with survival

w0 w4 w8 w12 w16 w20 w24 fu0

20

40

60

80

100

** ** ** ** ** ***

% n

on-n

aive

of C

D4+

cells

0 10 20 30 40 50 60 700

25

50

75

100

p = 0.036

months

Perc

ent s

urvi

val



Cut-off: 30%

w0 w4 w8 w12 w16 w20 w24 fu0

20

40

60

80

100

* * *

0 10 20 30 40 50 60 700

25

50

75

100

ns

months

Perc

ent s

urvi

val

≥ 30% increase

< 30% increase

N=7; med. surv. 41 mths


Cut-off: 30%* p<0.05

CD8+ T cells

% n

on-n

aive

of C

D8+

cells

≥ 30% increase

< 30% increase

CD4+ T cells

Healthy donor range

Healthy donor range

High Treg rates: associated with SD/PD and reduced survival

0 10 20 30 40 50 60 700

25

50

75

100 50% increase in Tregs at w24no increase in Tregs at w24

p = 0.023

months

Perc

ent s

urvi

val

N=18; med. surv. 37.0 mths


Regulatory T cells (nTregs)

0

3

6

9

12

15

% C

D4+

/CD

25hi

gh

0

3

6

9

12

15 ***

PR SD/PD

w0v1 w8v5 w16v9 w24v13 fu w0v1 w8v5 w16v9 w24v13 fu

0 10 20 30 40 50 60 700

25

50

75

100

months

Perc

ent s

urvi

val N=18; med.surv. 36.0 mths


High Treg levels before Tx

Low Treg levels before Tx

p = 0.089Cut-off: 6.3%

0 10 20 30 40 50 60 700

25

50

75

100

months

Perc

ent s

urvi

val N=18; med.surv. 36.0 mths


High Treg levels before Tx

Low Treg levels before Tx

p = 0.089Cut-off: 6.3%

Tumor-related elevated pre-treatment frequencies of CD4+CTLA4+ Tcellshave predictive value for survival on treatment

Pre-treatment CTLA4+ Th cells

IgG1 CTLA-4C

D4

CD

4

15.9 %

0

5

10

15

20p=0.0216

% C

D4+ C

TLA

-4+

(of “

conv

entio

nal”

CD

4s)

prostate cancerpatients

healthy donors

IgG1 CTLA-4C

D4

CD

4

15.9 %

IgG1 CTLA-4C

D4

CD

4

15.9 %

0

5

10

15

20p=0.0216

% C

D4+ C

TLA

-4+

(of “

conv

entio

nal”

CD

4s)

prostate cancerpatients

healthy donors

0 10 20 30 40 50 60 700

25

50

75

100

p = 0.011

months

High level of CD4+CTLA-4+

Low level of CD4+CTLA-4+


N=12;med.surv. 20.5 mths

Cut-off: 2.4%

Per

cent

sur

viva

l

T cell activation profile

Potential biomarkers:predictive vs prognostic; no relation according to Halabi-predicted survival

On-treatment predictive Immune parameter Median Survival

between groups P-value Median Halabi

Predicted Survival P-value

Non-naïve CD4+ cells 41.0 vs. 20.0 0.036 16.5 vs. 21.4 0.086 CD8+ICOS+ 21.0 vs. 57.0 0.043 16.7 vs.19.3 0.622 CD4+CD25intFoxP3+ 41.0 vs. 21.0 0.030 19.6 vs. 15.0 0.401 CD4+CD25hiFoxP3+ Tregs 37.0 vs. 21.0 0.045 15.0 vs 19.6 0.201

Pre-treatment predictive Immune parameter Median survival

between groups P-value Median Halabi

Predicted Survival P-value

Non-naïve CD8+ cells n.r. vs. 20.5 0.028 21.6 vs. 17.3 0.222 Non-naïve CD4+ cells 19.0 vs. 41.0 0.02 21.4 vs. 16.7 0.021 CD4+PD-1+ 41.0 vs. 18.0 0.014 20.5 vs. 15.9 0.194 CD4+CTLA-4+ (conv. T cells) 52.0 vs. 20.5 0.011 19.0 vs. 15.9 0.097 CD4+CD25hiFoxP3+ Tregs 20.0 vs. 36.0 0.087 20.9 vs. 19.0 0.230 n.r.= not reached

T cell activation profile

Unsupervised cluster analysis:CD4+CDTLA4+ as dominant predictor of survival on treatment

Min. Max.Stat. cut-off

004

006

011

013

018

014

003

022

002

008

017

007

009

023

015

026

010

021

025

020

027

016

019

005

028

004

006

011

013

018

014

003

022

002

008

017

007

009

023

015

026

010

021

025

020

027

016

019

005

028

Treg increaseNon-naïve CD4+ T cell increaseCD4+CD25intFoxP3+ increaseTreg preNon-naïve CD4+ T cell preNon-naïve CD8+ T cell preCD4+CTLA-4+ preCD4+PD-1+ pre

group 1 group 2group 3

Patient codes

0 10 20 30 40 50 60 700

25

50

75

100 group 3group 2

p=0.036

N=14; med.surv. 46.5 months

N=9; med.surv. 21 months

0 10 20 30 40 50 60 700

25

50

75

100 group 3group 2

p=0.036

N=14; med.surv. 46.5 months

N=9; med.surv. 21 months

months

perc

ent s

urvi

val

HSC CMP IMC

moMDSC

grMDSC

MO DC

Granulocytes

MΦ/M2

STAT3IL-6/VEGF

STAT3IL-6/VEGF

IL-10

Cancer DC

IL-6

IL-6/VEGF

IL-4IL-13

HSC CMP IMC

moMDSC

grMDSC

MO DC

Granulocytes

MΦ/M2

STAT3IL-6/VEGF

STAT3IL-6/VEGF

IL-10

Cancer DC

IL-6

IL-6/VEGF

IL-4IL-13

Myeloid subsets: also targets for GVAX and ipilimumab?

Oosterhoff Immunother 2011

Liu CII 2009

Suzuki Cell Transplant 2010

GM-CSF

PBDC: subset activation

pDCBDCA2/CD123

cDC1BDCA1/CD1c

cDC2BDCA3/CD141

cDC3CD14dim/MDC8

w0 w4 w8 w12 w16 w20 w24 fu0

5

10

15

20

25

***

*

**

w0 w4 w8 w12 w16 w20 w24 fu0

10

20

**

*

w0 w4 w8 w12 w16 w20 w24 fu0

10

20

30

40

50

*

w0 w4 w8 w12 w16 w20 w24 fu0

5

10

15

20*

*

CD

40 M

edFI

PBDC: cDC1 and cDC3 activation

Increased activation of cDC1 and cDC3 (also known as 6-sulfo LacNAc+ or SLAN-DC ) is related to survival

Per

cent

sur

viva

l≥ 70% increase< 70% increase


N=6; med. surv. 15.5 mthsCut-off 70% p = 0.0004

Per

cent

sur

viva

l

≥ 70% increase< 70% increase



Cut-off 70% p = 0.0031

Per

cent

sur

viva

l

cDC1 and cDC3 activationActivation of one or none



p < 0 .0001

cDC1 activation cDC3 activation

MDSC: monocytoid

Monocytic MDSC (Lin-CD14+HLA-DR-)Li

neag

e(C

D3/

16/1

9/56

)

R3

CD14HLA-DR

R2

0 10 20 30 40 50 60 700

25

50

75

100

months

high pretreatment

low pretreatment


N=11; med. surv. 20 mthsCut-off 0.3% p = 0.0062

Per

cent

sur

viva

l

% L

in- C

D14

+ HLA

-DR

-

w0v1 w8v5 w16v9 w24v13 fu10.0

0.5

1.0

1.5

2.0*

% L

in- C

D14

+ HLA

-DR

-

w0v1 w8v5 w16v9 w24v13 fu10.0

0.5

1.0

1.5

2.0*

High pre-treatment levels of mMDSC are associated with poor survival

Filipazzi et al. JCO 2007

MDSC: granulocytic

CD14

Granulocytic MDSC (CD11b+CD14-CD15+)C

D11

b

R2

CD15

R3

% C

D11

b+ CD

14- C

D15

+

0

1

2

3

4

5

w0v1 w8v5 w16v9 w24v13 fu1

0 10 20 30 40 50 60 700

25

50

75

100

months

= 120% increase< 120% increase



Cut-off 120% p = 0.015

Per

cent

sur

viva

l

On-treatment increases in grMDSC are associated with poor survival

Zea et al. Cancer Res 2005

A predictive T cell and myeloid marker profile

Unsupervised cluster analysis:High DC activation and Th CTLA4 and PD-1 expression and low suppressive MDSC and Treg levels together predict survival on GVAX+ipilimumab treatment

Group 1 Group 2 Group 3 Group 4Group 1 Group 2 Group 3 Group 4

Patient codes

CD4+CD25intFoxP3+ increaseTreg preNon- naive CD4+ T cell preNon-naive CD8+ T cell preCD4+CTLA4+ preCD4+PD-1+ precDC1 CD40 activationcDC3 CD40 activation

Non-naive CD4+ T cell increasegrMDSC increaseTreg increasemMDSC pre

Monocyte CD40 activation

Conclusions

Potential immune biomarkers for patient selection prior to treatment:

mMDSC, Tregs, effector/memory and CD4+PD-1+/CD4+CTLA4+ T cell rates

Next: validation• Treatment specific? (GVAX, ipilimumab monotherapies; other therapies?)

• Disease stage specific? (Early versus advanced prostate cancer?)

• Disease specific? (Melanoma vs prostate cancer)

Anita Stam & Saskia SantegoetsFighting the Blues…

…with wine…

…and awards

Immunotherapy lab

Saskia SantegoetsAnita StamSinéad LougheedPetra Scholten Martine ReijmMary von BlombergRik ScheperTanja de Gruijl

Medical Oncology Clinic

Helen GallFons van den EertweghWinald Gerritsen

Karin Jooss

Natalie Sacks

Kristen Hege

Israel Lowy

CELL GENESYSCELL GENESYS

Jean-Marie Cuillerot

lymphoid and myeloid biomarkers for clinical outcome of … · clinical results • psa declines...

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