1. 1 An Array of FDA Efforts in Pharmacogenomics Weida Tong Director, Center for Toxicoinformatics, NCTR/FDA Weida.tong@fda.hhs.gov CAMDA 08, Boku University, Vienna, Austria, Dec 4-6, 2008

2. 2 Pipeline Problem: Spending More, Getting Less While research spending (Pharma and NIH) has increased, fewer NME’s and BLA’s have been submitted to FDA Research spendingNDAs and BLAs received by FDA R&D spending NIH budgetNMEs BLAs

3. 3 The FDA Critical Path to New Medical Products Pharmacogenomics and toxicogenomics have been identified as crucial in advancing –Medical product development –Personalized medicine

4. 4 Guidance for Industry: Pharmacogenomic Data Submissions www.fda.gov/cder/genomics www.fda.gov/cder/genomics/regulatory.htm

5. 5 A Novel Data Submission Path - Voluntary Genomics Data Submission (VGDS) Defined in Guidance for Industry on Pharmacogenomics (PGx) Data Submission (draft document released in 2003; final publication, 2005) –To encourage the sponsor interacting with FDA through submission of PGx data at the voluntary basis –To provide a forum for scientific discussions with the FDA outside of the application review process. –To establish regulatory environment (both the tools and expertise) within the FDA for receiving, analyzing and interpreting PGx data

6. 6 VGDS Status Total of >40 submissions have been received The submissions contain PGx data from –DNA Microarrays –Proteomics –Metabolomics –Genotyping including Genome wide association study (GWAS) –Others Bioinformatics has played an essential role to accomplish: –Objective 1: Data repository –Objective 2: Reproduce the sponsor’s results –Objective 3: Conduct alternative analysis

7. 7 FDA Genomic Tool: ArrayTrack – Support FDA regulatory research and review Developed by NCTR/FDA –Develop 1: An integrated solution for microarray data management, analysis and interpretation –Develop 2: Support meta data analysis across various omics platforms and study data –Develop 3: SNPTrack, a sister product in collaboration with Rosetta FDA agency wide application –Review tool for the FDA VGDS data submission –>100 FDA reviewers and scientists have participated the training –Integrating with Janus for e-Submission

8. 8 Microarray data Proteomics data Metabolomics data Chemical data Clinical and non- clinical data Public data ArrayTrack: An Integrated Solution for omics research

9. 9 Protein Gene Metabolite

10. 10 Specific Functionality Related to VGDS Phenotypic anchoring Systems Approach Clinical pathology data CLinChem name is hidden Gene name is hidden Gene

11. 11 ArrayTrack-Freely Available to Public # of unique users calculated quarterly Web-access Local installation To be consistent with the common practice in the research community Over 10 training courses have been offered, including two in Europe Education: Part of bioinformatics course in UCLA, UMDNJ and UALR Eli Lilly choose ArrayTrack to support it’s clinical gene-expression studies after rigorously assessing the architectural structure, functionality, security assessments and custom support

12. 12 ArrayTrack Website http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/

13. 13 QC issue – How good is good enough? –Assessing the best achievable technical performance of microarray platforms (QC metrics and thresholds) Analysis issue – Can we reach a consensus on analysis methods? –Assessing the advantages and disadvantages of various data analysis methods Cross-platform issue – Do different platforms generate different results? –Assessing cross-platform consistency MicroArray Quality Control (MAQC) - An FDA-Led Community Wide Effort to Address the Challenges and Issues Identified in VGDS

14. 14 MAQC Way of Working Participants: Everyone was welcome; however, cutoff dates had to be imposed. Cost-sharing: Every participant contributed, e.g., arrays, RNA samples, reagents, time and resources in generating and analyzing the MAQC data Decision-making: Face-to-face meetings (1 st, 2 nd, 3 rd, and 4 th ) Biweekly, regular MAQC teleconferences (>20 times) Smaller-scale teleconferences on specific issues (many) Outcome: Peer-reviewed publication: Followed the normal journal-defined publication process 9 papers submitted to Nature Biotechnology 6 accepted and 3 rejected Transparency MAQC Data is freely available at GEO, ArrayExpress, and ArrayTrack RNA samples are available from commercial vendors

15. 15 MicroArray Quality Control (MAQC) project – Phase I MAQC-I: Technical Performance –Reliability of microarray technology –Cross-platform consistency –Reproducibility of microarray results MAQC-II: Practical Application –Molecular signatures (or classifiers) for risk assessment and clinical application –Reliability, cross-platform consistency and reproducibility –Develop guidance and recommendations Feb 2005 Sept 2006 Dec 2008 MAQC-I MAQC-II 137 scientists from 51 ORG >400 scientists from >150 ORG

16. 16 Results from the MAQC-I Study Published in Nature Biotechnology on Sept/Oct 2006 Nat. Biotechnol. 24(9) and 24(10s), 2006 Six research papers: MAQC Main Paper Validation of Microarray Results RNA Sample Titrations One-color vs. Two-color Microarrays External RNA Controls Rat Toxicogenomics Validation Plus: Editorial Nature Biotechnology Foreword Casciano DA and Woodcock J Stanford Commentary Ji H and Davis RW FDA Commentary Frueh FW EPA Commentary Dix DJ et al.

17. 17 Key Findings from the MAQC-I Study When standard operating procedures (SOPs) are followed and the data is analyzed properly, the following is demonstrated: High within-lab and cross-lab reproducibility High cross-platform comparability, including one- vs two- color platforms High correlation between quantitative gene expression (e.g. TaqMan) and microarray platforms –The few discordant measurements were found, mainly, due to probe sequence and thus target location

18. 18 How to determine DEGs - Do we really know what we know A circular path for DEGs –Fold Change – biologist initiated (frugal approach) Magnitude difference Biological significance –P-value – statistician joined in (expensive approach) Specificity and sensitivity Statistical significance –FC (p) – A MAQC findings (statistics got to know its limitation) The FC ranking with a nonstringent P-value cutoff, FC (P), should be considered for class comparison study Reproducibility

19. 19 Nature Science Nature Method Cell Analytical Chemistry

20. 20 Post-MAQC-I Study on Reproducibility of DEGs - A Statistical Simulation Study P vs FC Lab 1 Lab 2 Sensitivity 1-specificity POG FC Sorting POG Reproducibility

21. 21 How to determine DEGs - Do we really know what we don’t know A struggle between reproducibility and specificity/sensitivity –A monotonic relationship between specificity and sensitivity –A “???” relationship between reproducibility and specificity/sensitivity

22. 22 More on Reproducibility General impressions (conclusions): –Reproducibility is a complicated phenomena –No straightforward way to assess the reproducibility of DEGs Reproducibility and statistical power –More samples  higher reproducibility Reproducibility and statistical significance –Inverse relationship but not a simple trade-off Reproducibility and the gene length –A complex relationship with the DEG length Irreproducible not equal to biological irrelevant –If two DEGs from two replicated studies are not reproducible, both could be true discovery

23. 23 MicroArray Quality Control (MAQC) project – Phase II MAQC-I: Technical Performance –Reliability of microarray technology –Cross-platform consistency –Reproducibility of microarray results MAQC-II: Practical Application –Molecular signatures (or classifiers) for risk assessment and clinical application –Reliability, cross-platform consistency and reproducibility –Develop guidance and recommendations Feb 2005 Sept 2006 Dec 2008 MAQC-I MAQC-II 137 scientists from 51 ORG >400 scientists from >150 ORG

24. 24 Application of Predictive Signature Diagnosis Short term exposure Long term effect Clinical application (Pharmacogenomics) Safety Assessment (Toxicogenomics) Long term effect Treatment Treatment outcome Prognosis Phenotypic anchoring Prediction

25. 25 Data Set Validation Classifier Preprocessing QC Feature Selection Batch effect Which QC methods How to generate an initial gene pool for modeling P, FC, p(FC), FC(p) … How to assess the success - Chemical based prediction - Animal based prediction Normalization e.g.: Raw data, MAS5, RMA, dChip, Plier Which methods: KNN, NC, SVM, DT, PLS … Challenge 1

26. 26 Challenge 2: Assessing the Performance of a Classifier Prediction Accuracy: Sensitivity, Specificity Mechanistic Relevance: Biological understanding Robustness: Reproducibility of signatures 1 2 3

27. 27 Dataset Set Validation Classifier Preprocessing QC Feature Selection Normalization Freedom of choice (35 analysis teams) A consensus approach (12 teams) Validation, validation and Validation!

28. 28 What We Are Looking For Which factors (or parameters) critical to the performance of a classifier A standard procedure to determine these factors The procedure should be the dataset independent A best practice - Could be used as a guidance to develop microarray based classifiers Dataset Set Validation Classifier Preprocessing QC Feature Selection Normalization

29. 29 Three-Step Approach Step1 Training set 1.Classifiers 2.Sig. genes 3.DAPs Frozen Step 2 Blind test set Prediction Assessment Best Practice Step 3 Future sets Validate the Best Practice New exp for selected endpoints

30. 30 MAQC-II Data Sets ProvidersDatasets Size Step 1 - Training Step 2 - Test MDACCBreast cancer130100 UAMSMultiple myeloma350209 Univ. of Cologne Neuroblastoma251300 HamnerThe lung tumor 70 (18 cmpds) 40 (5 cmpds) Iconix Non-genotoxic hepatocarcinogenicity 216201 NIEHSLiver injury (Necrosis)214204 Clinical data Toxicogenomics data

31. 31 Where We Are Step1 Training set 1.Classifiers 2.Sig. genes 3.DAPs Frozen Step 2 Blind test set Prediction Assessment Best Practice Step 3 Future sets Validate the Best Practice New exp for selected endpoints

32. 32 18 Proposed Manuscripts Main manuscript - Study design and main findings Assessing Modeling Factors (4 proposals) Prediction Confidence (5 proposals) Robustness (3 proposals) Mechanistic Relevance (2 proposals) Consensus Document (3 proposals) Dataset Set Validation Classifier Preprocessing QC Feature Selection Normalization Prediction Accuracy Mechanistic Relevance Robustness

33. 33 Consensus Document (3 proposals) 1.Principles of classifier development: Standard Operating Procedures (SOPs) 2.Good Clinical Practice (GCP) in using microarray gene expression data 3.MAQC, VXDS and FDA guidance on genomics Modeling Assessing Consensus Guidance

34. 34 Best Practice Document One of the VGDS and MAQC objectives is to communicate with the private industry/research community to reach consensus on –How to exchange genomic data (data submission) –How to analyze genomic data –How to interpret genomic data Lessons Learned from VGDS and MAQC have led to development of Best Practice Document (Led by Federico Goodsaid) –Companion to Guidance for Industry on Pharmacogenomic Data Submission (Docket No. 2007D-0310). (http://www.fda.gov/cder/genomics/conceptpaper_2006 1107.pdf)http://www.fda.gov/cder/genomics/conceptpaper_2006 1107.pdf –Over 10 pharmas have provided comments

35. 35 An Array of FDA Endeavors - Integrated Nature of VGDS, ArrayTrack, MAQC and Best Practice Document ArrayTrack MAQC VGDS

36. 36 Member Of Center for Toxicoinformatics