Bioinformatics Tools for Biomarkers Discovery

1. Bioinformatics Tools for Biomarkers Discovery Director, Software/Database Development sgranite@jhu.edu IBM/CCBM Post-Doc Research Fellow actan@jhu.edu Stephen GRANITE & Aik Choon TAN Prof. Raimond L. Winslow rwinslow@jhu.edu, Director, CCBM, Prof. Donald Geman geman@jhu.edu, Prof. Daniel Naiman daniel.naiman@jhu.edu, Lei Xu leixu@jhu.edu, Troy Anderson troy_anderson@jhu.edu The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling (CCBM), Johns Hopkins University

2. Disease Normal Disease Normal Biomarkers Discovery Workflow Clinical Applications Candidate Biomarkers Sample Collection Follow-up Study Decision Rules Patients Transcriptomics Pipeline MAGE-DB2 Machine Learning Store Gene Expression Profiling Relative Expression Reversal Classifiers Experiments Query PROTEIN-DB2 Store Mass Spectrometry Query Proteomics Pipeline Available at CCBM Store Difference Gel Electrophoresis

3. Outline • Multi-scale biomedical data repositories • System Architecture • Relative Expression Reversal Classifiers • TSP & k-TSP classifiers • Microarray Gene expression data • Results on binary & multi-class disease classification problems • Data Integration and Cross-platform analysis • Difference Gel Electrophoresis (DIGE) Proteomics data • Results on disease classification • Conclusions

4. Multi-scale Biomedical Repositories • The MAGE-DB2 Project is developing a full relational mapping of the MicroArray Gene Expression (MAGE) object model (OM) optimized to run on IBM’s scalable, parallel database DB2. • The PROTEIN-DB2 Project is developing an open-source relational implementation of theProtein Standards Initiative (PSI) object model for storing complete descriptions of a range of proteomic experimental data and analyses. (Granite et al)

5. PROTEIN-DB2 Primary Data / Analysis Storage • Two-dimensional Gel Electrophoresis Images/Analyses • 2D-PAGE / Nonlinear Dynamics Progenesis Analysis • DIGE / GE Amersham DeCyder Analysis • Two-dimensional Liquid Chromatography • Beckman-Coulter PF2D primary data • Protein Array • Beckman-Coulter A2 primary data • Mass Spectrometry (MS) primary data / mzXML translation • Applied Biosystems Voyager • ABI/SCIEX QStar • Shimadzu Axima • ThermoFinnigan LCQ and LTQ • MS Search Results • Matrix Science’s Mascot HTML and XML output (Granite et al)

6. MAGE-DB2/PROTEIN-DB2 Architecture (Granite et al)

7. MAGE-DB2/PROTEIN-DB2 Webpages http://lpar4.wbmei.jhu.edu/wps/portal http://proteomics.jhu.edu/dl/pathidb.php (Granite et al)

8. Pairwise rank-based comparisons (relative expression values within each array) Generates accurate and simple decision rules TSPclassifier: Top Scoring Pair k-TSP classifier: k-disjoint Top Scoring Pairs Data driven, parameter-free learning algorithm Performance comparable to or exceeds that of other machine learning methods Easy to interpret, facilitating follow-up study (small number of genes) Relative Expression Reversal Classifiers (Tan et al., 2005, Bioinformatics, 21:3896-3904)

9. Rank-based Classification • Novelty: Replace the measured expression values by their ranks within profiles, hence obtaining invariance to normalization. • Example: Differentiate between classes by finding pairs of genes whose ordering typically changes from Normal to Disease. • Simple Interpretation:Inversion of mRNA abundance. (From D. Geman)

10. Normal, if Ri,new > Rj,new, ynew = hTSP(xnew) = (1) Disease, otherwise. TSP Classifier • For each pair of genes (i, j), i≠ j, 1 ≤ i, j ≤ G, compute: • Pij(Normal) = (Ri > Rj / Normal) • Pij(Disease) = (Ri > Rj / Disease) • Δij = | Pij(Normal) – Pij(Disease) | • Select only the top scoring pairs : • { (i*, j*): Δi*j* = Δmax } • TSP classifier (hTSP) is based on these pairs: • Example: Let all the top scoring pairs “vote” (Geman et al, 2004) • Example: Select one unique top scoring pair, based on maximizing difference in ranks (i, j) (Tan et al, 2005) • Prediction: Suppose Pij(Normal) > Pij(Disease), xnew = new profile: • If, on the other hand, if Pij(Disease) > Pij(Normal),then the decision rule is reversed. (Tan et al., 2005, Bioinformatics, 21:3896-3904)

11. k-TSP Classifier • Uses exactly k top disjoint pairs in prediction. • k is determined by internal cross-validation • Ensemble learning – to combine the discriminating power of many “weaker” rules to make more reliable predictions. • Prediction: • Suppose xnew = new profile, each gene pair (iu, ju), u = 1,…, k, votes according (1). • The k-TSP classifier hk-TSP employs an unweighted majority voting procedure to obtain the final prediction of ynew. (Tan et al., 2005, Bioinformatics, 21:3896-3904)

12. Microarray Data Sets (Binary class Problems) (Multi-class Problems) (Tan et al., 2005, Bioinformatics, 21:3896-3904)

13. Results(LOOCV Binary Class Problems) Number of Informative Genes (Tan et al., 2005, Bioinformatics, 21:3896-3904)

14. Results(Test Accuracy for Multi-Class Problems) Number of Informative Genes (Tan et al., 2005, Bioinformatics, 21:3896-3904)

15. Normalized Expression High Low (a) TSP ALL AML IF SPTAN1  CD33* THEN ALL; ELSE AML  = 0.9787 (b) k-TSP IF SPTAN1  CD33* THEN ALL; ELSE AML  = 0.9787 IF HA-1  ZYX* THEN ALL; ELSE AML  = 0.9787 IF TCF3* > APLP2 THEN ALL; ELSE AML  = 0.9574 IF ATP2A3*  CST3* THEN ALL; ELSE AML  = 0.9387 IF DGKD > MGST1 THEN ALL; ELSE AML  = 0.9387 IF CCND3*  NPC2 THEN ALL; ELSE AML  = 0.9387 IF TOP2B* > PLCB2 THEN ALL; ELSE AML  = 0.9387 IF Macmarcks  CTSD* THEN ALL; ELSE AML  = 0.9362 IF PSMB8  DF* THEN ALL; ELSE AML  = 0.9200 * Genes previously identified by Golub et al (1999) (Tan et al., 2005, Bioinformatics, 21:3896-3904)

16. “Direct” Data Integration Lab A Lab X Lab B Lab Y Lab C (Lei Xu et al, 2005, Bioinformatics, 21:3905-3911)

17. Data Sets (Lei Xu et al, 2005, Bioinformatics, 21:3905-3911)

18. TSPs from Data Integration (Lei Xu et al, 2005, Bioinformatics, 21:3905-3911)

19. Results on Test Set Comparisons of Marker TSP with Individual TSPs (Lei Xu et al, 2005, Bioinformatics, 21:3905-3911)

20. Marker TSP for Prostate Cancer • HPN (Hepsin) [biomarker candidate for prostate cancer] • STAT6 (Signal transduction and translation protein) IF HPN > STAT6 THEN Prostate Cancer ELSENormal PSA (Prostate Specific Antigen): Sn = 67.5% – 80% , Sp = 60% - 70% TSP (HPN, STAT6): Sn = 91.7%, Sp = 97.7% (From this study!) (Lei Xu et al, 2005, Bioinformatics, 21:3905-3911)

21. DIGE Technology (From http://www5.amershambiosciences.com) Proteomics Data Experimental Settings: Gels: 18 experiments Cy2 – Internal Standards (18) Cy3 – Cancer gels (18) Cy5 – Normal gels (18) 1098 protein spots (BVA ratios from DeCyder software) (Troy Anderson et al)

22. Decision Rule Decision Rule: IF Ratio530 Ratio786THEN Cancer, ELSE Normal. LOOCV Results: Accuracy: 97.2% (35/36) Sensitivity: 100% (18/18) Specificity: 94.4% (17/18) (Troy Anderson et al)

23. Low High Protein Marker Spots (Troy Anderson et al)

24. http://www.ccbm.jhu.edu

25. Conclusions • Bioinformatics tools to facilitate biomarkers discovery • k-TSP is comparable with the state-of-the-art classifiers (PAM, SVM) in classifying gene expression profiles • k-TSP generates simple and accurate decision rules • Biological significance • Easy to interpret • Potential clinical applications • Allow “direct” data integration without performing normalization • Allow cross-platform analysis • Applicable to a wide-range of high-throughput data