Human Cancer Genome Project Computational Systems Biology of Cancer: (I)

Human Cancer Genome Project

Computational Systems Biology of Cancer:

(I)


Bud MishraBud 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


War on CancerWar 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.


Introduction: Cancer and Introduction: Cancer and Genomics:Genomics:

What we know & what we do not

“Cancer is a disease of the genome.”


OutlineOutline

• Genomics• Genome Modification & Repair• Segmental Duplications Models


GenomicsGenomics

• 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:

= {A, T, C, G}


ComplementarityComplementarity• DNA is a double-

stranded polymer and should be thought of as a pair of sequences over .

• However, there is a relation of complementarity between the two sequences: – A , T, C , G


DNA Structure.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.


Structure and Structure and ComponentsComponents

• 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.


Inert & RigidInert & 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.


The Central DogmaThe 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.”

DNA RNA ProteinTranscription Translation


The Central DogmaThe 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.”

DNA RNA ProteinTranscription Translation


The New SynthesisThe New Synthesis

DNA RNA ProteinGenome Evolution

Selection

Part-lists, Annotation, Ontologies

Transcription Translation

Gen

oty

pe

Ph

en

oty

pe


Cancer Initiation and Cancer Initiation and ProgressionProgression

Cancer Initiation and Progression

Mutations, Translocations, Amplifications, Deletions

Epigenomics (Hyper & Hypo-Methylation)

Alternate Splicing

Proliferation, Motility, Immortality, Metastasis, Signaling


Multi-step Nature of Multi-step Nature of Cancer: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


Amplifications & DeletionsAmplifications & Deletions

Mutation in a TSG

Epigenomics

Conversion of aProto-Oncogene

Deletion of a TSG

Deletion of a TSG


P53 Gene (TSG)P53 Gene (TSG)


The Cancer Genome AtlasThe 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.


The Cancer Genome AtlasThe 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.


Specific GoalsSpecific 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.


To Achieve this goal …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.


Biomedical RationaleBiomedical 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.


Biomedical RationaleBiomedical 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).


HistoryHistory

• 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.


Scientific Foundation for a Human Scientific Foundation for a Human Cancer Genome ProjectCancer 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.


Human Genome StructureHuman Genome Structure


EBDEBD

• 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.”


Evolution by DuplicationEvolution by Duplication

Genome Evolution

Non-random distribution in Genomic data

Short words frequency Protein family sizeLong range orrelation

Motifs in cellular etworksLeu3

LEU1 BAT1 ILV2

UMP UDP

UTP CTP

Regulatory motifs Metabolic pathways Interaction clusters

Functional modules

Living cells

Organism

?


Human ConditionHuman Condition


Mer-scape…Mer-scape…

•Overlapping words of different sizes are analyzed for their frequencies along the whole human genome

•Red: 24-mers,• Green: 21-mers• Blue:18 mers• Gray:15 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.


Doublet RepeatsDoublet Repeats

• Serendipitous discovery of a new uncataloged class of short duplicate sequences; doublet repeats.– almost always < 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 0.00 57500000.00 115000000.00 172500000.00 230000000.00

Start of 10 Mb window on chromosome 2

0

200

400

600

800

Nu

mb

er

of

ele

me

nts

in

10

Mb

win

do

w


Segmental DuplicationsSegmental Duplications

• 3.5% ~ 5% of the human genome is found to contain– segmental duplications,

with length > 5 or 1kb, identity > 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.

From [Bailey, et al. 2002]

Human


The ModelThe Model

Duplication by recombination between repeats

Duplication by recombination between other repeats or other mechanisms

insertion

insertion deletion or

mutation

deletion or mutation

f - -

f ++

f + -

Mutation accumulation in the duplicated sequences

f - -

f ++

f + -


The Mathematical ModelThe Mathematical Model

0 ≤ d < ε ε ≤ d < 2ε (k-1)ε ≤ d < kε

H0

H1

α1-α-2β

1-α-2γ

1-α-β/2-γ

α

α

α

α

α

α

α

α

γ 2β

2γ β/2 2γ β/2 2γ β/2

γ 2β γ 2β

1-α-2β 1-α-2β

1-α-β/2-γ 1-α-β/2-γ

1-α-2γ 1-α-2γ

h0

h1 h1++

h1--

h0+-

h0--

h0++

f - -

f ++

f + -

α

α

α

Time after duplication

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;

α: mutation rate in duplicated sequences;

β: insertion rate of the specific repeat;

γ: mutation rate in the specific repeat;

d: divergence level of duplications;

ε: divergence interval of duplications.


Model FittingModel Fitting

Diversity:

f - -

f ++

f + -

Alu

Diversity:

f - -

f ++

f + -

L1

•The model parameters (αAlu, βAlu, γAlu, αL1, βL1, γL1) 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; h1++

Alu ≈ 0.3; h1L1 ≈ 0.76; h1++ L1 ≈ 0.35.

•The model parameters (αAlu, βAlu, γAlu, αL1, βL1, γL1) 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; h1++

Alu ≈ 0.3; h1L1 ≈ 0.76; h1++ L1 ≈ 0.35.


Polya’s UrnPolya’s Urn

Random drawn

Duplication

Put back


Repetitive Random Eccentric Repetitive Random Eccentric GODGOD

• Genome Organizing Devices (GOD)• Polya’s Urn Model:

• F’s: functions deciding probability distributions

F1 (decide an initial position)

F2 (decide selected length)

F3 (decide copy number)

k copies

F4 (decide insertion positions)


• Insertion (duplication):

3’ realignment and polymerase

reloading

normal replication

polymerase pausing and dissociation

• Deletion:

polymerase pausing and dissociation

normal replication

DNA polymerase stutteringDNA polymerase stuttering(replication slippage)(replication slippage)

DNA polymerase

3’ realignment and polymerase reloading


DNA is repaired-resulting in a duplication of the transposon and target site

Transposon inserted

Target DNA

TransposonsTransposons~~ causes: deletions and duplications causes: deletions and duplications

•Deletion:

•Duplication:

transposon

IS

IS

•Transposon actions ingenomic DNA:

transposon

Transposon looping out

Transposon deleted

Donor DNA

DNA intermediates

Transposase cuts in target DNA


DNA mismatch repair DNA mismatch repair mechanismmechanism

~ prevents duplications and deletions~ prevents duplications and deletions

MSH2 MSH6

MLH1 PMS2

Exo

MSH6MSH2

PMS2

MLH1

MSH6

MSH2

DNA polymerase

Mismatch recognition on

daughter strand

DNA -loop formation by translocation through the

proteins

Degradation of the

mismatched daughter strand in the -loop

Refilling the gap by DNA polymerase Corrected

daughter strand


Graph ModelGraph Model

Graph description•G = {V, E}, G is a directed multi-graph;

•V {Vi, all n-mer’s, i = 1…4n}, ;

•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:

C. Substitution: With probability q

A. Deletion: With probability p0

B. Duplication:With probability p1

;)dsubstitute is (

1

N

jj

i

i

k

kqVP

;1

1)ssubstitute (

NqVP

i

;)deleted is (

1

0

N

jj

i

i

k

kpVP

;)duplicated (

1

1

N

jj

i

i

k

kpVP


Model FittingModel Fitting

Real distribution from genome analysis

Expected distribution from random sequences

Model fitting results


Model Fitted ParameterModel Fitted Parameter

Mer size 6 mer 7 mer 8 mer 9 mer

q p1/p0 q p1/p0 q p1/p0 q p1/p0

M. genitalium 0.0176 1.1 0.0436 1.1 0.1587 1.5 0.3222 1.5

M. pneumoniae

0.0319 1.1 0.1151 1.5 0.2309 1.5 0.4363 1.5

P. abyssi 0.0269 1.1 0.0672 1.2 0.1778 1.4 0.3897 1.5

P. horikoshii 0.0234 1.1 0.0443 1.1 0.1390 1.3 0.3456 1.5

P. furiosus 0.0213 1.1 0.0384 1.1 0.1119 1.2 0.3114 1.5

H. pylori 0.0180 1.1 0.0320 1.1 0.0925 1.3 0.2262 1.5

H. influenzae 0.0202 1.1 0.0366 1.1 0.1364 1.5 0.2802 1.5

S. tokodaii 0.0180 1.1 0.0320 1.1 0.0925 1.3 0.2262 1.5

S. subtilis 0.0187 1.1 0.0326 1.1 0.1139 1.4 0.2585 1.5

E. coli K12 0.0207 1.1 0.0334 1.1 0.0698 1.1 0.2389 1.5

S. cerevisiae 0.0113 1.1 0.0176 1.1 0.0459 1.2 0.1311 1.4

C.elegans -- -- 0.0076 1.1 0.0115 1.1 0.0275 1.2

•The substitution rate, q, increases with the sizes of mer’s.

•The ratio between duplication and deletion rate, p1/p0, increases with sizes of mer’s.

•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.

•The substitution rate, q, increases with the sizes of mer’s.

•The ratio between duplication and deletion rate, p1/p0, increases with sizes of mer’s.

•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.


J.B.S. HaldaneJ.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 tragedy…In 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.


Human Cancer GenomeHuman Cancer Genome


CancerCancer

Normal epithelial mucosaNeoplastic polyp


A ChallengeA 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


KaryotypingKaryotyping


CGH:CGH:Comparative Genomic Hybridization.Comparative 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


CGH: Comparative Genomic CGH: Comparative Genomic Hybridization.Hybridization.


Microarray Analysis of Cancer Microarray Analysis of Cancer GenomeGenome

• 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

Normal DNA

Normal LCR

Tumor DNA

Tumor LCR

Label

Hybridize


A1

A2

A3

B1

B2

B3

C1

C2

C3

Copy Number FluctuationCopy Number Fluctuation


Measuring gene copy number differences Measuring gene copy number differences between complex genomesbetween 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

Hybridize to 500,000 features

microarray

Genomic DNA patient samples

Statsitical Analysis


Nattering Nabobs of Nattering Nabobs of NegativismNegativism

• 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.”


ChallengesChallenges

• ArrayCGH data is noisy!– Better computational biology algorithms– Better statistical modeling…In 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


Mishra’s Mystical 3M’sMishra’s Mystical 3M’s

• 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


DiscussionsDiscussions

Q&A…


Answer to CancerAnswer 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.


To be continued…To be continued…

Break…

Human Cancer Genome Project Computational Systems Biology of Cancer: (I)

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