1. The Role of Preclinical Models to Identify Novel Therapeutics in Rare Cancers Peter J. Houghton, Ph.D. St. Jude Children’s Research Hospital

2. How To Select New Agents for Clinical Trials? Rationale for the Pediatric Preclinical Testing Program (PPTP) Drug Development NCI/Industry/Academia Phase I Phase II Prioritization of agents for phase I Rational decisions to advance/stop development Potential to focus phase II trials Potential to identify sensitive tumors Establish relevant models that encompass clinical heterogeneity 400 new drugs in development

3. Drug X Testing @ MTD Panel APanel BPanel CPanel DPanel E Active in Model(s)? Full Dose Response/PK Orthotopic Models Transgenic Models Final Report No Yes No Stage 1 Report Other Tumor Models Available? Houghton et al. Clin Cancer Res. (2002) Overview of the PPTP Screen 6 neuroblastomas

4. Why Do Preclinical Cancer Models Fail? Species Tolerance: Host tolerance leads to high systemic exposure to drug -overprediction. Low tolerance leads to low systemic exposure, and underprediction of drug activity.Species Tolerance: Host tolerance leads to high systemic exposure to drug -overprediction. Low tolerance leads to low systemic exposure, and underprediction of drug activity. Tumor models do not recapitulate human cancer at the molecular level the molecular level Criteria for defining ‘activity’ is more stringent in clinical trials Preclinical tumor models do not encompass clinical heterogeneity Preclinical tumor models do not encompass clinical heterogeneity Clinical trials design ignores preclinical data Clinical trials design ignores preclinical data

5. Molecular Characterization of Xenograft Tumor Models Pediatric Preclinical Testing Program (PPTP) -Affymetrix (U133A) -SNP analysis (100K) -CGHPediatric Preclinical Testing Program (PPTP) -Affymetrix (U133A) -SNP analysis (100K) -CGH Pediatric Oncology Preclinical Protein-Tissue Array Project (POPP-TAP) -cDNA arrays -Tissue/Protein arraysPediatric Oncology Preclinical Protein-Tissue Array Project (POPP-TAP) -cDNA arrays -Tissue/Protein arrays How well do xenograft tumors represent the respective clinical disease? Tumor models do not recapitulate human cancer at the molecular levelTumor models do not recapitulate human cancer at the molecular level

6. KCNRAS Tumors Cluster Along Diagnostic Type by Unsupervised Clustering cDNA Array: all 38,789 Good Quality Genes RH1 ? EWS RH6 JHAN Javed Khan -POPP-TAP (subnitted) SC/Orthotopic

7. Identify Xenografts That Recapitulate the Tumors of Origin. MDS 38,789 genes of primary/xeno/cell line: EWS/NB/RMS

8. ANN trained on tumors predict xenografts PPTP Lines -Rh41 -Rh10 -Rh28 -Rh30 -Rh36 -Rh18 PPTP Lines -Rh41 -Rh10 -Rh28 -Rh30 -Rh36 -Rh18 PPTP Lines -SK-N-AS -NB-1643 -NB-1691 -NB-1771 -NB-EB -NB-SD -NB-1382 PPTP Lines -SK-N-AS -NB-1643 -NB-1691 -NB-1771 -NB-EB -NB-SD -NB-1382

9. Panels of Xenograft Tumors Accurately Reflect Clinical Responsiveness Vincristine 44% Cyclophosphamide 50% Actinomycin D 25% Topotecan 50% clinical responses Preclinical tumor models do not encompass clinical heterogeneity Preclinical tumor models do not encompass clinical heterogeneity

10. Comparison of Expression Profiles in Kidney Tumors and Their Derived Xenografts Models Clustering using 543 best classifiers >1Present (Affymetrix U133A) CCSK Fetal kidney HPLNR Wilms Xenograft

11. CCSK HPLNR Wilms Xenograft Wilm’s Tumor Xenografts Cluster with Their Clinical Samples

12. Chemosensitivity of Kidney Tumor Xenografts BMS247550 Link Expression Profiles to Chemosensitivity to Identify Biomarkers of Response Criteria for defining ‘activity’ is more stringent in clinical trials

13. Non-GBM Brain Tumors Kidney Tumors Glioblastoma Osteosarcoma Rhabdomyosarcoma Neuroblastoma Linking Chemosensitivty To Expression Profiling Identifying Biomarkers For Predicting Drug- Responsive Populations

14. ---------------------------------- Total no of records: Columns included: log2(dap086- u133v2_Signal), Z-scores (1), log2(dap110-u133v2_Signal), Z- scores (1), log2(dap113-u133v2_2_Signal), Z-scores (1), log2(dap111-u133v2_Signal), Z-scores (1), log2(dap121- u133v2_Signal), Z-scores (1), log2(dap073-u133v2_Signal), Z- scores (1), log2(dap078-u133v2_Signal), Z-scores (1), log2(dap077-u133v2_Signal), Z-scores (1), log2(dap074- u133v2_Signal), Z-scores (1), log2(dap075-u133v2_Signal), Z- scores (1), log2(dap071-u133v2_Signal), Z-scores (1), log2(dap070-u133v2_Signal), Z-scores (1), log2(dap069- u133v2_Signal), Z-scores (1), log2(dap084-u133v2_Signal), Z- scores (1), log2(dap079-u133v2_Signal), Z-scores (1), log2(dap085-u133v2_Signal), Z-scores (1), log2(dap083- u133v2_Signal), Z-scores (1), log2(dap081-u133v2_Signal), Z- scores (1), log2(dap076-u133v2_Signal), Z-scores (1), log2(dap082-u133v2_Signal), Z-scores (1), log2(dap068- u133v2_Signal), Z-scores (1), log2(dap072-u133v2_Signal), Z- scores (1), log2(dap080-u133v2_Signal), Z-scores (1), log2(dap092-u133v2_Signal), Z-scores (1), log2(dap093- u133v2_Signal), Z-scores (1), log2(dap103-u133v2_Signal), Z- scores (1), log2(dap101-u133v2_Signal), Z-scores (1), log2(dap102-u133v2_Signal), Z-scores (1), log2(dap106- u133v2_Signal), Z-scores (1), log2(dap100-u133v2_Signal), Z- scores (1), log2(dap099-u133v2_Signal), Z-scores (1), log2(dap104-u133v2_Signal), Z-scores (1), log2(dap105- u133v2_Signal), Z-scores (1), log2(dap089-u133v2_Signal), Z- scores (1), log2(dap095-u133v2_Signal), Z-scores (1), log2(dap094-u133v2_Signal), Z-scores (1), log2(dap096- u133v2_Signal), Z-scores (1), log2(dap097-u133v2_Signal), Z- scores (1), log2(dap098-u133v2_Signal), Z-scores (1), log2(dap091-u133v2_Signal), Z-scores (1), log2(dap090- u133v2_Signal), Z-scores (1), log2(dap109-u133v2_Signal), Z- scores (1), log2(dap108-u133v2_Signal), Z-scores (1), log2(dap112-u133v2_Signal), Z-scores (1), log2(dap107- u133v2_Signal), Z-scores (1) Empty values replaced by: 0 Clustering method: UPGMA (unweighted average) Similarity measure: Euclidean distance Ordering function: Average value Hierarchical Clustering Trusted samples based on LogSignal CV 0.5 Max-Min>=100 at least 3 Present (1113 probesets) ALL-19 BT-50 NB-EBc1 D-456 OS-2 BT-28 OS-164OS-166OS-187SKNEP EW-8EW-5WT-12WT-14WT-13WT-11WT-10 Rh36Rh10Rh65Rh41Rh28 EW-1 Rh30 BT-29 WT-16 Rh18 BT-46 CHL-79NB-1691 SK-N-AS NB-1771NB-SDNB-1643NB-132 BT-45 BT-39SJ-GBM2 BT-56D-212BT-41BT-36 OS-17OS-21OS-1

15. Group 1 versus Group 2 Osteosarcomas ( Probesets >4-fold Ttest FDR<0.01) OS#1OS#2

16. Chemosensitivity of Tumors in the PPTP Panel Vincristine

17. The PPTP Richard Gorlick (osteosarcoma)Richard Gorlick (osteosarcoma) John Maris (neuroblastoma)John Maris (neuroblastoma) Henry Friedman (glioblastoma)Henry Friedman (glioblastoma) Richard Lock (ALL)Richard Lock (ALL) Pat Reynolds (in vitro testing)Pat Reynolds (in vitro testing) Malcolm Smith (CTEP)Malcolm Smith (CTEP) Javed Khan (NCI)Javed Khan (NCI) Geoff Neale (St. Jude)Geoff Neale (St. Jude) Chris Morton (St. Jude; coordinator)Chris Morton (St. Jude; coordinator)

18. Thanks

19. Drug X Testing @ MTD Panel APanel BPanel CPanel DPanel E Active in Model(s)? Full Dose Response/PK Orthotopic Models Transgenic Models Final Report No Yes No Stage 1 Report Other Tumor Models Available? Houghton et al. Clin Cancer Res. (2002) Overview of the PPTP Screen

20. Model Systems for Drug Selection Drug Development NCI/Industry/Academia Phase I Phase II Rational decisions to advance/stop development based on PK parametersRational decisions to advance/stop development based on PK parameters Potential to focus phase II trialsPotential to focus phase II trials Potential to identify biomarkers for patient selectionPotential to identify biomarkers for patient selection Relevant models (panels). -molecular identity -encompass clinical heterogeneityRelevant models (panels). -molecular identity -encompass clinical heterogeneity

21. Tumor Panel: 9-10-05

22. Tumor Panel: 9-10-05 (cont.)

23. Volcano: Group OS1 vs Group OS2