Download MET/HGF Axis

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. Kopetz
The 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
EMT Low EMT Score
EMT +
High EMT
Score
Figure 1
Figure 2
Shioiri M et al. Br J Cancer. Jun 2006 ; Loboda A et al. Med Genomics. Jan 2011
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
Epigenetic Modulation
Genes
A
B
C
A
B
C
Post
Translational
Modification
Proteins
Protein
Processing
Tumor
Weigelt B et al. Ann Oncol. Sep 2012
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 biomarker
Thiery 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, Migration
Raghav 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:
40%
• Anatomy:
25%
17%
18%
Rectum
37%
Colon
63%
I
II
III
IV
MET overexpression: A Distinct
Subset
• MET protein expression is right skewed
Protein (Z-score)
4
• Top quartile represents distinct subset
3
2
1
Study Sample
(N = 139)
Right Skewed
0
-1
• Poor correlation with MET gene expression (r = 0.16)
High MET portends poor survival
4
3
2
1
0
-1
High MET portends poor survival
4
MET Low
3
MET-High
2
MET High
1
0
-1
MET-Low
Hazard Ratio: 2.92 (P = 0.003)
Clinicopathological Associations
• MET protein expression:
• Not associated with any clinical-pathological
variables including stage
P = 0.008
100%
MET Protein Group
Rectal
• Colon > Rectum
Colon
P < 0.0001
80%
60%
40%
20%
0%
-0.5
0.0
0.5
MET RPPA
1.0
Colon
MET-Low
Rectum
MET-High
Protein-Protein Associations
MET & SLUG Protein
• SLUG encoded by SLUG/SNAI2 gene
• Zinc finger protein transcription factor
• Represses E-cadherin transcription  EMT
r = 0.63
SLUG RPPA
SLUG RPPA
4
2
P < 0.0001
-2
2
-2
MET RPPA
4
2
P < 0.0001
1
0
MET-Low
MET-High
MET & ERCC1 Protein
• DNA nucleotide excision repair protein
• Negative predictive marker for platinum therapy
• SNAIL upregulates ERCC1 expression
ERCC1 RPPA
1
• ERCC1 protein correlates with
P < 0.001
MET expression (r = 0.6)
0
• Higher ERCC1 in MET
overexpressed (P < 0.001)
-1
MET-Low
MET-High
Protein-Gene Associations
Results : EMT Markers
Gene
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
ZEB2
0.005
Upregulated EMT
markers
VIM
P = 0.011
ZEB2
P = 0.005
ZEB1
P = 0.010
AXL
P = 0.005
-1
0
MET-High
1
2
MET-Low
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)
100
-100
100
Cheng
-100
-200
P < 0.001
200 -200
Taube
200
Cheng
Salazar
200
100
-100
100
-100
-200
Taube
P < 0.001
200
100
200 -100
100
200
Salazar
-100
-200
P < 0.001
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
EMT gene scores & MET
• EMT meta gene score:
• MET overexpression group vs. MET normal group
100
50
50
50
0
0
0
-50
-50
MET-Low
MET-High
Cheng (P = 0.016)
-50
MET-Low
MET-High
Salazar (P = 0.017)
MET-Low
MET-High
Taube (P = 0.029)
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 Pursuit
of Biomarkers
Conventional Strategy
Target based biomarkers
Drug
Biomarker
Trial
Taxonomy based biomarkers
Proposed Strategy
Tumor
Biology
Genomic
Profiling
A
B
Biomarker
Trial
Drug
C
Acknowledgement
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
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.
COLLABORATORS
Dr. Amin Hesham, M.D., M.Sc.
Dr. David S. Hong, M.D.
NCI
TCGA initiative
Collaborators