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Rearrangements and Duplications in Tumor Genomes.

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Presentation on theme: "Rearrangements and Duplications in Tumor Genomes."— Presentation transcript:

1 Rearrangements and Duplications in Tumor Genomes

2 Tumor Genomes Compromised genome stability Mutation and selection Chromosomal aberrations –Structural: translocations, inversions, fissions, fusions. –Copy number changes: gain and loss of chromosome arms, segmental duplications/deletions.

3 Rearrangements in Tumors Change gene structure, create novel fusion genes Gleevec (Novartis 2001) targets ABL-BCR fusion

4 Rearrangements in Tumors Alter gene regulation Burkitt lymphoma translocation IMAGE CREDIT: Gregory Schuler, NCBI, NIH, Bethesda, MD, USA Regulatory fusion in prostate cancer (Tomlins et al.Science Oct. 2005)

5 Complex Tumor Genomes 1)What are detailed architectures of tumor genomes? 2)What genes affected? 3)What processes produce these architectures? 4)Can we create custom treatments for tumors based on mutational spectrum? (e.g. Gleevec)

6 Common Alterations across Tumors Mutations activate/repress circuits. Multiple points of attack. “Master genes”: e.g. p53, Myc. Others probably tissue/tumor specific. repression activation Duplicated genes Deleted genes

7 Human Cancer Genome Project What tumors to sequence? What to sequence from each tumor? 1.Whole genome: all alterations 2.Specific genes: point mutations 3.Hybrid approach: structural rearrangements etc.

8 Human Cancer Genome Project What tumors to sequence? What to sequence from each tumor? 1.Whole genome: all alterations 2.Specific genes: point mutations 3.Hybrid approach: structural rearrangements etc.

9 End Sequence Profiling (ESP) C. Collins and S. Volik (UCSF Cancer Center) 1)Pieces of tumor genome: clones ( kb). Human DNA 2) Sequence ends of clones (500bp). 3) Map end sequences to human genome. Tumor DNA Each clone corresponds to pair of end sequences (ES pair) (x,y). Retain clones that correspond to a unique ES pair. yx

10 Valid ES pairs l ≤ y – x ≤ L, min (max) size of clone. Convergent orientation. End Sequence Profiling (ESP) C. Collins and S. Volik (UCSF Cancer Center) 1)Pieces of tumor genome: clones ( kb). Human DNA 2) Sequence ends of clones (500bp). 3) Map end sequences to human genome. Tumor DNA yx L

11 End Sequence Profiling (ESP) C. Collins and S. Volik (UCSF Cancer Center) 1)Pieces of tumor genome: clones ( kb). Human DNA 2) Sequence ends of clones (500bp). 3) Map end sequences to human genome. Tumor DNA yx Invalid ES pairs Putative rearrangement in tumor ES directions toward breakpoints L

12 Outline What does ESP reveal about tumor genomes? 1.Identify locations of rearrangements. 2.Reconstruct genome architecture, sequence of rearrangements. 3. In combination with other genome data (CGH).

13 ESP Data (Jan. 2006) Coverage of human genome: ≈ 0.34 for MCF7, BT474 ES pairs Clones BT474 MCF7 SKBR3 Normal Brain Breast1 Breast2 Ovary Prostate Breast Cancer Cell Lines Tumors

14 1. Rearrangement breakpoints Known cancer genes (e.g. ZNF217, BCAS3/4, STAT3) Novel candidates near breakpoints. MCF7 breast cancer Small-scale scrambling of genome more extensive than expected.

15 Structural Polymorphisms Human genetic variation more than nucleotide substitutions Short indels/inversions present (Iafrate et al. 2004, Sebat et al. 2004, Tuzun et al. 2005, McCarroll et al. 2006, Conrad et al etc.) ≈ 3% (53/1570) invalid ES pairs explained by known structural variants. s 1.6 Mb inversion s A t C-B inversion Human Variant ACB Reference Human t

16 2. Tumor Genome Architecture 1)What are detailed architectures of tumor genomes? 2)What sequence of rearrangements produce these architectures?

17 Human genome (known) Tumor genome (unknown) Unknown sequence of rearrangements Location of ES pairs in human genome. (known) Map ES pairs to human genome. B CEA D x2x2 y2y2 x3x3 x4x4 y1y1 x5x5 y5y5 y4y4 y3y3 x1x1 ESP Genome Reconstruction Problem Reconstruct tumor genome

18 Human genome (known) Tumor genome (unknown) Unknown sequence of rearrangements Location of ES pairs in human genome. (known) Map ES pairs to human genome. -C -D EA B B CEA D x2x2 y2y2 x3x3 x4x4 y1y1 x5x5 y5y5 y4y4 y3y3 x1x1 ESP Genome Reconstruction Problem Reconstruct tumor genome

19 -C -D E A B -C-DEAB Tumor Human ESP Genome Reconstruction: Comparative Genomics BCEAD Tumor

20 BCEAD -C -D E A B Tumor Human ESP Genome Reconstruction: Comparative Genomics

21 BCEAD -C -D E A B Tumor Human ESP Genome Reconstruction: Comparative Genomics

22 BCEAD -C -D E A B Tumor (x 2,y 2 ) (x 3,y 3 ) (x 4,y 4 ) (x 1,y 1 ) y 4 y 3 x 1 x 2 x 3 x 4 y 1 y 2 ESP Genome Reconstruction: Comparative Genomics

23 B C E A D Human BCEAD 2D Representation of ESP Data Each point is ES pair. Can we reconstruct the tumor genome from the positions of the ES pairs? (x 2,y 2 ) (x 3,y 3 ) (x 4,y 4 ) (x 1,y 1 ) ESP Plot Human

24 B C E A D BCEAD 2D Representation of ESP Data Each point is ES pair. Can we reconstruct the tumor genome from the positions of the ES pairs? ESP Plot

25 B C E A D Human B -D E A DAC E -C B -D EA B Reconstructed Tumor Genome ESP Plot → Tumor Genome

26 B C E A D Human BCEAD 2D Representation of ESP Data Each point is ES pair. Can we reconstruct the tumor genome from the positions of the ES pairs?

27 Human 2D Representation of ESP Data Each point is ES pair. Can we reconstruct the tumor genome from the positions of the ES pairs?

28 Real data noisy and incomplete! Valid ES pairs satisfy length/direction constraints l ≤ y – x ≤ L Invalid ES pairs indicate rearrangements experimental errors

29 Computational Approach 2.Find simplest explanation for ESP data, given these mechanisms. 3.Motivation: Genome rearrangements studies in phylogeny. 1.Use known genome rearrangement mechanisms s A t C-B s A t CB inversion HumanTumor s A t -B s A t -CBDCD translocation

30 G = [0,M], unichromosomal genome. Reversal  s,t (x)= x, if x t, t – (x – s), otherwise. Given: ES pairs (x 1, y 1 ), …, (x n, y n ) Find: Minimum number of reversals  s1,t1, …,  sn, tn such that if  =  s1,t1 …  sn, tn then (  x 1,  y 1 ), …, (  x n,  y n ) are valid ES pairs. x1x1 y1y1 G ’ =  G x1x1 y1y1 G BCA -BA x2x2 y2y2 x2x2 y2y2  t s ESP Sorting Problem

31 All ES pairs valid. t s Sequence of reversals. st x1x1 y1y1 x1x1 y1y1 BCA -C -B A y3y3 x3x3 y2y2 y3y3  t s x3x3 x2x2 y2y2 x2x2

32 Filtering Experimental Noise 1)Pieces of tumor genome: clones ( kb). Human DNA 2) Sequence ends of clones (500bp). 3) Map end sequences to human genome. Tumor DNA Rearrangement Cluster invalid pairs Chimeric clone Isolated invalid pair y x

33 Sparse Data Assumptions tumor 1.Each cluster results from single inversion. 2. Each clone contains at most one breakpoint. human y1y1 x2x2 x3x3 y3y3 y2y2 x1x1 y1y1 x2x2 x3x3 y3y3 y2y2 x1x1 tumor

34 Human ESP Genome Reconstruction: Discrete Approximation 1)Remove isolated invalid pairs (x,y)

35 Human 2)Define segments from clusters ESP Genome Reconstruction: Discrete Approximation 1)Remove isolated invalid pairs (x,y)

36 Human 3)ES Orientations define links between segment ends ESP Genome Reconstruction: Discrete Approximation 2)Define segments from clusters 1)Remove isolated invalid pairs (x,y)

37 Human ESP Genome Reconstruction: Discrete Approximation (x 2, y 2 ) (x 3, y 3 ) (x 1, y 1 ) t s 3)ES Orientations define links between segment ends 2)Define segments from clusters 1)Remove isolated invalid pairs (x,y)

38 ESP Graph Tumor genome ( ) = signed permutation of ( ) Paths in graph are tumor genome architectures. Edges: 1.Human genome segments 2.ES pairs

39 (Sankoff et al.1990) Sorting permutations by reversals Polynomial time algorithms O(n 4 ) : Hannenhalli and Pevzner, O(n 2 ) : Kaplan, Shamir, Tarjan, O(n) [distance t] : Bader, Moret, and Yan, O(n 3 ) : Bergeron, Reversal  (i,j) [inversion]  =  1  2 …  n signed permutation Problem: Given , find a sequence of reversals  1, …,  t with such that:  ¢  1 ¢  2 ¢ ¢ ¢  t = (1, 2, …, n) and t is minimal.  1 …  i-1 -  j... -  i  j+1 …  n Solution: Analysis of breakpoint graph ← ESP graph

40 Sorting Permutations 

41 Breakpoint Graph end  start Black edges: adjacent elements of  end Gray edges: adjacent elements of i = Key parameter: Black-gray cycles

42 Breakpoint Graph end  start Theorem: Minimum number of reversals to transform  to identity permutation i is: d(  ) ≥ n+1 - c(  ) where c(  ) = number of gray-black cycles. Black edges: adjacent elements of  end start end Gray edges: adjacent elements of i = ESP Graph → Tumor Permutation and Breakpoint Graph Key parameter: Black-gray cycles

43 MCF7 Breast Cancer Cell Line Low-resolution chromosome painting suggests complex architecture. Many translocations, inversions.

44 ESP Data from MCF7 tumor genome Each point (x,y) is ES pair. Coordinate in human genome 6239 ES pairs (June 2003) 5856 valid (black) 383 invalid 256 isolated (red) 127 form 30 clusters (blue)

45 MCF7 Genome Human chromosomesMCF7 chromosomes 5 inversions 15 translocations Raphael, Volik, Collins, Pevzner. Bioinformatics Sequence of

46 Array Comparative Genomic Hybridization (aCGH) 3. Combining ESP with other genome data

47 CGH Analysis Divide genome into segments of equal copy number Copy number profile Copy number Genome coordinate

48 CGH Analysis Divide genome into segments of equal copy number Copy number profile Numerous methods (e.g. clustering, Hidden Markov Model, Bayesian, etc.) Segmentation No information about: Structural rearrangements (inversions, translocations) Locations of duplicated material in tumor genome. Copy number Genome coordinate

49 CGH Segmentation How are the copies of segments linked??? Copy number Genome Coordinate Tumor genome ES pairs links segments

50 ESP + CGH ES near segment boundaries Copy number Genome Coordinate CGH breakpoint ESP breakpoint

51 ESP and CGH Breakpoints BT474 MCF7 ESP breakpoints CGH breakpoints 33 (P = 5.4 x ) (P = 1.2 x ) 730 ESP breakpoints CGH breakpoints /39 clusters 8/33 clusters

52 Microdeletion in BT Copy number ES pair ≈ 600kb Valid ES pair < 250kb “interesting” genes in this region

53 Combining ESP and CGH ES pairs links segments. Copy number balance at each segment boundary: 5 = Copy number Genome Coordinate 3 2 5

54 Combining ESP and CGH CGH copy number not exact. What genome architecture “most consistent” with ESP and CGH data? Copy number Genome Coordinate ≤ f(e) ≤ 5 1 ≤ f(e) ≤ 3 1 ≤ f(e) ≤ 4

55 Combining ESP and CGH Copy number Genome Coordinate Edge for each CGH segment. 2.Edge for each ES pair consistent with segments. 3.Range of copy number values for each CGH edge. Build graph 3 ≤ f(e) ≤ 51 ≤ f(e) ≤ 31 ≤ f(e) ≤ 4

56 Network Flow Problem Flow constraints: l(e) ≤ f(e) ≤ u(e) CGH edge: l(e) and u(e) from CGH ESP edge: l(e) = 1, u(e) = 1 f(e) Flow constraint on each CGH edge l(e) ≤ f(e) ≤ u(e) 8 e

57 Network Flow Problem Flow constraints: l(e) ≤ f(e) ≤ u(e) CGH edge: l(e) and u(e) from CGH ESP edge: l(e) = 1, u(e) = 1 f(e) Flow in = flow out at each vertex  (u,v) f( (u,v) ) =  (v,w) f( v,w) ) 8 v l(e) ≤ f(e) ≤ u(e) 8 e

58 Network Flow Problem Minimum Cost Circulation with Capacity Constraints (Sequencing by Hybridization, Sequence Assembly) Source/sink min  e  (e) Subject to: Costs:  (e) = 0, e ESP or CGH edge 1, e incident to source/sink f(e)  (u,v) f( (u,v) ) =  (v,w) f( v,w) ) 8 v l(e) ≤ f(e) ≤ u(e) 8 e Flow constraints: l(e) ≤ f(e) ≤ u(e) CGH edge: l(e) and u(e) from CGH ESP edge: l(e) = 1, u(e) = 1

59 Network Flow Results Unsatisfied flow are putative locations of missing ESP data. Prioritize further sequencing. Source/sink f(e) Targeted ESP by screening library with CGH probes.

60 Network Flow Results Identify amplified translocations –14 in MCF7 –5 in BT474 Eulerian cycle in combined graph gives tumor genome architecture. Flow values → Edge multiplicities

61 Human Cancer Genome Project What tumors to sequence? What to sequence from each tumor? 1.Whole genome: all alterations 2.Specific genes: point mutations 3.Hybrid approach: structural rearrangements etc.

62 Human Cancer Genome Project


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