PREDICTING OUTCOME IN PREDICTING OUTCOME IN OSTEOSARCOMA USING A OSTEOSARCOMA USING A GENOME-WIDE APPROACHGENOME-WIDE APPROACH

N Gokgoz ,T Yan, M Ghert, S Eskandarian N Gokgoz ,T Yan, M Ghert, S Eskandarian W He, R Parkes, SB Bull, RS Bell,W He, R Parkes, SB Bull, RS Bell,

IL Andrulis and JS WunderIL Andrulis and JS Wunder

Samuel Lunenfeld Research Institute, Mount Sinai Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, CanadaHospital, Toronto, Canada

• Treatment involves (neo)adjuvant chemotherapy and wide surgical resection

•Patients with Metastases at Diagnosis:

5 year disease-free survival 10-20%.

•Patients without Metastases at Diagnosis:

5 year disease-free survival 50-78%.

•Few accurate clinical predictors of outcome

•Molecular markers ( e.g. p53, RB, cdk4,SAS): not prognostic

OSTEOSARCOMAOSTEOSARCOMA

CAN GENE EXPRESSION PREDICT CAN GENE EXPRESSION PREDICT

METASTASES IN OSTEOSARCOMA?METASTASES IN OSTEOSARCOMA?

•Expression patterns of multiple genes may be more predictive than one or two alone

Hypothesis: The study of global gene expression patterns in osteosarcomas may improve classification of these tumors and prediction of disease outcome.

•Microarray Analysis to characterize “gene expression signatures”.

An Emerging Molecular ParadigmAn Emerging Molecular Paradigm

Tumor SamplesTumor Samples

• Osteosarcoma Tumor Bank• 64 fresh frozen, high grade intramedullary

osteosarcoma• all tumor specimens were from open

biopsies performed prior to chemotherapy• tumor specimen chosen based on frozen

section histological analysis• minimum follow-up 24 months or metastasis

High Grade

Intramedullary

N=64 patients

No Metastases

at Diagnosis

N=46 patients

Metastases

at Diagnosis

N=18 patients

No metastases at follow-up N=29

Metastases at follow-up N=17

What are the underlying molecular differences What are the underlying molecular differences between Mets at Dx vs. No Mets at Dx ?between Mets at Dx vs. No Mets at Dx ?

OSA Patients

Microarray Analysis of OS TumorsMicroarray Analysis of OS Tumors on 19 K chipson 19 K chips

Each hybridization compared Cy5 labeled cDNA from one of the tumor samples with Cy3 labeled cDNA from the reference sample (a pool of 11 tumor cell lines). The arrows indicate the genes that have

high (red) Cy5/Cy3 and low (green) Cy5/Cy3 ratios.

Cy5 Cy3 Cy5/Cy3 Ratio

Ontario Cancer Institute

Toronto Canada

Image Acquisition : Axon ScannerSpot Analysis : GenePix Pro.5Data Storage: IobianTM Gene Traffic

Reference PoolTumor

Statistical AnalysisStatistical Analysis

• replication and reproducibility studies for validity• local background subtraction• log transformation• normalization – subarray effects• single gene differential expression

(T-test using BrB ArrayTools)• adjust for multiple testing• multiple gene tumor classification• “honest” tumor class prediction using cross-

validation

Metastases at Dx Metastases at Dx vs vs

No Metastases at DxNo Metastases at Dx

7352 cDNAs

T-statistic p<0.001

(BrB Array Tools)

n=1368 genesfor tumor classification/clustering

No Mets at Diagnosis Mets at Diagnosis

100 Most Significant Genes100 Most Significant Genes

““Honest” Tumor Class Prediction Honest” Tumor Class Prediction using Cross-Validation (CV)using Cross-Validation (CV)

• Leave-One Out (LOO) cross-validation method

• Several prediction methods were applied on expression data set to examine their accuracy for the metastatic status of the patients.

““Honest” Tumor Class Prediction Honest” Tumor Class Prediction using Cross-Validation (CV)using Cross-Validation (CV)

•Metastasis Suppressor1 (MTSS1)•Cell Adhesion Integrins and Selectin-P•Cell cycle checkpoint genes PARC (a regulator of p53 localization and degradation) Cyclin dependent kinases CDK4-6•Chromosome instability MCC (Mutated in Colorectal Carcinoma)•Genes related to chemotherapy sensitivity/resistance MSRP (multidrug resistance-related protein) DNA metyhyltransferase 1 associated protein, •Cytoskeleton Organization

Ezrin (Villin2)

POTENTIAL GENE PATHWAYS IN 1368 GENE LIST POTENTIAL GENE PATHWAYS IN 1368 GENE LIST

C. Khanna et al., Cancer Research, 2001.P. Leonard et al., BJC, 2003. C. Khanna et al., Nature Medicine ,2004. Y. Yu et al., Nature Medicine, 2004.

•Ezrin has been shown to be involved in promotion of metastasis in a number of cancer systems including osteosarcoma.

Linker between membrane molecules and actin cytoskeleton

EZRINEZRIN

•MA Analysis: Different Platforms OCI Arrays - 2 Spots for Ezrin Gene - Only 1 spot was in our

discriminative gene list

Ezrin GeneEzrin Gene

UTR

Spot 1 Spot 2

Conclusions:Conclusions:

• There is a very large disparity in outcome for patients with osteosarcoma who have Metastases at Diagnosis vs No Metastases at Diagnosis

• Gene expression profiles generated by microarray analysis discriminated these 2 groups with a 94 % prediction accuracy

• Genes that are differentially expressed between the 2 groups require further follow–up (Ezrin)

High Grade

Intramedullary

N=64 patients

No Metastases at Diagnosis

N=46 Metastases at follow-up N=17Metastases at

Diagnosis N=18

No Metastases at follow-up N=29

Future AnalysesFuture Analyses

1. Mets at Dx vs No Mets at Dx.• Determine classifiers • Identify pathways related to genes in the classifier

2. Patients developed mets during follow-up and not.• Determine classifiers • Chemotherapy response• Identify pathways related to genes in the classifier

3. Characterization of biological pathways • e.g. Ezrin

AcknowledgementAcknowledgementMount Sinai Hospital

IL Andrulis

JS Wunder

T.Yan, M. GhertS.Eskandarian

Hospital for Sick Children D.Malkin

Vancouver General Hospital C.Beauchamp

S Bull

W He

R Parkes

R Kandel

RS Bell

University of Washington E.Conrad III

Royal Orthopaedic Hospital R.Grimer

Memorial Sloan-Kettering J.Healey

Mayo Clinic M.Rock/ L.Wold

AcknowledgementsAcknowledgements

• Ontario Cancer Research Network (OCRN)• National Cancer Institute of Canada

(NCIC)• Canadian Institute of Health Research (CIHR)

Interdisciplinary Health Research Team (IHRT) in Musculoskeletal Neoplasia

• Rubinoff-Gross Chair in Orthopaedic Oncology at Mount Sinai Hospital, University of Toronto