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(I) Human Cancer Genome Project Computational Systems Biology of Cancer: Human Cancer Genome Project 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 Human Cancer Genome Project Human Cancer Genome Project 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. Human Cancer Genome Project Introduction: Cancer and Genomics: What we know & what we do not “Cancer is a disease of the genome.” Human Cancer Genome Project Outline • Genomics • Genome Modification & Repair • Segmental Duplications Models Human Cancer Genome Project 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} Human Cancer Genome Project Complementarity • DNA is a doublestranded 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 Human Cancer Genome Project 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. Human Cancer Genome Project 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. Human Cancer Genome Project 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. The Central Dogma Human Cancer Genome Project DNA Transcription RNA Translation Protein • 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.” Human Cancer Genome Project 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.” DNA Transcription RNA Translation Protein Human Cancer Genome Project Genome Evolution The New Synthesis DNA Transcription RNA Protein Translation Part-lists, Annotation, Ontologies Selection Human Cancer Genome Project Cancer Initiation and Progression Mutations, Translocations, Amplifications, Deletions Epigenomics (Hyper & HypoMethylation) Alternate Splicing Cancer Initiation and Progression Proliferation, Motility, Immortality, Metastasis, Signaling Human Cancer Genome Project 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 Human Cancer Genome Project Amplifications & Deletions Mutation in a TSG Epigenomics Conversion of a Proto-Oncogene Deletion of a TSG Deletion of a TSG Human Cancer Genome Project P53 Gene (TSG) Human Cancer Genome Project 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. Human Cancer Genome Project 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. Human Cancer Genome Project 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. Human Cancer Genome Project 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. Human Cancer Genome Project 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. Human Cancer Genome Project 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). History Human Cancer Genome Project • 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. Human Cancer Genome Project 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. Human Cancer Genome Project Human Genome Structure Human Cancer Genome Project 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.” Human Cancer Genome Project Evolution by Duplication Organism Living cells Functional modules Motifs in cellular etworks Leu3 LEU1 BAT1 UMP ILV2 Regulatory motifs UTP UDP CTP Metabolic pathways Interaction clusters Non-random distribution in Genomic data ? Long range orrelation Short words frequency Genome Evolution Protein family size Human Cancer Genome Project Human Condition Human Cancer Genome Project 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 Repeats Human Cancer Genome Project • Serendipitous discovery of a new uncataloged class of short duplicate sequences; doublet repeats. Number of elements in 10 Mb window – almost always < 100 bp 800 600 400 200 0 0.00 57500000.00 115000000.00 172500000.00 Start of 10 Mb window on chromosome 2 230000000.00 • (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 Segmental Duplications Human Cancer Genome Project • 3.5% ~ 5% of the human genome is found to contain – Human 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 Cancer Genome Project The Model f -- f -- deletion or mutation insertion f +- f +Duplication by recombination between other repeats or other mechanisms deletion or mutation insertion f ++ Duplication by recombination between repeats f ++ Mutation accumulation in the duplicated sequences The Mathematical Model Human Cancer Genome Project Time after duplication 1-α-2β h0-- 1-α-2β α 1-α-2β α α α f -- α f +- h1-γ H0 h0 2β h0+- γ α 2γ h0++ h1 2γ α 1-α-β/2-γ β/2 ε ≤ d < 2ε 2β α 1-α-β/2-γ β/2 0≤d<ε γ α 1-α-β/2-γ H1 2β 2γ α α β/2 (k-1)ε ≤ d < kε h1++ 1-α-2γ 1-α-2γ 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 f ++ 1-α-2γ 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 Fitting Human Cancer Genome Project Alu L1 f -f -- f +- f +f ++ Diversity: f ++ Diversity: •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 Urn Human Cancer Genome Project Random drawn Put back Duplication Human Cancer Genome Project Repetitive Random Eccentric GOD • 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) DNA polymerase stuttering (replication slippage) Human Cancer Genome Project • Deletion: • Insertion (duplication): normal replication DNA polymerase normal replication polymerase pausing and dissociation polymerase pausing and dissociation 3’ realignment and polymerase reloading 3’ realignment and polymerase reloading Transposons Human Cancer Genome Project ~ causes: deletions and duplications •Transposon actions in genomic DNA: Donor DNA •Duplication: transposon DNA intermediates Target DNA •Deletion: IS Transposon looping out Transposon deleted transposon IS Transposase cuts in target DNA Transposon inserted DNA is repaired-resulting in a duplication of the transposon and target site DNA mismatch repair mechanism ~ prevents duplications and deletions Human Cancer Genome Project Mismatch recognition on daughter strand DNA a-loop formation by translocation through the proteins MSH6 MSH2 Exo PMS2 Degradation of the mismatched daughter strand in the a-loop MLH1 MLH1 PMS2 MSH2 MSH6 MSH2 MSH6 Refilling the gap by DNA polymerase DNA polymerase Corrected daughter strand Human Cancer Genome Project Graph Model A. Deletion: With probability p0 B. Duplication:With probability p1 C. Substitution: With probability q 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: P (Vi is deleted ) p0 ki ; P (Vi is substitute d) q N k j 1 P (Vi duplicated ) p1 j ki j 1 k j 1 ;P (V substitute s) q i N k ki j ; N j 1 ; N 1 Human Cancer Genome Project Model Fitting Real distribution from genome analysis Expected distribution from random sequences Model fitting results Human Cancer Genome Project Model Fitted Parameter Mer size 6 mer q p1/p0 7 mer q p1/p0 8 mer q p1/p0 9 mer 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. Human Cancer Genome Project 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 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 Genome Project Human Cancer Genome Human Cancer Genome Project Cancer Normal epithelial mucosa Neoplastic polyp Human Cancer Genome Project 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 Human Cancer Genome Project Karyotyping CGH: Human Cancer Genome Project Comparative Genomic Hybridization. • • • • Amplification Deletion 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 Human Cancer Genome Project CGH: Comparative Genomic Hybridization. Human Cancer Genome Project Microarray Analysis of Cancer Genome Normal DNA • Representations are reproducible samplings of DNA populations in Tumor DNA which the resulting DNA has a new format and reduced complexity. Normal LCR Tumor LCR Label Hybridize – 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 Human Cancer Genome Project Copy Number Fluctuation A1 B1 C1 A2 B2 C2 A3 B3 C3 Human Cancer Genome Project Measuring gene copy number differences between complex genomes Genomic DNA patient samples Hybridize to 500,000 features microarray Statsitical Analysis • 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 Human Cancer Genome Project 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-tonoise issues its researchers are likely to encounter will be “absolutely enormous.” Human Cancer Genome Project Challenges • 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 Human Cancer Genome Project Mishra’s Mystical 3M’s • Rapid and accurate solutions Measure Mine Model – Bioinformatic, statistical, systems, and computational approaches. – Approaches that are scalable, agnostic to technologies, and widely applicable • Promises, challenges and obstacles— Human Cancer Genome Project Discussions Q&A… Human Cancer Genome Project 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. Human Cancer Genome Project To be continued… Break…