Computational Systems Biology of Cancer

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(I)
Computational Systems Biology of Cancer
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Bud Mishra

• Professor of Computer Science, Mathematics and
  Cell Biology
• Courant Institute, NYU School of Medicine, Tata
  Institute of Fundamental Research, and Mt. Sinai
  School of Medicine

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(No Transcript)
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War on Cancer

• Reports that say that something hasn't happened
  are always interesting to me, because as we know,
  there are known knowns there are things we know
  we know. We also know there are known unknowns
  that is to say we know there are some things we
  do not know. But there are also unknown unknowns
  the ones we don't know we don't know.
• US Secretary of Defense, Mr. Donald Rumsfeld,
  Quoted completely out of context.

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Introduction Cancer and Genomics

• What we know what we do not
• Cancer is a disease of the genome.

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Outline

• Genomics
• Genome Modification Repair
• Segmental Duplications Models

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Genomics

• Genome
• Hereditary information of an organism is encoded
  in its DNA and enclosed in a cell (unless it is a
  virus). All the information contained in the DNA
  of a single organism is its genome.
• DNA molecule can be thought of as a very long
  sequence of nucleotides or bases
• S A, T, C, G

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Complementarity

• DNA is a double-stranded polymer and should be
  thought of as a pair of sequences over S.
• However, there is a relation of complementarity
  between the two sequences
• A , T, C , G

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DNA Structure.

• The four nitrogenous bases of DNA are arranged
  along the sugar- phosphate backbone in a
  particular order (the DNA sequence), encoding all
  genetic instructions for an organism.
• Adenine (A) pairs with thymine (T), while
  cytosine (C) pairs with guanine (G).
• The two DNA strands are held together by weak
  bonds between the bases.

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Structure and Components

• Complementary base pairs
• (A-T and C-G)
• Cytosine and thymine are smaller (lighter)
  molecules, called pyrimidines
• Guanine and adenine are bigger (bulkier)
  molecules, called purines.
• Adenine and thymine allow only for double
  hydrogen bonding, while cytosine and guanine
  allow for triple hydrogen bonding.

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Inert Rigid

• Thus the chemical (hydrogen bonding) and the
  mechanical (purine to pyrimidine) constraints on
  the pairing lead to the complementarity and makes
  the double stranded DNA both chemically inert and
  mechanically quite rigid and stable.

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The Central Dogma

• The central dogma(due to Francis Crick in 1958)
  states that these information flows are all
  unidirectional
• The central dogma states that once information'
  has passed into protein it cannot get out again.

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The Central Dogma

• The transfer of information from nucleic acid
  to nucleic acid, or from nucleic acid to protein,
  may be possible, but transfer from protein to
  protein, or from protein to nucleic acid is
  impossible. Information means here the precise
  determination of sequence, either of bases in the
  nucleic acid or of amino acid residues in the
  protein.

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The New Synthesis
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Cancer Initiation and Progression
Mutations, Translocations, Amplifications,
Deletions Epigenomics (Hyper Hypo-Methylation) A
lternate Splicing
Cancer Initiation and Progression
Proliferation, Motility, Immortality, Metastasis,
Signaling
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Multi-step Nature of Cancer

• Cancer is a stepwise process, typically requiring
  accumulation of mutations in a number of genes.
• 6-7 independent mutations typically occur over
  several decades
• Conversion of proto-oncogenes to oncogenes
• Inactivation of tumor suppressor gene

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Amplifications Deletions
Mutation in a TSG
Epigenomics
Conversion of a Proto-Oncogene
Deletion of a TSG
Deletion of a TSG
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P53 Gene (TSG)
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The Cancer Genome Atlas

• Obtain a comprehensive description of the genetic
  basis of human cancer.
• Identify and characterize all the sites of
  genomic alteration associated at significant
  frequency with all major types of cancers.

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The Cancer Genome Atlas

• Increase the effectiveness of research to
  understand
• tumor initiation and progression,
• susceptibility to carcinogensis,
• development of cancer therapeutics,
• approaches for early detection of tumors
• the design of clinical trials.

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Specific Goals

• Identify all genomic alterations significantly
  associated with all major cancer types.
• Such knowledge will propel work by thousands of
  investigators in cancer biology, epidemiology,
  diagnostics and therapeutics.

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To Achieve this goal

• Create large collection of appropriate,
  clinically annotated samples from all major types
  of cancer and

• Characterize each sample in terms of
• All regions of genomic loss or amplification,
• All mutations in the coding regions of all human
  genes,
• All chromosomal rearrangements,
• All regions of aberrant methylation, and
• Complete gene expression profile, as well as
  other appropriate technologies.

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Biomedical Rationale

• Cancer is a heterogeneous collection of
  heterogeneous diseases.
• For example, prostate cancer can be an indolent
  disease remaining dormant throughout life or an
  aggressive disease leading to death.
• However, we have no clear understanding of why
  such tumors differ.

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Biomedical Rationale

• Cancer is fundamentally a disease of genomic
  alteration.
• Cancer cells typically carry many genomic
  alterations that confer on tumors their
  distinctive abilities (such as the capacity to
  proliferate and metastasize, ignoring the normal
  signals that block cellular growth and migration)
  and liabilities (such as unique dependence on
  certain cellular pathways, which potentially
  render them sensitive to certain treatments that
  spare normal cells).

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History

• 1960s
• The genetic basis of cancer was clear from
  cytogenetic studies that showed consistent
  translocations associated with specific cancers
  (notably the so-called Philadelphia chromosome in
  chronic myelogenous leukemia).
• 1970s
• Recognize specific cancer-causing mutations
  through recombinant DNA revolution of the 1970s.
• The identification of the first vertebrate and
  human oncogenes and the first tumor suppressor
  genes,
• These discoveries have elucidated the cellular
  pathways governing processes such as cell-cycle
  progression, cell-death control, signal
  transduction, cell migration, protein
  translation, protein degradation and
  transcription.
• For no human cancer do we have a comprehensive
  understanding of the events required.

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Scientific Foundation for a Human Cancer Genome
Project

• Gene resequencing.
• Specific gene classes (such as kinases and
  phosphatases) in particular cancer types.
• Epigenetic changes.
• Loss of function of tumor suppressor genes by
  epigenetic modification of the genome such as
  DNA methylation and histone modification.
• Genomic loss and amplification.
• Consistent association with genomic loss or
  amplification in many specific regions,
  indicating that these regions harbor key cancer
  associated genes

• Chromosome rearrangements.
• Activate kinase pathways through fusion proteins
  or inactivating differentiation programs through
  gene disruption.
• Hematological malignancies a single
  stereotypical translocation in some diseases
  (such as CML) and as many as 20 important
  translocations in others (such as AML).
• Adult solid tumors have not been as well
  characterized, in part owing to technical
  hurdles.

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Human Genome Structure
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EBD

• J.B.S. Haldane (1932)
• A redundant duplicate of a gene may acquire
  divergent mutations and eventually emerge as a
  new gene.
• Susumu Ohno (1970)
• Natural selection merely modified, while
  redundancy created.

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Evolution by Duplication
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Human Condition
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Mer-scape

• Overlapping words of different sizes are analyzed
  for their frequencies along the whole human
  genome
• Red 24-mers,
• Green 21-mers
• Blue18 mers
• Gray15 mers
• To the very left is a ubiquitous human
  transposon Alu. The high frequency is indicative
  of its repetitive nature.
• To the very right is the beginning of a gene. The
  low frequency is indicative of its uniqueness in
  the whole genome.

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Doublet Repeats

• Serendipitous discovery of a new uncataloged
  class of short duplicate sequences doublet
  repeats.
• almost always lt 100 bp
• (Top) . The distance between the two loci of a
  doublet is plotted versus the chromosomal
  position of the first locus.
• (Bottom) Distribution of doublets (black) and
  segmental duplications (red) across human
  chromosome 2

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Segmental Duplications

• 3.5 5 of the human genome is found to contain
• segmental duplications, with length gt 5 or 1kb,
  identity gt 90.
• These duplications are estimated to have emerged
  about 40Mya under neutral assumption.
• The duplications are mostly interspersed
  (non-tandem), and happen both inter- and
  intra-chromosomally.

Human
From Bailey, et al. 2002
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The Model
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The Mathematical Model
0 d lt e
e d lt 2e
(k-1)e d lt ke
h1 proportion of duplications by repeat
recombination h1 proportion of
duplications by recombination of the specific
repeat h1- - proportion of duplications
by recombination of other repeats h0 proportion
of duplications by other repeat-unrelated
mechanism h0 proportion of h0 with
common specific repeat in the flanking regions
h0- proportion of h0 with no common
specific repeat in the flanking regions
h0- - proportion of h0 with no specific repeat
in the flanking regions
a mutation rate in duplicated sequences ß
insertion rate of the specific repeat ?
mutation rate in the specific repeat d
divergence level of duplications e divergence
interval of duplications.
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Model Fitting

• The model parameters (aAlu, ßAlu, ?Alu, aL1, ßL1,
  ?L1) are estimated from the reported mutation and
  insertion rates in the literature.
• The relative strengths of the alternative
  hypotheses can be estimated by model fitting to
  the real data.
• h1Alu 0.76 h1Alu 0.3 h1L1 0.76 h1 L1
  0.35.

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Polyas Urn
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Repetitive Random Eccentric GOD

• Genome Organizing Devices (GOD)
• Polyas Urn Model
• Fs functions deciding probability distributions

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DNA polymerase stuttering(replication slippage)

• Insertion (duplication)

• Deletion

normal replication
DNA polymerase
normal replication
polymerase pausing and dissociation
polymerase pausing and dissociation
3 realignment and polymerase reloading
3 realignment and polymerase reloading
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Transposons causes deletions and duplications

• Transposon actions in
• genomic DNA

Donor DNA

• Duplication

transposon
DNA intermediates
Target DNA
Transposase cuts in target DNA

• Deletion

transposon
IS
IS
Transposon looping out
Transposon inserted
Transposon deleted
DNA is repaired-resulting in a duplication of the
transposon and target site
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DNA mismatch repair mechanism prevents
duplications and deletions
Mismatch recognition on daughter strand
Degradation of the mismatched daughter strand in
the a-loop
DNA a-loop formation by translocation through the
proteins
Refilling the gap by DNA polymerase
Corrected daughter strand
DNA polymerase
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Graph Model
A. Deletion With probability p0

• Graph description
• G V, E, G is a directed multi-graph
• V ? Vi, all n-mers, i 14n,
• E ? (Vi , Vj ), when Vi represents the n-mer
  immdiately upstream of the n-mer represented by
  Vj in the genomic sequence
  
• ki incoming (or outgoing) degree of node i
  (Vi) copy number of the n-mer represented by
  Vi
• During the graph evolution, at each iteration,
  one of the following happens deletion (with
  probability p0), duplication (with probability
  p1) or substitution (with probability q). p0 p1
  q 1.
• For an arbitrary node Vi , the probabilities of
  one of the above events happens is as follows

B. DuplicationWith probability p1
C. Substitution With probability q
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Model Fitting
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Model Fitted Parameter

• The substitution rate, q, increases with the
  sizes of mers.
• The ratio between duplication and deletion rate,
  p1/p0, increases with sizes of mers.
• The substitution rate, q, tends to decrease when
  the genome sizes are larger. Especially, q is
  much smaller in eukaryotic genomes than in
  prokaryotic genomes.

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J.B.S. Haldane

• If I were compelled to give my own appreciation
  of the evolutionary process, I should say this
  In the first place it is very beautiful. In that
  beauty, there is an element of tragedyIn an
  evolutionary line rising from simplicity to
  complexity, then often falling back to an
  apparently primitive condition before its end, we
  perceive an artistic unity
• To me at least the beauty of evolution is far
  more striking than its purpose.
• J.B.S. Haldane, The Causes of Evolution. 1932.

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Human Cancer Genome
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Cancer
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A Challenge

• At present, description of a recently diagnosed
  tumor in terms of its underlying genetic lesions
  remains a distant prospect. Nonetheless, we look
  ahead 10 or 20 years to the time when the
  diagnosis of all somatically acquired lesions
  present in a tumor cell genome will become a
  routine procedure.
• Douglas Hanahan and Robert Weinberg
• Cell, Vol. 100, 57-70, 7 Jan 2000

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Karyotyping
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CGHComparative Genomic Hybridization.

• Equal amounts of biotin-labeled tumor DNA and
  digoxigenin-labeled normal reference DNA are
  hybridized to normal metaphase chromosomes
• The tumor DNA is visualized with fluorescein and
  the normal DNA with rhodamine
• The signal intensities of the different
  fluorochromes are quantitated along the single
  chromosomes
• The over-and underrepresented DNA segments are
  quantified by computation of tumor/normal ratio
  images and average ratio profiles

Amplification
Deletion
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CGH Comparative Genomic Hybridization.
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Microarray Analysis of Cancer Genome

• Representations are reproducible samplings of DNA
  populations in which the resulting DNA has a new
  format and reduced complexity.
• We array probes derived from low complexity
  representations of the normal genome
• We measure differences in gene copy number
  between samples ratiometrically
• Since representations have a lower nucleotide
  complexity than total genomic DNA, we obtain a
  stronger specific hybridization signal relative
  to non-specific and noise

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Copy Number Fluctuation
A1
B1
C1
A2
B2
C2
A3
B3
C3
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Measuring gene copy number differences between
complex genomes

• Compare the genomes of diseased and normal
  samples
• Error Control
• The use of representations augmenting microarrays
• Representations reproducibly sample the genome
  thereby reducing its complexity. This increases
  the signal-to-noise ratio and improves
  sensitivity
• Statistical Modeling the sources of Noise
• Bayesian Analysis

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Nattering Nabobs of Negativism

• Some scientists are concerned about the cost and
  the possibility that a project of this scale
  could take money away from smaller ones
• Craig Venter, who led a private project to
  determine the human DNA blueprint in competition
  with the human genome project, said it would make
  more sense to look at specific families of genes
  known to be involved in cancer.
• Lee Hood, president of the Institute for Systems
  Biology, has called the premise of the Cancer
  Genome Project naïve, suggesting that
  signal-to-noise issues its researchers are likely
  to encounter will be absolutely enormous.

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Challenges

• ArrayCGH data is noisy!
• Better computational biology algorithms
• Better statistical modelingIn some technologies,
  the noise is systematic (e.g., affy SNP-chips)
• Better bio-technologies (GRIN Genomics,
  Robotics, Informatics, Nanotechnology)
• No efficient technology for epigenomics,
  translocations or de novo mutations.
• Algorithms for multi-locus association studies
• Systems view of cancer by integrating data from
  multiple sources
• Genomic, Epigenomic, Transcriptomic, Metabolomic
  Proteomic.
• Regulatory, Metabolic and Signaling pathways

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Mishras Mystical 3Ms

• Rapid and accurate solutions
• Bioinformatic, statistical, systems, and
  computational approaches.
• Approaches that are scalable, agnostic to
  technologies, and widely applicable
• Promises, challenges and obstacles

Measure
Mine
Model
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Discussions

• QA

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Answer to Cancer

• If I know the answer I'll tell you the answer,
  and if I don't, I'll just respond, cleverly.
• US Secretary of Defense, Mr. Donald Rumsfeld.

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To be continued

• Break