Analysis of Methylation Silencing at Multiple Loci from Multiple Tumor Types

1. Analysis of Methylation Silencing at Multiple Loci from Multiple Tumor Types Karl T. Kelsey, M.D. Andres Houseman, Sc.D.

2. Genetics and the Genome Structural unit of the genome is a spatial unit described by: Promoter region Enhancer region Splice sites/introns Coding regions 3� regions for stability/transport

3. The genome has a three dimensional structure The DNA is wound approx twice around 4 dimeric histones (H2A, H2B, H3 and H4) with H1 as the linker between each nucleosome The interplay of DNA sequence and histone architecture is precise and crucially important to gene expression

5. Nucleosome Architecture N-terminal tails protrude from the nucleosome Acetylation of lysines Methylation of lysines & arginines Phosporylation of serines and threonines Ubiquitination and sumolaytion of lysines ADP-ribosylation of glutamic acids

7. EPIGENETIC Alterations Modifications in gene expression Heritable Stable Potentially reversible Compared to genetic Mutations, deletions Not reversible

9. Understand Mechanisms of Carcinogens Epigenetics � New mechanism in cancer Studying Epigenetics in Cancers Looking at exposures relationships Epigenetic changes as biomarkers

10. Getting to Cancer Oncogenes Normal genes Signal for growth, angiogenesis, etc. Get mutated or amplified in tumors ? lose control Tumor Suppressor Genes Normal genes Stop growth, prevent cell cycle, etc. Get silenced in tumors

11. Classical - Genetic Damage

12. Does this explain everything? Tumor Suppressor gene No expression in tumor Need to inactivate both copies of gene Not all inactivation is explained through mutation or deletion What about non-mutagenic carcinogens? Alternative Method of Inactivation??

13. Gene Expression

14. Where epigenetics happen!

15. DNA Methylation The covalent addition of methyl group to 5th position of cystosine Largely confined to CpG dinucleotides CpG islands - regions of more than 500 bp with CG content > 55% Islands found in promoter regions of genes Catalyzed by DNA methyl transferase.

16. Promoter CpG Island Hypermethylation

17. Consequences of Hypermethylation

18. Is this Normal? Non-coding repetitive elements Lines, Sines, Alu repeats Centromeric regions Inactive X-chromosome Imprinted genes Some gene promoters � in cell-type specific fashion

19. DNA Methylation in Cancer Aberrant Occurs in promoters of tumor suppressors Tumor specific & Clonal Silences transcription of a gene � equivalent to mutation or deletion Alternative �hit� to inactivation of tumor suppressor Targeting and specificity unclear Not a �global� phenomenon Carcinogens driving this alteration?

20. methylation silencing in cancerIs it associated with: Genes? Tissues? Age? Gender? Carcinogen exposure? Treatment-survival? Can this alteration be diagnostic?

26. Questions Does carcinogen exposure induce methylation? Are genes coordinately silenced? What is the distribution of methylation silencing? Are all tumors the same? Does this cluster?

27. Methylation silencing in surgically treated cancers: Lung: N=173 Bladder: N=351 Head and Neck: N=345 Mesothelioma: N=71

29. Can one look at this data through another lens?

30. Comparison of Methylation Profile Between Different Cancers How distinct are different tumor types with respect to methylation profile? Are methylation profiles associated with disease type? How accurately can methylation profile �predict� disease?

31. Data 4 tumor types (910 cases) Bladder Cancer (350) Head & Neck Cancer (351) Lung Cancer (138) Mesothelioma (71) 18 genes/markers 15 genes, 3 MINTs

33. Tests for Association Traditional chi-square test (4 x 18 table) ?2 = 695.5, 51 d.f., P<10-8 Permutation Test (based on chi-square statistic) P<10-3, (99th %ile of permutation dist = 55.8) Conclusion: strong association

34. Prediction

35. Jackknife Prediction Error �Leave-one-out� cross-validation For each subject i Delete subject i from data, Use remaining data to �train� model Use model to predict outcome for i Compare to actual outcome Summarize for all subjects Assessment: Misclassification rate (0-1, 0 is best) �Kappa� statistic for concordance (0-1, 1 is best) Entropy (smaller is better)

36. Jackknife Results Best prediction from multinomial regression, but results are somewhat difficult to interpret Cross-tabulation easiest to interpret but worst performance CART a compromise between prediction error and interpretability

37. CART Results

38. Analysis by Disease What can we say about patterns of methylation within a specific tumor type? How do methylation �profiles� correlate with other data (e.g. survival)? Types of analysis: Latent Class Latent Trait (Rasch Model)

39. Rasch Model Essentially a (GL) random effects model Basic model: Each gene has a different �baseline� frequency characterized by �j Each subject�s overall level of methylation is determined the value of the latent variable U.

40. Rasch Model with Survival Parametric proportional hazards model Baseline hazard modeled as Weibull Latent variable U is a survival covariate Similar results using Cox model with empirical Bayes estimates of U

41. Bladder Cancer

42. Lung Cancer

43. Mesothelioma

44. Head and Neck Cancer

45. Conclusions Bladder cancer survival significantly associated with methylation Lung cancer survival marginally associated with methylation Methylation not significantly associated with survival for mesothelioma and H&N cancer. Similar results using LCA

46. Acknowledgements HSPH Carmen Marsit Brock Christensen Heather Nelson Kim Kraunz Karen Heffernan Linqian Zhao Louise Ryan Dartmouth University Margaret Karagas