1. Copyright  2003 limsoon wong Data Mining of Gene Expression Profiles for the Diagnosis and Understanding of Diseases Limsoon Wong Institute for Infocomm Research

2. Copyright  2003 limsoon wong Plan Some accomplishments and challenges in knowledge discovery from biological and clinical data Data mining in microarray analysis –diagnosis of disease state and subtype –derivation of treatment plan –understanding of gene interaction network

3. Copyright  2003 limsoon wong Knowledge Discovery from Biological and Clinical Data: MOTIVATION

4. Copyright  2003 limsoon wong Complete genomes are now available Knowing the genes is not enough to understand how biology functions Proteins, not genes, are responsible for many cellular activities Proteins function by interacting with other proteins and biomolecules GENOME PROTEOME INTERACTOME Driving Forces: Genes, Proteins, Interactions, Diagnosis, & Cures

5. Copyright  2003 limsoon wong If we figure out how these work, we get these Benefits To the patient: Better drug, better treatment To the pharma: Save time, save cost, make more $ To the scientist: Better science

6. Copyright  2003 limsoon wong To figure these out, we bet on... “solution” = Data Mgmt + Knowledge Discovery Data Mgmt = Integration + Transformation + Cleansing Knowledge Discovery = Statistics + Algorithms + Databases

7. Copyright  2003 limsoon wong Knowledge Discovery from Biological and Clinical Data: ACCOMPLISHMENT

8. Copyright  2003 limsoon wong Integration Technology (Kleisli) Cleansing & Warehousing (FIMM) MHC-Peptide Binding (PREDICT) Protein Interactions Extraction (PIES) Gene Expression & Medical Record Datamining (PCL) Gene Feature Recognition (Dragon) Venom Informatics 1994 19981996 2000 2002 ISS KRDL LIT/I 2 R GeneticXchange Molecular Connections Biobase 8 years of bioinformatics R&D in Singapore

9. Copyright  2003 limsoon wong Predict Epitopes, Find Vaccine Targets Vaccines are often the only solution for viral diseases Finding & developing effective vaccine targets is slow and expensive process Develop systems to recognize protein peptides that bind MHC molecules Develop systems to recognize hot spots in viral antigens

10. Copyright  2003 limsoon wong Recognize Functional Sites, Help Scientists Effective recognition of initiation, control, and termination of biological processes is crucial to speeding up and focusing scientific experiments Data mining of bio seqs to find rules for recognizing & understanding functional sites Dragon’s 10x reduction of TSS recognition false positives

11. Copyright  2003 limsoon wong Diagnose Leukaemia, Benefit Children Childhood leukaemia is a heterogeneous disease Treatment is based on subtype 3 different tests and 4 different experts are needed for accurate diagnosis  Curable in USA,  fatal in Indonesia A single platform diagnosis based on gene expression Data mining to discover rules that are easy for doctors to understand

12. Copyright  2003 limsoon wong Understand Proteins, Fight Diseases Understanding function and role of protein needs organised info on interaction pathways Such info are often reported in scientific paper but are seldom found in structured databases Knowledge extraction system to process free text extract protein names extract interactions

13. Copyright  2003 limsoon wong Data Mining in Microarray Analysis: MICROARRAY BACKGROUND

14. Copyright  2003 limsoon wong What’s a Microarray? Contain large number of DNA molecules spotted on glass slides, nylon membranes, or silicon wafers Measure expression of thousands of genes simultaneously

15. Copyright  2003 limsoon wong Affymetrix GeneChip Array

16. Copyright  2003 limsoon wong Making Affymetrix GeneChip quartz is washed to ensure uniform hydroxylation across its surface and to attach linker molecules exposed linkers become deprotected and are available for nucleotide coupling

17. Copyright  2003 limsoon wong Gene Expression Measurement by GeneChip

18. Copyright  2003 limsoon wong A Sample Affymetrix GeneChip File (U95A)

19. Copyright  2003 limsoon wong Data Mining in Microarray Analysis: DISEASE SUBSTYPE DIAGNOSIS

20. Copyright  2003 limsoon wong Pediatric Acute Lymphoblastic Leukemia A heterogeneous disease with more than 12 subtypes, e.g., T-ALL, E2A-PBX1, TEL- AML1, BCR-ABL, MLL, and Hyperdip>50. Treatment response is subtype dependent 80% continuous remission if subtype is correctly diagnosed and the corresponding treatment plan is applied

21. Copyright  2003 limsoon wong Subtype Diagnosis Require different tests: –immunophenotyping –cytogenetics –molecular diagnostics Require different experts: –hematologist –oncologist –pathologist –cytogeneticist

22. Copyright  2003 limsoon wong Difficulties and Implications The different tests and experts are not commonly available within a single hospital, especially in less advanced countries  An 80%-curable disease in USA can be a fatal disease in Indonesia!  Is there a single diagnostic platform that does not need multiple human specialists?

23. Copyright  2003 limsoon wong A Potential Solution by Microarrays Yeoh et al., Cancer Cell 1:133--143, 2002 Diagnostic ALL BM samples (n=327) 33 -3  -2  -1  0 11 22  = std deviation from mean Genes for class distinction (n=271) TEL-AML1BCR- ABL Hyperdiploid >50E2A- PBX1 MLLT-ALLNovel TEL-AML1 E2A-PBX1 T-ALL Hyperdiploid >50 BCR-ABL MLL Novel

24. Copyright  2003 limsoon wong Some Caveats Study was performed on Americans May not be applicable to Singaporeans, Malaysians, Indonesians, etc. Large-scale study on local populations currently in the works

25. Copyright  2003 limsoon wong Typical Procedure in Analysing Gene Expression for Diagnosis Gene expression data collection Gene selection Classifier training Classifier tuning (optional for some machine learning methods) Apply classifier for diagnosis of future cases

26. Copyright  2003 limsoon wong Feature Selection Methods A refresher of feature selection methods

27. Copyright  2003 limsoon wong Signal Selection (Basic Idea) Choose a signal w/ low intra-class distance Choose a signal w/ high inter-class distance

28. Copyright  2003 limsoon wong Signal Selection (eg., t-statistics)

29. Copyright  2003 limsoon wong Signal Selection (eg.,  2)

30. Copyright  2003 limsoon wong Signal Selection (eg., CFS) Instead of scoring individual signals, how about scoring a group of signals as a whole? CFS –Correlation-based Feature Selection –A good group contains signals that are highly correlated with the class, and yet uncorrelated with each other

31. Copyright  2003 limsoon wong Gene Expression Profile Classification An introduction to gene expression profile classification by the example on ALL subtype diagnosis

32. Copyright  2003 limsoon wong Subtype Classification of ALL A tree-structured diagnostic workflow was recommended by the doctors, as per Yeoh et al., Cancer Cell 1:133--143, 2002

33. Copyright  2003 limsoon wong Training and Testing Sets

34. Copyright  2003 limsoon wong Our procedure for ALL subtype diagnosis Gene expression data collection Gene selection by entropy Classifier training by emerging pattern Classifier tuning (optional for some machine learning methods) Apply classifier for diagnosis of future cases by PCL

35. Copyright  2003 limsoon wong Signal Selection (eg., entropy)

36. Copyright  2003 limsoon wong Emerging Patterns (EPs) An EP is a set of conditions –usually involving several features –that most members of a class satisfy –but none or few of the other class satisfy A jumping EP is an EP that –some members of a class satisfy –but no members of the other class satisfy We use only most general jumping EPs

37. Copyright  2003 limsoon wong PCL: Prediction by Collective Likelihood

38. Copyright  2003 limsoon wong Accuracy (using 20 genes of lowest entropy) PCL 1:1 0:2 5 1:1 0:0 4 0:1 1:1 1:6 14 0:1 1:1 5 0:1 2:2 7

39. Copyright  2003 limsoon wong Comprehensibility

40. Copyright  2003 limsoon wong Gene Expression Profile Classification How about other feature selection and classification methods?

41. Copyright  2003 limsoon wong Some gene selection heuristics all-CFS: all features from CFS top20-  2: 20 features w/ highest  2 stats top20-t: 20 features w/ highest t-stats top20-mit: 20 features w/ highest MIT stats entropy: 20 features w/ lowest entropy all-  2: all features meeting 5% significance level of  2 stats

42. Copyright  2003 limsoon wong Some other classification methods k-NN (k=1) –majority votes of the k nearest neighbours determined by Euclidean distance C4.5 –widely used decision tree method. Naïve Bayes (NB) –probabilistic prediction using Bayes’ rule SVM –(linear) discriminant function that maximizes separation of boundary samples

43. Copyright  2003 limsoon wong Accuracy Feature selection improves performance Entropy+PCL has consistent high performance

44. Copyright  2003 limsoon wong When 20 genes are selected randomly Average over 100 experiments Cf. 7-15 mistakes total with good feature selection

45. Copyright  2003 limsoon wong Data Mining in Microarray Analysis: TREATMENT PLAN DERIVATION A pure speculation!

46. Copyright  2003 limsoon wong Can we do more with EPs? Detect gene groups that are significantly related to a disease Derive coordinated gene expression patterns from these groups Derive “treatment plan” based on these patterns

47. Copyright  2003 limsoon wong Colon Tumour Dataset Alon et al., PNAS 96:6745--6750, 1999 We use the colon tumour dataset above to illustrate our ideas –22 normal samples –40 colon tumour samples

48. Copyright  2003 limsoon wong Detect Gene Groups Feature Selection –Use entropy method –35 genes have cut points Generate EPs –19501 EPs in normals –2165 EPs in tumours EPs with largest support are gene groups significantly co-related to disease

49. Copyright  2003 limsoon wong Top 20 EPs

50. Copyright  2003 limsoon wong Observation 1 Some EPs contain large number of genes and still have high freq E.g., {2, 3, 6, 7, 13, 17, 33} has freq 90.91% in normal and 0% in cancer samples  Nearly all normal sample’s gene expr. values satisfy all conds. implied by these 7 items

51. Copyright  2003 limsoon wong Observation 2 Freq of singleton EP is not necessarily larger than EP having multiple genes E.g., {5} is EP in cancer samples and has freq 32.5% E.g., {16, 58, 62} is EP in cancer samples and has freq 75.5%  Groups of genes and their correlation's could be more impt than single genes

52. Copyright  2003 limsoon wong Observation 3 M33680 has lowest entropy of the 35 genes if cutpoint is set at 352 18/40 of cancer samples shift expr level of M33680 from its normal range to its abnormal range

53. Copyright  2003 limsoon wong Treatment Plan Idea Increase/decrease expression level of particular genes in a cancer cell so that –it has the common EPs of normal cells –it has no common EPs of cancer cells

54. Copyright  2003 limsoon wong Treatment Plan Example From the EP {2,3,6,7,13,17,33} –91% of normal cells express the 7 genes (T51560, T49941, M62994, R34701, L02426, U20428, R10707) in the corr. Intervals –a cancer cell never express all 7 genes in the same way –if expression level of improperly expressed genes can be adjusted, the cancer cell can have one common EP of normal cells –a cancer cell can then be iteratively converted into a normal one

55. Copyright  2003 limsoon wong Choosing Genes to Adjust

56. Copyright  2003 limsoon wong Doing more adjustments... Down regulating T49941 leads to 2 more top 10 EPs of normal cells to show up in the adjusted T1 Down regulating X62153 to below 396 and T72403 to below 296 leads to T1 having 9 top 10 EPs of normal cells Ave. no. of EPs in normal cells is 9 So the adjusted T1 now has impt features of normal cells

57. Copyright  2003 limsoon wong Next, eliminate common EPs of cancer cells in T1 6 more genes (K03001, T49732, U29171, R76254, D31767, L40992) are adjusted All top 10 EPs of cancer cells now disappear from T1 Ave. no. of top 10 EPs contained in cancer cells is 6 The adjusted T1 now holds enough common features of normal cells and no features of cancer cells  T1 is converted to normal cells

58. Copyright  2003 limsoon wong “Treatment Plan” Validation “Adjustments” were made to the 40 colon tumour samples based on EPs as described Classifiers trained on original samples were applied to the adjusted samples It works!

59. Copyright  2003 limsoon wong A Big But... Effective means for identifying mechanisms and pathways through which to modulate gene expression of selected genes need to be developed

60. Copyright  2003 limsoon wong Data Mining in Microarray Analysis: GENE INTERACTION PREDICTION

61. Copyright  2003 limsoon wong Beyond Classification of Gene Expression Profiles After identifying the candidate genes by feature selection, do we know which ones are causal genes and which ones are surrogates? Diagnostic ALL BM samples (n=327) 33 -3  -2  -1  0 11 22  = std deviation from mean Genes for class distinction (n=271) TEL-AML1BCR- ABL Hyperdiploid >50E2A- PBX1 MLLT-ALLNovel

62. Copyright  2003 limsoon wong Gene Regulatory Circuits Genes are “connected” in “circuit” or network Expression of a gene in a network depends on expression of some other genes in the network Can we reconstruct the gene network from gene expression data?

63. Copyright  2003 limsoon wong Key Questions For each gene in the network: which genes affect it? How they affect it? –Positively? –Negatively? –More complicated ways?

64. Copyright  2003 limsoon wong Some Techniques Bayesian Networks –Friedman et al., JCB 7:601--620, 2000 Boolean Networks –Akutsu et al., PSB 2000, pages 293--304 Differential equations –Chen et al., PSB 1999, pages 29--40 Classification-based method –Soinov et al., “Towards reconstruction of gene network from expression data by supervised learning”, Genome Biology 4:R6.1--9, 2003

65. Copyright  2003 limsoon wong A Classification-based Technique Soinov et al., Genome Biology 4:R6.1-9, Jan 2003 Given a gene expression matrix X –each row is a gene –each column is a sample –each element x ij is expression of gene i in sample j Find the average value a i of each gene i Denote s ij as state of gene i in sample j, –s ij = up if x ij > a i –s ij = down if x ij  a i

66. Copyright  2003 limsoon wong To see whether the state of gene g is determined by the state of other genes – we see whether  s ij | i  g  can predict s gj –if can predict with high accuracy, then “yes” –Any classifier can be used, such as C4.5, PCL, SVM, etc. To see how the state of gene g is determined by the state of other genes –apply C4.5 (or PCL or other “rule-based” classifiers) to predict s gj from  s ij | i  g  –and extract the decision tree or rules used A Classification-based Technique Soinov et al., Genome Biology 4:R6.1-9, Jan 2003

67. Copyright  2003 limsoon wong Advantages of this method Can identify genes affecting a target gene Don’t need discretization thresholds Each data sample is treated as an example Explicit rules can be extracted from the classifier (assuming C4.5 or PCL) Generalizable to time series

68. Copyright  2003 limsoon wong Acknowledgements Vladimir Bajic See-Kiong Ng Vladimir Brusic Huiqing Liu Jinyan Li

69. Copyright  2003 limsoon wong Data Mining in Microarray Analysis: NOTES

70. Copyright  2003 limsoon wong References J.Li, L. Wong, “Geography of differences between two classes of data”, Proc. 6th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 325--337, 2002 J.Li, L. Wong, “Identifying good diagnostic genes or gene groups from gene expression data by using the concept of emerging patterns”, Bioinformatics, 18:725--734, 2002 J.Li et al., “A comparative study on feature selection and classification methods using a large set of gene expression profiles”, GIW, 13:51--60, 2002

71. Copyright  2003 limsoon wong References E.-J. Yeoh et al., “Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling”, Cancer Cell, 1:133--143, 2002 U.Alon et al., “Broad patterns of gene expression revealed by clustering analysis of tumor colon tissues probed by oligonucleotide arrays”, PNAS 96:6745--6750, 1999 L.A.Soinov et al., “Towards reconstruction of gene networks from expression data by supervised learning”, Genome Biology 4:R6.1--9, 2003.

72. Copyright  2003 limsoon wong > Data Mining of Gene Expression Profiles for > the Diagnosis and Understanding of Diseases > > This talk is divided into two parts. In Part I, I will provide a > brief overview of some accomplishments and challenges > in Bioinformatics. In Part II, I will discuss the data mining > in the analysis of microarray gene expression profiles for > (a) diagnosis of disease state or subtype, (b) derivation of > disease treatment plan, and (c) understanding of gene > interaction networks. >