Definitions A class of diseases characterized by malignant growth of a group of cells Growth is uncontrolled Invasive and Damaging Often able to metastasize An instance of such a disease a malignant tumor ID: 1042315
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1. Cancer Sequencing
2. What is Cancer?DefinitionsA class of diseases characterized by malignant growth of a group of cellsGrowth is uncontrolledInvasive and DamagingOften able to metastasizeAn instance of such a disease (a malignant tumor)A disease of the genomehttp://en.wikipedia.org/wiki/Cancerhttp://faculty.ksu.edu.sa/tatiah/Pictures%20Library/normal%20male%20karyotyping.jpg
3. What is Cancer?DefinitionsA class of diseases characterized by malignant growth of a group of cellsGrowth is uncontrolledInvasive and DamagingOften able to metastasizeAn instance of such a disease (a malignant tumor)A disease of the genomehttp://en.wikipedia.org/wiki/Cancerhttp://www.moffitt.org/CCJRoot/v2n5/artcl2img4.gif
4. Fundamental Changes in Cancer Cell PhysiologyEvasion of anti-cancer control mechanismsApoptosis (e.g. p53)Antigrowth signals (e.g. pRb)Cell SenescenceHanahan and Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70. Exploitation of natural pathways for cellular growthGrowth Signals (e.g. TGF family)AngiogenesisTissue Invasion & MetastasisAcceleration of Cellular Evolution Via Genome InstabilityDNA RepairDNA Polymerase
5. Many Paths Lead to Cancer Self-SufficiencyHanahan, Douglas, and Ra Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70.
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9. http://www.dreamstime.com/stock-photos-tumor-cells-image23571283
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11. 20142000: initial draft2003: Complete human reference genome($3 billion)
12. Cancer HeterogeneityGreaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–13 (2012).
13. Why Sequence Cancer Genomes?Better understand cancer biologyPathway informationTypes of mutations found indifferent cancers
14. Why Sequence Cancer Genomes?Better understand cancer biologyPathway informationTypes of mutations found indifferent cancersCancer DiagnosisGenetic signatures of cancer types will inform diagnosisNon-invasive means of detecting or confirming presence of cancerImprove cancer therapiesTargeted treatment of cancer subtypesCOSMIC Database, v48, July 2010http://www.sanger.ac.uk/genetics/CGP/cosmic/Forbes et al. 2011. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Research 39: D945-D950Samples544809Mutations141212Papers10383Whole Genomes29
15. Why Sequence Cancer Genomes?Better understand cancer biologyPathway informationTypes of mutations found indifferent cancersCancer DiagnosisGenetic signatures of cancer types will inform diagnosisNon-invasive means of detecting or confirming presence of cancerImprove cancer therapiesTargeted treatment of cancer subtypesCOSMIC Database, v71, Oct 2014http://www.sanger.ac.uk/genetics/CGP/cosmic/Samples1058292Mutations2710449 Papers20247Whole Genomes15047
16. Why Sequence Cancer Genomes?Better understand cancer biologyPathway informationTypes of mutations found indifferent cancersCancer DiagnosisGenetic signatures of cancer types will inform diagnosisNon-invasive means of detecting or confirming presence of cancerImprove cancer therapiesTargeted treatment of cancer subtypesCOSMIC Database, v74, Sept 2015http://www.sanger.ac.uk/genetics/CGP/cosmic/Samples1,144,255Mutations3,480,051 Papers22,276Whole Genomes22,690
17. 100 bpTwo ends of the fragments are sequencedSequenced readsDNA is amplifiedDNA is cut into small fragments500 bpGenome BackgroundReference alleleAlternate allele
18. 100 bpTwo ends of the fragments are sequencedSequenced readsDNA is amplifiedDNA is cut into small fragments500 bpGenome BackgroundSequencing ErrorAlignment Error
19. Genome BackgroundTypes of SNVs in a cancer sample:Germline (SNPs)InheritedAll cells have it2. SomaticAcquired during cancer progressionNot present in normal cells
20. Factors that effect mutation signalLimited genetic material (lower depth)Mixture of tumor and normal tissueCancer HeterogeneityFactors that introduce noiseFormalin-fixed and Paraffin-embedded samplesIncreased number of mutations and unusual genomic rearrangementsGeneral ConsiderationEach individual has many unique mutations that could be confused with cancer causing mutationsConsiderations for Cancer Sequencing
21. Human Genome VariationSNPTGCTGAGATGCCGAGANovel SequenceTGCTCGGAGATGC - - - GAGAInversionMobile Element orPseudogene InsertionTranslocationTandem DuplicationMicrodeletionTGC - - AGATGCCGAGATranspositionLarge DeletionNovel Sequenceat BreakpointTGC
22. Variant TypesVariant TypesSingle Nucleotide Variants(SNVs)Small Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequence
23. SNV CallingVariant TypesSingle Nucleotide Variants(SNVs)Small Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceA bayesian approach is the most general and common method of calling SNVsMAQ, SOAPsnp, Genome Analyis ToolKit (GATK), SAMtools
24. SNV CallingVariant TypesSingle Nucleotide Variants(SNVs)Small Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequencehttp://www.broadinstitute.org/gatk//events/2038/GATKwh0-BP-5-Variant_calling.pdf
25. SNV CallingVariant TypesSingle Nucleotide Variants(SNVs)Small Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceA given human genome (germline) differs from the reference genome at millions of positions.A cancer genome differs from the healthy genome of its host by tens of thousands of positions at most, which is several orders of magnitude fewer differences than germline versus referenceHow do we distinguish germline mutations from somatic mutations?
26. Somatic SNV callingTumor TissueNormal TissueCompare the alignment resultsMost naïve: use a standard SNV caller on both datasets. If there is a mutation found in the tumor sample but not the normal, it is somatic!
27. Short Indel CallingVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceReferenceDeletionInsertion
28. Short Indel CallingVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceReferenceDeletionInsertionReferenceRead mappingin practiceUnmappable part of read (just the read end)Unmapped read (could not be alignedanywhere)
29. Short Indel Calling – Discordant Reads PairsII) DeletionI) Insertionidll - il + dlVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceReferenceReference
30. Short Indel Calling – Split Read MappingVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceReferenceReferenceDeletionRead mappingin practice
31. Short Indel Calling – Split Read MappingVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel SequenceReferenceReferenceDeletionRead mappingin practiceRemap each end of thesuspicious reads
32. Copy Number VariantsRef:A B C D E FG H I KA B C D C E FG H C I KA B C D C E FG H C I KVariant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequence
33. Copy Number VariantsRef:A B C D E FG H I KA B C D C E FG H C I KC C C C Depth of CoverageModified from Dalca and Brudno. 2010. Genome variation discovery with high-throughput sequencing data. Briefings in bioinformatics 11, no. 1: 3-14Variant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequence
34. Problems with DOC Very sensitive to stochastic variance in coverageSensitive to bias coverage (e.g. GC content).Impossible to determine non-reference locations of CNVsGraph methods using paired-end reads help overcome some of these problemsCopy Number VariantsRef:A B C D E FG H I KA B C D C E FG H C I KC C C C Depth of CoverageVariant TypesSingle Nucleotide Variants(SNVs)Small Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequence
35. Variant TypesRef:A B C D E FG H I K1 2 34 5 6 7 84 G I K1 2 31 2 4 3 5 6 7 8Structural RearrangementTranslocation3 2 1 5 6 7 8Inversion1 3 5 9 6 7 8Large Insertion / Deletion^2Variant TypesSingle Nucleotide Variants(SNVs)Short Insertion / Deletion (indels)Copy Number Variants (CNVs)Structural Variants (SVs)Novel Sequence
36. Summary of Variant TypesMeyerson et al. . 2010. Advances in understanding cancer genomes through second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696
37. Tracking the Evolution of Cancer
38. Source:http://marnieclark.com/category/types-of-breast-cancer/dcis/Ductal carcinoma in situ (DCIS)Invasive Ductal Carcinoma (IDC)
39. NormalEarly NeoplasiaDuctal CarcinomaIn situProgressionInvasive Ductal CarcinomaIDCENEN
40. P1P2P3P4P5P6LymphNormalENENADCISIDCLesionsPatientsQuestionsWhat is the genetic relationship of lesions to one another?What are the early genomic events? Types of VariantsSomatic SNVsAneuploidies>50x whole genome coverage per sample
41. Variant calling processAlignmentLocal RealignmentBase quality RecalibrationPCR duplicationremovalGATK Unified-GenotyperFiltersRescue ChallengesPipelineSequencing ErrorMapping ErrorSequencing CoverageNormal ContaminationGermlineSomaticFinal Variants Set
42. ENIDCNormalSomatic SNVs as lineage markers
43. Somatic SNVs as lineage markersIDC (1000 SNVs)EN(300 SNVs)200shared800IDC only100EN onlyENIDCNormal
44. IDC (1000 SNVs)EN(300 SNVs)200shared800IDC only100EN only200Somatic SNVs as lineage markersENIDCNormal
45. IDC (1000 SNVs)EN(300 SNVs)200shared800IDC only100EN only800100Somatic SNVs as lineage markersENIDCNormal
46. ENIDCNormalSomatic SNVs as lineage markersIDC (1000 SNVs)EN(300 SNVs)1000300
47. Patient 2 – SNVsENDCISIDC515803313370681
48. Patient 2 – SNVsENDCISIDC515803313370NormalDCISENIDC681
49. Patient 2 – SNVsENDCISIDC5153370Alternate Allele Fraction in ENNormalDCISENIDC68113380
50. AneuploidiesCopy GainCopy Loss
51. Lesser allele fractionChromosomePatient 2 - AneuploidiesPlots are windows of 1000 SNPs overlapping by 500
52. 1q: GainX: Loss 16q: Loss 16p: GainPatient 2 - Aneuploidies
53. Patient 2 - Aneuploidies
54. 1q16p16qXPatient 2 – Final TreeAneuploidies in 14 other chromosomesNormalDCISENIDC
55. Results
56. Branched tree modelVictoria PopicAutomated Inference of Multi-Sample Cancer PhylogeniesRaheleh SalariSample 1Sample 3SMutH: Somatic Mutation HierarchiesSample 2
57. VAF profiles of SNVs across samples
58. VAF profiles of SNVs – Clustering
59. Edge u v :Cell-Lineage VAF Constraintuv“Possibly mutations in u happened before those in v”
60. For each node u and its children C :Tree ConstructionFind all spanning trees that satisfy VAF constraints(extension of Gabow&Myers spanning tree search algorithm)Rank trees according to their agreement with VAFsuvw
61. Simulation ResultsPred: pairs of nodes ordered correctlyBranch: pairs of nodes correctly assigned to separate branchesShared edges: edges shared between true and reconstructed treesuvwuvwzyx
62. ccRCC Study of Renal Carcinoma by Gerlinger et. al (2014)HGSC Study of Ovarian Cancer Bashashati et. al (2013)Reconstruction of Lineage Trees in Recent Literature
63. PIK3CA H1047R PIK3CA H1047L Expanded Breast Cancer Lineage Trees
64. What is a haplotype?AAAATTTAAAA A CC T AORA T CC A AORA T AC A CA A AC T AORcorrectwrongwrongwrong?
65. AAAATTTAAAWhat is a haplotype?Phasing is the process of recovering haplotypes from genotype data
66. The importance of phase informationCompound HeterozygosityTwo-hit cancer modelDifferent phenotypes
67. MolecularGeneticPopulationUnrelated duos, triosPedigreesTriosEach physical read is a single molecule that can be directly phasedIBD analysis, inheritance state analysisPopulation inferenceRead AssemblyDifferent Approaches for PhasingPopulationGeneticMolecularCommon variantsYesYesYesPrivate variantsNoYesYesSomatic variantsNoNoYes