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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.

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Emt met crc

EMT ☤ METCRC

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

Disclosures

  • No relevant relationships to disclose.


Learning objectives

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

Overview

  • Introduction

    • Epithelial-mesenchymal transition (EMT)

    • Challenges & Research question

    • MET/HGF Axis

  • Study

    • Objective

    • Methodology

    • Results

    • Conclusions

  • Future


Overview1

Overview

  • Introduction

    • Epithelial-mesenchymal transition (EMT)

    • Challenges & Research question

    • MET/HGF Axis

  • Study

    • Objective

    • Methodology

    • Results

    • Conclusions

  • Future


Emt normal cells

EMT & Normal cells

  • Epithelial phenotype ► Mesenchymal phenotype

  • Embryogenesis & Development

Weinberg RA et al. J Clin Invest. Jun 2009


Emt tumors

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 i dentify emt

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 p oor p rognosis

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

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

A

B

C

Genes

Post

Translational

Modification

A

B

C

Proteins

Protein

Processing

Tumor

Weigelt B et al. Ann Oncol. Sep 2012


Research question

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

  • 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


Overview2

Overview

  • Introduction

    • Epithelial-mesenchymal transition (EMT)

    • Challenges & Research question

    • MET/HGF Axis

  • Study

    • Objective

    • Methodology

    • Results

    • Conclusions

  • Future


Study objective

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

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 methodology1

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 methodology2

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

Baseline Characteristics

  • Protein & Gene expression data (N = 139)

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

  • Stage Distribution:

  • Anatomy:


Met overexpression a distinct s ubset

MET overexpression: A Distinct Subset

  • MET protein expression is right skewed

    • Top quartile represents distinct subset

Study Sample

(N = 139)

Right Skewed

Protein (Z-score)

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


High met portends poor survival

High MET portends poor survival


High met portends poor survival1

High MET portends poor survival

Hazard Ratio: 2.92 (P = 0.003)

MET Low

MET-High

MET High

MET-Low


Clinicopathological associations

Clinicopathological Associations

  • MET protein expression:

    • Not associated with any clinical-pathological variables including stage

    • Colon > Rectum

P = 0.008

P < 0.0001


Protein protein associations

Protein-Protein Associations


Met slug protein

MET & SLUG Protein

  • SLUG encoded by SLUG/SNAI2 gene

  • Zinc finger protein transcription factor

  • Represses E-cadherin transcription  EMT

r = 0.63

P < 0.0001

P < 0.0001


Met ercc1 protein

MET & ERCC1 Protein

  • DNA nucleotide excision repair protein

  • Negative predictive marker for platinum therapy

  • SNAIL upregulates ERCC1 expression

  • ERCC1 protein correlates with MET expression (r = 0.6)

  • Higher ERCC1 in MET overexpressed (P < 0.001)

P < 0.001


Protein gene associations

Protein-Gene Associations


Results emt markers

Results : EMT Markers

VIM

P = 0.011

ZEB2

P = 0.005

ZEB1

P = 0.010

AXL

P = 0.005

MET-High

MET-Low


Emt signatures correlate well

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)

P < 0.001

P < 0.001

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 gene scores & MET

  • EMT meta gene score:

    • MET overexpression group vs. MET normal group

Cheng (P = 0.016)

Salazar (P = 0.017)

Taube (P = 0.029)


Conclusions

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

Take Home Message

  • MET protein expression can potentially be used as a clinical biomarker representative of the EMT “mesenchymal” phenotype in CRC.


Overview3

Overview

  • Introduction

    • Epithelial-mesenchymal transition (EMT)

    • Problem at hand & Research question

    • MET/HGF Axis

  • Study

    • Objective

    • Methodology

    • Results

    • Conclusions

  • Future


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

Proposed Paradigm for Pursuitof Biomarkers

Conventional Strategy

Target based biomarkers

Drug

Biomarker

Trial

Taxonomy based biomarkers

Proposed Strategy

A

Tumor Biology

Biomarker

Genomic

Profiling

Trial

B

Drug

C


Acknowledgement

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

Collaborators

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

Dr. David S. Hong, M.D.

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


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