Molecular Classification of Cancer

1. Molecular Classificationof Cancer Class Discovery and Class Prediction by Gene Expression Monitoring

2. Overview • Motivation • Microarray Background • Our Test Case • Class Prediction • Class Discovery

3. Motivation • Importance of cancer classification • Cancer classification has historically relied on specific biological insights • We will discuss a systematic and unbiased approach for recognizing tumor subtypes

4. Microarray Background • Microarrays enable simultaneous measurement of the expression levels of thousands of genes in a sample • Microarray: • Glass slide with a matrix of thousands of spots printed on to it • Each spot contains probes which bind to a specific gene

5. Microarray Background (cont.) • The result: • Spots in the array are dyed in shades of red to green • The process: • DNA samples are taken from the test subjects • Samples are dyed with fluorescent colors and placed on the Microarray • Hybridization of DNA and cDNA

6. Sample 1 Sample 2 Gene 1 1.04 2.08 Gene 2 3.2 10.5 Gene 3 3.34 1.05 Gene 4 1.85 0.09 Microarray Background (cont.) • Microarray data is translated into an n x p table(p – number of genes, n – number of samples)

7. Demonstration http://www.bio.davidson.edu/courses/genomics/chip/chip.html

8. Our Test Case • 38 bone marrow samples from acute leukemia patients (27 ALL, 11 AML) • RNA from the samples was hybridized to microarrays containing probes for 6817 human genes • For each gene, an expression level was obtained

9. Class Prediction • Initial collection of samples belonging to known classes • Goal: create a “class predictor” to classify new samples • Look for “informative genes” • Make a prediction based on these genes • Test the validity of the predictor

10. Genes whose expression pattern is strongly correlated with the class distinction strongly correlated poorly correlated Informative genes

11. Neighborhood Analysis • Are the observed correlations stronger than would be expected by chance? C represents the AML/ALL class distinction C* is a random permutation of C.Represents a random class distinction

12. Application to the Test Case • Roughly 1100 genes were more highly correlated with the AML-ALL class distinction than would be expected by chance

13. Make a Prediction • Use a fixed subset of “informative genes” (most correlated with the class distinction) • Make a prediction on the basis of the expression level of these genes in a new sample

14. The magnitude of the vote is Wi Vi • Wi reflects how well the gene is correlated with the class distinction • reflects the deviation of Xi from the average of µAML and µALL Prediction Algorithm • Each gene Gi votes, depending on whether its expression level Xi in the sample is closer to µAML or µALL

15. Prediction Algorithm (cont.) • The votes for each class are summed to obtain total votes VAML and VALL

16. Prediction Algorithm (cont.) • The prediction strength is calculated: • The sample is assigned to the winning class provided that the PS exceeds a predetermined threshold(0.3 in the test case)

17. Testing the Validity of Class Predictors • Cross Validation • withhold a sample • build a predictor based on the remaining samples • predict the class of the withheld sample • repeat for each sample • Assess accuracy on an independent set of samples

18. Application to the Test Case • 50 genes most highly correlated with the AML-ALL distinction were chosen • A class predictor based on these genes was built

19. Application to the Test Case • Performance in cross validation: • Out of 38 samples there were 36 predictions and 2 uncertainties (PS < 0.3) • 100% accuracy • PS median 0.77

20. Application to the Test Case (cont.) • Performance on an independent set of samples: • Out of 34 samples there were 29 predictions and 5 uncertainties (PS < 0.3) • 100% accuracy • PS median 0.73

21. Comments • Why 50 genes? • Large enough to be robust against noise • Small enough to be readily applied in a clinical setting • Predictors based on between 10 to 200 genes all performed well • Genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology

22. Comments (cont.) • Creation of a new predictor involves expression analysis of thousands of genes • Application of the predictor then requires only monitoring the expression level of few informative genes

23. Class Discovery • Cluster tumors by gene expression • Apply a clustering technique to produce presumed classes • Evaluation of the Classes: • Are the classes meaningful? • Do they reflect true structure?

24. Clustering Technique - SOMs • SOMs – Self Organizing MapsWell suited for identifying a small number of prominent classes • Find an optimal set of “centroids” • Partition the data set according to the centroids • Each centroid defines a cluster consisting of the data points nearest to it • We won't go into details about the calculation of SOMs

25. Application of a two-cluster SOM to the test case • Quite effective at automatically discovering the two types of leukemia • Not perfect Class A1: 24 ALL, 1 AML Class A2: 10 AML, 3 AML

26. Evaluation of the Classes • How can we evaluate such classes if the “right” answer is not already known? • Hypothesis: class discovery can be tested by class prediction • If the classes reflect true structure, then a class predictor based on them should perform well • Let’s test this hypothesis...

27. Validity of Predictors Based on A1 and A2 • Predictors based on different numbers of informative genes performed well • For example: a 20-gene predictor

28. Validity of Predictors Based on A1 and A2 cont. • Performance on independent samples: • PS median 0.61 • Prediction made for 74% of samples

29. Validity of Predictors Based on A1 and A2 cont. • Performance in cross validation: • 34 accurate predictions with high prediction strength • One error • Three uncertains

30. the one cross validation error 2 of the 3 cross validation uncertains

31. Iterative Procedure • Use a SOM to initially cluster the data • Construct a predictor • Remove samples that are not correctly predicted in cross-validation • Use the remaining samples to generate an improved predictor • Test on an independent data set

32. Validity of Predictors Based on Random Clusters • Performance: • Poor accuracy in cross validation • Low PS on independent samples

33. Conclusion • The AML-ALL distinction could have been automatically discovered and confirmed without previous biological knowledge

34. Application of a 4-cluster SOM to the Test Case

35. Evaluation of the Classes • Complement approach: • Construct class predictors to distinguish each class from its complement • Pair-wise approach: • Construct class predictors to distinguish between each pair of classes Ci,Cj • Perform cross validation only on samples in Ci and Cj

36. Evaluation of the Classes • Class predictors distinguished the classes from one another, with the exception of B3 versus B4

37. Conclusion • The results suggest the merging of classes B3 and B4 • The distinction corresponding to AML, B-ALL and T-ALL was confirmed

38. Uses of Class Discovery • Identify fundamental subtypes of any cancer • Search for fundamental mechanisms that cut across distinct types of cancers

39. Questions? • Thank you for listening