New mechanism in cancer Studying Epigenetics in Cancers

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Analysis of Methylation Silencing at Multiple
Loci from Multiple Tumor Types

• Karl T. Kelsey, M.D.
• Andres Houseman, Sc.D.

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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

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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

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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

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EPIGENETIC Alterations

• Modifications in gene expression
• Heritable
• Stable
• Potentially reversible
• Compared to genetic
• Mutations, deletions
• Not reversible

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Understand Mechanisms of Carcinogens

• Epigenetics New mechanism in cancer
• Studying Epigenetics in Cancers
• Looking at exposures relationships
• Epigenetic changes as biomarkers

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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

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Classical - Genetic Damage
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• Carcinogen damages base
• No repair
• Replication

• Selection occurs
• Cell with mutation multiplies
• Clonal field established

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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??

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Gene Expression
Promoter Region
Gene
RNA
Transcription factors
RNA Polymerase
Proteins
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Where epigenetics happen!
Promoter Region
Gene
CpG Island
CpG
CpG dinucleotides are under-represented in the
genome, but over-represented in promoter regions
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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 gt 55
• Islands found in promoter regions of genes
• Catalyzed by DNA methyl transferase.

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Promoter CpG Island Hypermethylation
CpG
methyl-CpG
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Consequences of Hypermethylation
Transcription repressors
MBD
CpG
methyl-CpG
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Is this Normal?
YES!

• Non-coding repetitive elements
• Lines, Sines, Alu repeats
• Centromeric regions
• Inactive X-chromosome
• Imprinted genes
• Some gene promoters in cell-type specific
  fashion

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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?

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methylation silencing in cancerIs it associated
with

• Genes?
• Tissues?
• Age?
• Gender?
• Carcinogen exposure?
• Treatment-survival?
• Can this alteration be diagnostic?

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OpinionCpG ISLAND METHYLATOR PHENOTYPE IN CANCER
Jean-Pierre Issa   about the author
Jean-Pierre Issa is at the Department of
Leukemia, M. D. Anderson Cancer Center, Unit 425,
1515 Holcombe, Houston, Texas 77030, USA.
jpissa_at_mdanderson.org DNA hypermethylation in
CpG-rich promoters is now recognized as a common
feature of human neoplasia. However, the
pathophysiology of hyper-methylation (why, when,
where) remains obscure. Cancers can be classified
according to their degree of methylation, and
those cancers with high degrees of methylation
(the CpG island methylator phenotype, or CIMP)
represent a clinically and aetiologically
distinct group that is characterized by
'epigenetic instability'. Furthermore,
CIMP-associated cancers seem to have a distinct
epidemiology, a distinct histology, distinct
precursor lesions and distinct molecular features.
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An important hurdle in the field will be to
achieve a consensus definition for the CpG island
methylator phenotype (CIMP). This is no trivial
issue, given the variety of methods available for
studying DNA methylation, each of which might
give a slightly different definition70. The
choice of genes and the minimal number of genes
examined is also essential. In all studies of
CIMP so far (positive or negative), each group
has used different methods and different genes,
which can only contribute to the confusion.
Moreover, the choice of genes is also tissue-type
dependent, and a definition for colon cancer
might not be applicable to other cancers.
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Until such experiments are completed, our
laboratory has been defining CIMP in colon
cancers by quantitatively studying a reduced set
of genes, namely MINT1, MINT2, MINT31, CDKN2A and
MLH1.
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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?

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Methylation silencing in surgically treated
cancers

• Lung N173
• Bladder N351
• Head and Neck N345
• Mesothelioma N71

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Distribution of Tumor Suppressor Gene Methylation
by Disease
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Can one look at this data through another lens?
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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?

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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

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Tests for Association

• Traditional chi-square test (4 x 18 table)
• ?2 695.5, 51 d.f., Plt10-8
• Permutation Test (based on chi-square statistic)
• Plt10-3, (99th ile of permutation dist 55.8)
• Conclusion strong association

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Prediction
CART K-means Simple parametric model based on
cross-tabulation Multinomial regression model
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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)

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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

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CART Results
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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)

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Rasch Model

• Essentially a (GL) random effects model
• Basic model
• Each gene has a different baseline frequency
  characterized by ßj
• Each subjects overall level of methylation is
  determined the value of the latent variable U.

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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

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Bladder Cancer
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Lung Cancer
shape
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Mesothelioma
shape
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Head and Neck Cancer
shape
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Conclusions

• Bladder cancer survival significantly associated
  with methylation
• Lung cancer survival marginally associated with
  methylation
• Methylation not significantly associated with
  survival for mesothelioma and HN cancer.
• Similar results using LCA

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Acknowledgements

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

• Brigham and Womens Hospital
• David Sugarbaker
• Raphael Bueno
• Jonathan Fletcher
• Bill Richards
• John Godleski
• University of California, San Francisco
• John Wiencke