Hodgkin Lymphoma: From Discovery to Clinical Translation – Robust Gene Expression from FFPET

Hodgkin Lymphoma: From Discovery to Clinical Translation

Randy D. GascoyneBC Cancer AgencyVancouver, Canada

Ventana Meeting, Tucson 2013

Robust gene expression from FFPET

Outline

• Brief history of Hodgkin lymphoma & CHL biology

• A 3-stage roadmap to understanding HL biology

• Benign macrophages contribute to outcome prediction

• Clinical translation, NanoString and a multi-gene predictor

• Future work & take home lessons

3

Epidemiology of Hodgkin lymphoma in Canada & USA

78,130 Estimated lymphoma cases in 2011

55%Males55%Males

45%Females45%

Females78,130

NHL78,130

NHL 10,06010,060

Close to 10,060 in Canada & USA diagnosed with Hodgkin Lymphoma annually

• Approximately 1,450 will die from the disease

• Over 206,000 patients have a history of HL

20 yrs >55 yrs34 yrs

32%32% 28%28%

Age

Bimodal Distribution

HL Patients

Higher survival rate but an increasedHigher survival rate but an increasedincidence of long-term health complicationsincidence of long-term health complications

No standard treatment exists forNo standard treatment exists forolder patients (>60 years old)older patients (>60 years old)

PFS and DSS for classical Hodgkin lymphoma over consecutive eras in BC

Pro

po

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ail

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Time (years) Time (years)

Pro

po

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ise

as

e-s

pe

cif

ic s

urv

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l1960s

1960s

1970s

1970s1980s

1980s

1990s

1990s

2000s

2000s

Progression-free Survival Disease-Specific Survival

Joseph Connors, 2012 (unpublished)

Clinical aspects of HL• Common cancer in younger people

• In about 20-25%, primary therapy with ABVD will fail to cure the patients

• High-dose therapy (autoBMT) salvages ~ 50%

• Late toxicities are a problem and thus it would be ideal if we could identify those patients who are being over-treated, in addition to those patients destined to fail their primary treatment

• Clinical translation will require that we identify robust biomarkers that can predict both treatment success and treatment failure

Classical Hodgkin lymphoma

CD30

CD3 CD20

Study design, methods and analysis tools

One-cycle cRNA labeling reaction

Molecular Machines &Industries (MMI)CellCut Laser microdissection

Affymetrix HG U133 2.0 Plus array

Mic

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Whole genomeamplification

Two-cycle cRNAlabeling reaction


Submegabase ResolutionTiling Array (SMRT ARRAY)

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CD30

Frozen lymph node

Steidl et al., NEJM 2010, 362: 875-885

Steidl et al., NEJM 2010, 362: 875-885

GenderStageType failureTreatment outcome

Sig

nifican

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-regu

latedg

enes in

treatmen

t failures

Sig

nifican

tly do

wn

-regu

latedg

enes in

treatmen

t failures

Cluster A Cluster B

Hierarchical clustering of 130 pretreatment gene expression profiles

Progression Free Survival (years)

20151050

Cum

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Sur

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1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

Median PFS:0%-5%: not reached5-25%: 6.15 years>25%: 2.71 yearsLog rank: p=0.034

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Disease Specific Survival (years)

2520151050

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1.0

.9

.8

.7

.6

.5

.4

.3

.2

.1

0.0

10-year DSS:0%-5%: 88.6%5-25%: 67.4%>25%: 59.6%Log rank: p=0.0027

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Steidl et al., NEJM 2010

CD68 Immunohistochemistry

CD68

CD68

Studies on the prognostic value of tumor-associated macrophages in classical Hodgkin lymphoma

Markers used Method # Outcome correlation Reference

PNA Histochemistry 43 Adverse (refractory disease, early relapse)

Ree et al, Cancer 1985

STAT1, ALDH1A1 GE, IHC 235 Adverse (DSS) Sanchez-Aguilera et al, Blood 2006

LYZ, STAT1, ALDH1A1 GE, IHC 194 Adverse (refractory disease, early relapse)

Sanchez-Espiridion et al, Clincial Cancer Research 2009

CD68 IHC 166 Adverse (PFS, DSS) Steidl et al, NEJM 2010

LYZ, STAT1 GE 262 Favorable (FFS) Sanchez-Espiridion et al, Blood 2010

CD68, CD163 IHC 288 Adverse (EFS, OS) Kamper et al, Haematologica 2010

CD68 IHC 59 Adverse (refractory disease) Benedicte et al, Blood 2010 [abstr.]

CD68 (in combination with FOXP3)

IHC 122 Adverse (FFTF, OS) Greaves et al, Blood 2010 [abstr.]

CD68 IHC 144 Adverse (EFS, DSS) Yoon et al, Blood 2010 [abstr.]

CD68 IHC 105 Adverse (OS) Tzankov et al [personal communication]

CD68 IHC 45 Adverse (PFS) Hohaus & Larocca[personal communication]

CD68 IHC 153 Adverse (OS, PFS) Farinha et al USCAP 2011 [abstr.]

CD68, CD163 IHC (double staining) 82 Adverse (OS) Zaki et al, Virch Arch 2011

CD68 IHC 52 Adverse (OS) Jakovic et al, Leuk & Lym 2011

CD68, CD163 IHC 265 No survival impact Azambuja et al, Ann Oncol 2012

CD68, CD163 IHC 144 Adverse (OS) Yoon et al, Europ J Haematol (in-press)

CD68 (PG-M1) IHC 151 Adverse (PFS) & correlation with interim PET

Touati et al, ASH 2011 [abst 1558]

CD68, CD163, STAT1, LYZ IHC 266/103 Adverse (DSS) for CD68 Sanchez-Espiridion et al, Haematol 12

Modified from Steidl et al, Haematologica 2011

Cu

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Time (years)

FFS Training

CD68high (n=54)

CD68low (n=89)C

um

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su

rviv

al

Time (years)

FFS Training

CD68high (n=54)

CD68low (n=89)

Cu

mu

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p<0.01

CD68high (n=55)

CD68low (n=89)

Time (years)

OSValidation

Cu

mu

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p<0.01

CD68high (n=55)

CD68low (n=89)

Time (years)

OSValidation

Cu

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p=0.04

Time (years)

FFS Validation

CD68high (n=55)

CD68low (n=89)

Cu

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p=0.04

Time (years)

FFS Validation

CD68high (n=55)

CD68low (n=89)C

um

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su

rviv

al

CD68high (n=54)

CD68low (n=89)

Time (years)

OSTraining

Cu

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ival

CD68high (n=54)

CD68low (n=89)

Time (years)

OSTraining

A

Macrophages predict survival in a randomizedphase III clinical trial (E2496)

KL Tan et al, Blood 2012, 120: 3280-7

Cu

mu

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p<0.01

CD163high (n=66)

CD163low (n=78)

Time (years)

OSValidation

Cu

mu

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p<0.01

CD163high (n=66)

CD163low (n=78)

Time (years)

OSValidation

Cu

mu

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p<0.01

Time (years)

FFSValidation

CD163high (n=66)

CD163low (n=78)

Cu

mu

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urv

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p<0.01

Time (years)

FFSValidation

CD163high (n=66)

CD163low (n=78)C

um

ula

tive

su

rviv

al CD163high (n=53)

CD163low (n=90)

Time (years)

OS Training

Cu

mu

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ival CD163high (n=53)

CD163low (n=90)

Time (years)

OS Training

Cu

mu

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Time (years)

FFSTraining

CD163high (n=53)

CD163low (n=90)C

um

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tive

su

rviv

al

Time (years)

FFSTraining

CD163high (n=53)

CD163low (n=90)

B

OS and FFS for E2496 CHL cases based on IHC for CD163 (n = 277)

KL Tan et al, Blood 2012, 120: 3280-7

Microenvironment in Hodgkin lymphoma

Steidl et al, JCO 2011, 29: 1812-26




Mic

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2.Two-cycle cRNAlabeling reaction




Mic

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1.CD30

Frozen lymph node

Steidl et al., Blood, 116: 418-27 2010


Recurrent imbalances in 53 classical Hodgkin lymphoma samples: Treatment outcome correlations

Treatmentfailure

Treatmentsuccess

Chromosome 16

ABCC1

Progression Free Survival with 16p gain

Progression Free Survival (years)

242220181614121086420

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10-year PFS:16p gain present: 12.8%16p gain absent: 63.0%Log rank: p=0.002

Steidl et al., Blood, 116: 418-27 2010

Diagnosis Start Rx End Rx

CureBeginningof disease

Years0 1 2 3 4

No CR / Progressionduring treatment

= primary refractory

Early relapse(≤6 months afterend of treatment)

Late relapse(>6 months afterend of treatment)

Frequency of 16p gains and time point of progression/relapse

LateSequelae

(MDS, AML,carcinoma)

Follow-up

83.3% 33.3% 25.0%

Relative frequency of 16p gains (% of cases)

Link to primary drug resistance ?

Two-cycle cRNAlabeling reaction



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1.CD30

Frozen lymph node


Steidl et al, Blood 2012, 120: 3530-40


Gene expression profiling from microdissected HRS cells

Cis & trans correlations from microdissected HRS cellscomparing gene expression with copy-number alterations


18321832

2012

Can we translate biomarker discovery in Hodgkin lymphoma into the clinic?

2011

There is a clear clinical need

• The only tool to inform on an expectation of survival in CHL is the International Prognostic Score (IPS)

• This is only used to design and interpret clinical trials and is NOT used to make treatment decisions for individual patients

• No biological tool is available to reproducibly assign risk or be used as a predictive test on which up-front treatment decisions could be made

Hodgkin lymphoma: The current state of play

• 10-20% of patients with advanced stage CHL succumb to disease

• “One size fits all” approach to treatment

• Lack of reliable tests at diagnosis that can guide management

Scott and Gascoyne, 2013

At the heart of the debate

• There are differing opinions about the up-front chemotherapy regimen for advanced-stage classical HL

• To some extent this represents a European vs North American bias (ABVD vs escalated BEACOPP)

• ABVD fails to cure a sizable minority of patients while escBEACOPP visits unnecessary toxicity on a significant percentage of patients

Study outline• Develop a gene-expression based predictor of OS

in advanced stage CHL treated with standard intensity treatment

• Train on data from a phase III randomized control clinical trial (E2496)

• Validate in an independent cohort treated with ABVD, enriched for primary treatment failure and including a weighted analysis

LI Gordon et al, JCO 2013, 31: 684-91DW Scott et al, JCO 2013, 31: 692-700

The E2496 intergroup trial• Phase III randomized controlled trial comparing

ABVD and Stanford V (n ~ 854 patients)

• Locally extensive and advanced stage cHL

• Identical outcomes between the 2 arms– FFS and OS

LI Gordon et al, JCO 2013, 31: 684-91

NanoString® Technologies | Confidential27

nCounter Assay

Hybridize CodeSet to RNA

Remove Excess Reporters

Bind Reporter to Surface

Immobilize and Align Reporter

Image Surface

Count Codes

mRNA Capture and Reporter Probes

GK Geiss et al, Nat Biotech 2008, 26: 317-25


nCounter Assay





Image Surface

Count Codes

Hybridized mRNA Excess Probes



nCounter Assay





Image Surface

Count Codes

Surface of cartridge is coated with streptavidin

Hybridized Probes Bind to Cartridge

29


nCounter Assay





Image Surface

Count Codes

Immobilize and Align Report for Image Collecting and Barcode Counting

30

− +


nCounter Assay





Image Surface

Count Codes

One Coded Reporter = 1 Nucleic Acid



nCounter Assay





Image Surface

Count Codes

Codes are Counted and Tabulated

-Direct digital readout of mRNA-No enzymes

-No amplification-Ideally suited to 100 bp mRNA found in FFPET

-Same technology touted in breast cancer (PAM50)

Scott et al, JCO 2013, 31: 692-700

259 genes

SMLR80 genes

Macrophage41 genes

HRS22 genes

Cytotoxic T cell/NK20 genes

Tregs11 genes

B cells9 genes

Eosinophils/Mast cells7 genes

Adipocytes6 genes

“Spanish”14 genes

“French”10 genes

MHC9 genes

Apoptosis10 genes

Angiogenesis7 genes

ExtracellularMatrix 8 genes

Other 7 genes

Housekeeping6 genes

Scott et al, JCO 2013, 31: 692-700

Results

• 95% of cases yielded quality results

• 23 gene model

• Signature representative of:– Increased macrophages

– Th1 immune response

– Increased cytotoxic T/NK cells

in the pretreatment biopsies of patients that died

Scott et al, JCO 2013, 31: 692-700

Genes associated with overall survival

ThresholdP

redi

ctor

Sco

re

•Dichotomize the training cohort into “low-” and “high-risk” groups

•Maximize the Х2 of the Mantel-Cox test

Scott et al, JCO 2013, 31: 692-700

Training cohort

Scott et al, JCO 2013

Prop

ortio

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eral

l sur

viva

l

Time (years)

Training cohort94%

75%]19%

Training cohort – the IPSPr

opor

tion

over

all s

urvi

val

Time (years)

Median follow-up 5.3 years

p = 0.76

IPS score3 to 70 to 2

Validation cohort

Scott et al, JCO 2013

Status at last follow up

Alive

Dead

TRAINING COHORT WEIGHTED VALIDATION COHORT

Overall survival by predictor score

Scott et al, JCO 2013, 31: 692-700

Survival curves

Cohorts C statistic 5 year OS (%) Hazard Ratio 95% CILog-rank P

value

Training 0.73Low risk 94

7.1 3.3 – 15.1High risk 75

WeightedValidation

0.70Low risk 92

6.7 2.6 – 17.4 <0.001High risk 63

Summary of predictor performance

Scott et al, JCO 2013, 31: 692-700

Where does it stand?• An internally validated prognostic test for ABVD

treated patients

• Requires external validation

• Requires testing in patients treated with other regimens

• The real value is in a predictive test

Scott and Gascoyne, JCO on-line 2012

Prop

ortio

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eral

l sur

viva

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Time (years)

Clinical utility

Adequately treated- Should we consider de-escalation to reduce morbidity and long term sequelae?

Prop

ortio

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eral

l sur

viva

l

Time (years)

Clinical utility

Inadequately treated- Can this be overcome by more aggressive upfront treatment?- Do we require novel agents?

Target subjects for biological studies

Other predictors in use

• PET scan after 2 cycles– Defines a high-risk group– Studies ongoing to determine predictive value– “Penalty” for not being at diagnosis

• Opportunity to correlate the NanoString predictor with PET results– Intergroup trial (SWOG 0816) (Phase II Trial)– BC Cancer Agency data

Scott and Gascoyne, 2013

Take home messages

• A fundamental understanding of the biology & genomics of CHL clearly informs on potential diagnostic & prognostic strategies

• Doing science in the context of a clinically relevant question provides value-added information

• Clinical translation using robust technologies that impact treatment decision making is the path forward, even for relatively low incidence cancers

AcknowledgementsCentre for Lymphoid Cancer

Christian SteidlJoseph ConnorsNathalie Johnson

Laurie SehnKerry Savage

Pedro FarinhaGraham Slack

King TanSanja RogicMerrill Boyle

Adele TeleniusSusana Ben-NeriahBarbara MeissnerBruce Woolcock

Robert KridelDavid ScottSuman Singh

Holly EelyHeidi Cheung

Jacqueline WongBarbara Yuen The BCCRC

Fong Chun ChanSohrab Shah

ECOGBrad Kahl

Sandra HorningLeo Gordon

Fangxin HongECOG cliniciansECOG patientsMike Ferriere

Megan CreamerIntergroup participants

External CollaboratorsLisa Rimsza

Arjan DiepstraAnke van den Burg

The end: Questions?

Hodgkin Lymphoma: From Discovery to Clinical Translation – Robust Gene Expression from FFPET

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Transcript of Hodgkin Lymphoma: From Discovery to Clinical Translation – Robust Gene Expression from FFPET