1. Molecular Classification of 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 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 The result: –Spots in the array are dyed in shades of red to green

6. Microarray Background (cont.) Microarray data is translated into an n x p table (p – number of genes, n – number of samples) 0.091.85Gene 4 1.053.34Gene 3 10.53.2Gene 2 2.081.04Gene 1 Sample 2Sample 1

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

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

11. Neighborhood Analysis Are the observed correlations stronger than would be expected by chance? C* is a random permutation of C. Represents a random class distinction C represents the AML/ALL 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. Prediction Algorithm Each gene G i votes, depending on whether its expression level X i in the sample is closer to µ AML or µ ALL The magnitude of the vote is W i V i –W i reflects how well the gene is correlated with the class distinction – reflects the deviation of X i from the average of µ AML and µ ALL

15. Prediction Algorithm (cont.) The votes for each class are summed to obtain total votes V AML and V ALL

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. Genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology 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

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 Maps Well 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 Class A1: 24 ALL, 1 AML Class A2: 10 AML, 3 AML Quite effective at automatically discovering the two types of leukemia Not perfect

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

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 C i,C j –Perform cross validation only on samples in C i and C j

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