PREDICTING OUTCOME IN OSTEOSARCOMA USING A GENOME-WIDE APPROACH

1. PREDICTING OUTCOME IN OSTEOSARCOMA USING A GENOME-WIDE APPROACH N Gokgoz ,T Yan, M Ghert, S Eskandarian W He, R Parkes, SB Bull, RS Bell, IL Andrulis and JS Wunder Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Canada

2. OSTEOSARCOMA • 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

3. An Emerging Molecular Paradigm • 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”. CAN GENE EXPRESSION PREDICT METASTASES IN OSTEOSARCOMA?

4. Tumor 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

5. What are the underlying molecular differences between Mets at Dx vs. No Mets at Dx ? OSA Patients No metastases at follow-up N=29 No Metastases at Diagnosis N=46 patients High Grade Intramedullary N=64 patients Metastases at follow-up N=17 Metastases at Diagnosis N=18 patients

6. Cy5 Cy3 Cy5/Cy3 Ratio Microarray Analysis of OS Tumorson 19 K chips Ontario Cancer Institute Toronto Canada Tumor Reference Pool 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. Image Acquisition : Axon Scanner Spot Analysis : GenePix Pro.5 Data Storage: IobianTM Gene Traffic

7. Statistical 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

8. Metastases at Dx vs No Metastases at Dx 7352 cDNAs T-statistic p<0.001 (BrB Array Tools) n=1368 genes for tumor classification/clustering

9. 100 Most Significant Genes No Mets at Diagnosis Mets at Diagnosis

10. “Honest” Tumor Class Prediction 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.

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

12. POTENTIAL GENE PATHWAYS IN 1368 GENE LIST • 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)

13. EZRIN • 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 • 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.

14. Ezrin Gene • MA Analysis: Different Platforms OCI Arrays - 2 Spots for Ezrin Gene - Only 1 spot was in our discriminative gene list Spot 1 Spot 2 UTR

15. 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)

16. Future Analyses No Metastases at follow-up N=29 No Metastases at Diagnosis N=46 High Grade Intramedullary N=64 patients Metastases at follow-up N=17 Metastases at Diagnosis N=18 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

17. Acknowledgement Mount Sinai Hospital Hospital for Sick Children D.Malkin IL Andrulis JS Wunder T.Yan, M. Ghert S.Eskandarian Vancouver General Hospital C.Beauchamp S Bull W He R Parkes University of Washington E.Conrad III R Kandel RS Bell Royal Orthopaedic Hospital R.Grimer Memorial Sloan-Kettering J.Healey Mayo Clinic M.Rock/ L.Wold

18. Acknowledgements • 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