EMT MET☤CRC

MET overexpression as a hallmark of the epithelial-mesenchymal transition

(EMT) phenotype in colorectal cancer

K. Raghav, W. Wang, G.C. Manyam, B.M. Broom, C. Eng,

M.J. Overman, S. KopetzThe University of Texas M D Anderson Cancer Center, Houston TX

Disclosures

• No relevant relationships to disclose.

Learning Objectives

• Recognize epithelial-mesenchymal transition

(EMT) as a principal molecular subtype in

colorectal cancers.

• Identify MET protein overexpression as a key

clinical biomarker of EMT physiology in

colorectal cancers.

Overview• Introduction

• Epithelial-mesenchymal transition (EMT)• Challenges & Research question• MET/HGF Axis

• Study• Objective• Methodology• Results• Conclusions

• Future

Overview• Introduction

• Epithelial-mesenchymal transition (EMT)• Challenges & Research question• MET/HGF Axis

• Study• Objective• Methodology• Results• Conclusions

• Future

EMT & Normal cells

• Epithelial phenotype ► Mesenchymal

phenotype

• Embryogenesis & Development Weinberg RA et al. J Clin Invest. Jun 2009

EMT & Tumors

• EMT ‘mesenchymal’ phenotype:

• Migratory capacity: Invasion & Metastasis

• Linked to chemo-resistance (oxaliplatin and 5FU)

Thiery JP. Nature Reviews Cancer. Jun 2002 ; Yang AD et al. Clin Cancer Res. Jul 2006

Gene Signatures identify EMT

• Gene signatures:

• EMT ‘mesenchymal’ subtype

• Distinct biology

Cheng WY et al. PLoS One. Apr 2012 ; Loboda A et al. Med Genomics. Jan 2011

EMT foretells Poor prognosis

• EMT molecular classification is prognostic• EMT or mesenchymal-subtype: Worse Prognosis

• Epithelial-Subtype: Better Prognosis

Figure 1 Figure 2Shioiri M et al. Br J Cancer. Jun 2006 ; Loboda A et al. Med Genomics. Jan 2011

High EMTScore

Low EMT Score

EMT +

EMT -

Challenges in Defining EMT Phenotype in Clinic

• EMT Gene Signature:

• Extensive ongoing efforts

• Hard to implement in clinic

• Limited availability

• Protein Biomarker:

• More practical

• Readily available

A B C

Epigenetic Modulation

Tumor

A B C

Genes

Proteins

PostTranslationalModification

Weigelt B et al. Ann Oncol. Sep 2012

ProteinProcessing

Research Question

• Possibility of using a clinical biomarker, to reflect

EMT biology to recognize EMT “mesenchymal”

subtype as identified by EMT gene signatures ?

• Possible marker: MET

• MET is motogenic: + Cell mobility & invasiveness

• First EMT cell lines transformed using MET activation.

• Common signaling pathways with EMT

• Optimized assays & integrated as a biomarkerThiery JP. Nature Reviews Cancer. Jun 2002

MET/HGF Axis

• MET/HGF Axis:

• Receptor: MET

• Ligand: HGF/SF

• Regulates

• Gene expression

• Cytoskeleton

• Aberrancy:

• Tumor Proliferation, Survival, Invasion, MigrationRaghav K & Eng C. Colorectal Cancer Aug 2012

Overview• Introduction

• Epithelial-mesenchymal transition (EMT)• Challenges & Research question• MET/HGF Axis

• Study• Objective• Methodology• Results• Conclusions

• Future

Study Objective

• To identify association between MET protein

expression and gene/protein expression of

EMT markers and EMT gene signatures in

human colorectal cancers.

Study Methodology

• Data collection:

• The Cancer Genome Atlas (TCGA) Data

• The cBio Cancer Genomics Portal

• Data type (Untreated primary):

• Gene expression: mRNA Expression

• RNA Sequencing

• Protein levels (MET, SLUG, ERCC1):

• Reverse phase protein array RPPA

Study Methodology

• Tumors classified as per MET protein levels:

• MET High/Overexpressed: Protein in top quartile

• MET Low: Protein level < 3rd Quartile

• 58 genes associated with EMT phenotypes evaluated:

• Unsupervised: ≥ 2 EMT signatures (N = 41)

• Loboda, Taube, Salazar & Cheng EMT profiles

• Nominated: Common EMT markers (N = 17)Salazar R et al. J Clin Oncol. Jan 2011 ; Cheng WY et al. PLoS One. Apr 2012 ; Taube JH et al. Proc Natl Acad Sci U S A. Aug 2010

Study Methodology

• Statistical methods:

• Non-parametric Spearman rank correlation

• Mann-Whitney unpaired two-sample U test

• Regression tree method

• Kaplan-Meier estimates

• P < 0.05: Statistically significant

• All tests were two-sided

Baseline Characteristics

• Protein & Gene expression data (N = 139)

• Median age at diagnosis: 71 yrs. (35-90 yrs.)

• Stage Distribution:

• Anatomy:

I II III IV

17%

40%

25%

18%

Colon63%

Rectum37%

MET overexpression: A Distinct Subset

-1

0

1

2

3

4

Study Sample(N = 139)

Right Skewed

• MET protein expression is right skewed

• Top quartile represents distinct subset

• Poor correlation with MET gene expression (r = 0.16)

Pro

tein

(Z

-sco

re)

-1

0

1

2

3

4

High MET portends poor survival

-1

0

1

2

3

4

High MET portends poor survival

Hazard Ratio: 2.92 (P = 0.003)

MET High

MET Low MET-High

MET-Low

MET RPPA-0.5 0.0 0.5 1.0

Co

lon

Re

cta

l

Clinicopathological Associations

• MET protein expression:

• Not associated with any clinical-pathological

variables including stage

• Colon > Rectum

P < 0.0001

P = 0.008

ME

T P

rote

in G

rou

p

Colon Rectum0%

20%

40%

60%

80%

100%

MET-Low MET-High

Protein-Protein Associations

MET & SLUG Protein

• SLUG encoded by SLUG/SNAI2 gene

• Zinc finger protein transcription factor

• Represses E-cadherin transcription EMT

MET RPPA

S

LU

G R

PP

A

-2 2 4

-2

2

4

r = 0.63

P < 0.0001

P < 0.0001

SL

UG

RP

PA

MET-Low MET-High

0

1

2

MET & ERCC1 Protein

• DNA nucleotide excision repair protein

• Negative predictive marker for platinum therapy

• SNAIL upregulates ERCC1 expression

P < 0.001 • ERCC1 protein correlates with

MET expression (r = 0.6)

• Higher ERCC1 in MET

overexpressed (P < 0.001)

ER

CC

1 R

PP

A

MET-Low MET-High

-1

0

1

Protein-Gene Associations

Results : EMT MarkersGene P Gene P

AEBP 0.034 GREM1 0.033

AXL 0.005 LUM 0.035

CDH11 0.006 MGP 0.003

CDH2 0.029 MMP11 0.038

COPZ2 0.008 PRXX1 0.002

CTGF 0.035 SERPINF1 0.004

DCN 0.006 SPOCK1 0.003

ECM2 0.016 TAGLN 0.033

FAP 0.020 TCF4 0.046

FBLN5 0.017 TGFB1I1 0.012

FGF1 0.008 THBS2 0.022

FGF7 0.045 VIM 0.011

FSTL1 0.032 ZEB1 0.010

Upregulated EMT markers

ZEB2 0.005-1 0 1 2

AXLP = 0.005

ZEB1P = 0.010

ZEB2P = 0.005

VIMP = 0.011

MET-LowMET-High

EMT signatures correlate well

• EMT gene signature scores:

• Cheng vs. Salazar (r = 0.8)

• Salazar vs. Taube (r = 0.6)

• Taube vs. Cheng (r = 0.7)

Salazar

T

aube

-100 100 200

-200

-100

100

200

P < 0.001

Taube

C

heng

-200 -100 100 200

-200

-100

100

200

P < 0.001Salazar R et al. J Clin Oncol. Jan 2011 ; Cheng WY et al. PLoS One. Apr 2012 ; Taube JH et al. Proc Natl Acad Sci U S A. Aug 2010

P < 0.001

Cheng

Sal

azar

-100 100 200

-200

-100

100

200

EMT gene scores & MET

• EMT meta gene score:

• MET overexpression group vs. MET normal group

Cheng (P = 0.016) Taube (P = 0.029)Salazar (P = 0.017)

MET-Low MET-High-50

0

50

MET-Low MET-High

-50

0

50

100

MET-Low MET-High-50

0

50

Conclusions

• MET protein expression

• Highest quartile represents a distinct subset

• Not correlate with MET mRNA expression

• Higher in colon than in rectal cancers

• Higher expression of SLUG transcription factor

• Higher ERCC1 protein levels

• Increased gene expression of EMT markers

• Higher EMT gene signature scores

Take Home Message

• MET protein expression can potentially be

used as a clinical biomarker representative of

the EMT “mesenchymal” phenotype in CRC.

Overview• Introduction

• Epithelial-mesenchymal transition (EMT)• Problem at hand & Research question• MET/HGF Axis

• Study• Objective• Methodology• Results• Conclusions

• Future

Future

• Validation of these results on an independent dataset is currently being performed.

• Evaluation of IHC in assessing MET protein expression is underway.

• MET can be used as a clinical bio-marker for patient selection for trials targeting EMT.

• Unique approach for biomarker search

Proposed Paradigm for Pursuitof Biomarkers

Ge

no

mic

Pro

filin

g

Biomarker

Proposed Strategy

TrialDrug Biomarker

Conventional Strategy

Tumor Biology

TrialB

C

A

Target based biomarkers

Taxonomy based biomarkers

Drug

Acknowledgement

KOPETZ LAB TEAM

Dr. Ali Kazmi, M.D.

Dr. Arvind Dasari, M.D.

Maria Pia Morelli, M.D., Ph.D.

Shweta Aggarwal, M.D.

Feng Tian, Ph.D.

Zhi-Qin Jiang, M.D., Ph.D.

NCI

TCGA initiative

Collaborators

CO-INVESTIGATORS

Wenting Wang, Ph.D.

Ganiraju C Manyam, Ph.D.

Bradley M Broom, Ph.D.

Cathy Eng, M.D., FACP

Michael J. Overman, M.D.

Scott Kopetz, M.D., Ph.D., FACP

COLLABORATORS

Dr. Amin Hesham, M.D., M.Sc.

Dr. David S. Hong, M.D.