Copy Number Variations

Copy Number Variations DTC BioInformatics Course WTHCG, Friday 3rd of May 2013

Jean-Baptiste Cazier Bioinformatics Core Leader, Department of Oncology http://www.oncology.ox.ac.uk/research/jean-baptiste-cazier [email protected]

Outline • 

Definitions –  More important than it may seem

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Identification –  Technology, Algorithmic, Design

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Classic studies –  McCarrol & Korn, GSV, WTCCC, Obesity Short Break

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The special case of Cancer –  More problems

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High Throughput Sequencing –  Cancer Case again

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Conclusions

JB Cazier, May 2013

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Definitions • 

Acronyms: –  CNP: • 

Copy Number Polymorphisms

–  CNV: • 

Copy Number Variations

–  CNA: •  • 

Copy Number Aberrations Copy Number Alterations

Finding the missing heritability of complex diseases TA Manolio et al. Nature 461, 747-753 (2009)

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Creation: Germline vs Somatic –  Is the CNV coming from the original cell or did it evolve only in a few ? •  • 

There are very many CNVs shared among population like SNPs or STRs Somatic propagation of CNVs is a mark of Cancer

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Revival • 

Genome-Wide Association provided some success in the identification of variants for many diseases: –  AMD, Coeliac disease, Type 2 Diabetes, Prostate Cancer, Colorectal Cancer, etc.

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However most variants are ‘only’ statistically significant: –  80% fall outside of coding regions

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The case of Missing Heritability: –  Whatever the number of variants identified, they usually account for only a small proportion of the heritability

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Missing Heritability • 

Need to find other “reasons” to explain the difference.

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Heritability definition – 

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Proportion of phenotypic variance attributable to additive genetic factors

The Common Variant Common Disease model is challenged – 

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Look for more markers • 

Rarer with strong effect

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Common with lower effect Gene-Gene interaction Shared environment

Feasibility of identifying genetic variants by risk allele frequency and strength of genetic effect (odds ratio).

This is essentially a question of power • 

Groups are joining forces in very large consortium

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Better technological coverage of the rarer variants

More variant types •  • 

Copy Number Variation InDels, Segmental Duplications.

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Comparable phenotyping in meta analysis ?

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The ‘Dark Matter’ – 

Does it really exists ?

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Can we see it beyond its influence ?

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Gain, Loss, etc • 

Normal: –  2 chromosomes are inherited, one from each parents

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Deletion: –  Homozygous: 0 copy left –  Hemizygous: 1 copy left –  Sizeable event: => Not an InDels

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Gain –  –  –  – 

Can be 3, 4, 5, … copies Most often nearby duplication, but not always Sizeable event: Not Line, Sine, repeats, etc.

Copy Number Variation in Human Health, Disease, and Evolution Zhang F et al, Ann. Rev. of Gen. and Hum. Gen. 2009 (10) 451-481

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Copy Neutral Loss of Heterozygosity –  Not Copy Number Polymorphism per se, but needs to be addressed

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CNV in color

SNP array

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Chromosome aberrations in solid tumors Donna G et al. Nature Genetics 34, 369 - 376 (2003)

a)  Aberrations leading to aneuploidy. b)  Aberrations leaving the chromosome apparently intact 7

Mechanisms •  4 main mechanisms in the generation of CNV: –  NAHR • 

Non-Allelic Homologous Recombination

–  NHEJ • 

Non-Homologous End-Joining

–  FoSTeS • 

Fork Stalling and Template Switching

–  L1 retrotransposition

Copy Number Variation in Human Health, Disease, and Evolution Zhang F et al, Ann. Rev. of Gen. and Hum. Gen. 2009 (10) 451-481

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Characterization • 

Identification: a Genome-Wide test –  Karyotyping –  Spectral Karyotyping (SKY)

–  –  –  – 

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Comparative Genetic Hybridization (CGH) Array CGH (aCGH) “SNP”- array High- Throughput Sequencing

Validation: a local test –  –  –  – 

qPCR: quantitative Polymerase Chain Reaction MLPA: Multiplex Ligation-dependent Probe Amplification Fluorescent In-Situ Hybridization (FISH) Sequencing

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Array technology •  Array CGH –  Agilent, Nimblegen –  2 channels: compare hybridization level to a common background reference –  Usually 42 million probes genome-wide • 

Resolution up to 200bp

•  SNP array –  Illumina, Affymetrix –  Test one or few samples at a time –  Initially developed for genotyping • 

2 channels: allele A/B

–  Increasing density of markers •  • 

From 10,000 Linkage SNPs Up to 5M SNPs and CNV probes

Affymetrix JB Cazier, May 2013

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SNP-array signature • 

Sample data for a number of different copy number and LOH events. –  The Log R Ratio scales with copy number –  The distribution of the B allele frequency is governed by a more complex relationship with allowable genotypes.

Simulation

Gain Real data

Neutral Loss

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Copy Number Loss

SNP array

aCGH

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Copy Number Loss and Gain

SNP array

aCGH

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Mixed Cell Population

SNP array

aCGH

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Copy Neutral LOH

SNP array

aCGH

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Automatic recognition of CNVs • 

Originally done by visual inspection –  Problem of reproducibility –  Problem of accuracy –  With increasing density, problem of possibility to see

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Automation and test –  Moving average –  Probe selection / compilation –  Segmentation, Hidden Markov Model –  Significance testing

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Need to compile data with uncertainty

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Moving average

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Automatisation by use of Hidden Markov Model •  Select automatically the optimal Copy Number sequence over a chromosome to fit the Model •  Evaluate the probability of the sequence of intensity signal fitting this model –  Can test various models and select the most appropriate

•  The Model can be trained simply by feeding “typical” data sets –  –  –  – 

2 1 0

Look for minimum number of changes Look for maximum instability Select a most likely default state …

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2 1 0 2 1 0

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Process •  Definition:

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Start Value:

–  Find the underlying states giving the observation •  –  Underlying states are the number of copies: 0,1,2, … –  Observation is the Signal Intensity •  –  Defined by 3 probabilistic entities

(P(0), P(1), P(2))

State Transition: (P(0|0), P(1|0), P(2 |0), P(0|1), P(1 |1), P(2 |1), P(0|2), P(1 |2), P(2 |2))

Emission probability (P(Obs|0), P(Obs |1), P(Obs|2))

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2

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Obs1

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Obs2

Obs3

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Obs4

ObsN

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Segmentation CNAM employs a powerful optimal segmenting algorithm using dynamic programming to detect inherited and de novo CNVs on a per-sample (univariate) and multi-sample (multivariate) basis. Unlike Hidden Markov Models, which assume the means of different copy number states are consistent, optimal segmenting properly delineates CNV boundaries in the presence of mosaicism, even at a single probe level, and with controllable sensitivity and false discovery rate.

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Available software •  Graphical Interface: –  –  –  –  –  –  –  –  –  – 

Agilent Golden Helix Nexus Partek BeadStudio/GenomeStudio Golf CNAT CNAG dChip …

•  Uneven field of quality and specificity JB Cazier, May 2013

•  Command line –  –  –  –  –  –  – 

QuantiSNP PennCNV BirdSuite PICNIC ASCAT OncoSNP * …

•  R packages –  Somatics* * –  DNACopy –  CNVTools –  Aroma –  … * Cancer Specific tools

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Development of array •  In 2008 McCarroll and Korn published the identification of CNPs and CNVs using/designing Affymetrix SNP 6.0 high resolution array

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SNP 6.0 by McCarroll • 

“ We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs)” McCarroll

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Published both analysis with chip design and algorithm suite: BirdSuite –  Perform both genotyping and CNV identification –  First call for known CNP –  Look for new CNV

•  •  • 

80% of observed copy number differences due to common CNPs (MAF>5%), > 99% derived from inheritance rather than new mutation. Found a common deletion polymorphism in perfect LD with Crohn’s disease SNPs –  2kb upstream IRGM –  Affect level of expression

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High density of probes • 

Can identify smaller events –  E.g. Important to spot residual event in translocation/fusion genes

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Gain confidence in SNP-regions by increasing the number of probes

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Can get better resolutions, i.e. more accurate breakpoints: –  Can split existing large regions into smaller ones

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Better coverage of CNP –  These regions were mainly not be covered by SNP-only arrays –  Beware of overrepresentation of these regions

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Tiling across the genome –  More exhaustive picture

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Increase density

4 2 1

Copy Number 10K

4 2 1

250K Nsp

4 2 1

250K Sty

4 2 1

6.0

Loss of 65Kb region confidently identified only with SNP 6.0, Bryan Young et al, Cancer Research UK JB Cazier, May 2013

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Too much data ? t-test t-test on Run I

t-test on Run II

Summation of I and II 4 2 1

Copy Number

Log 2 Ratio I

4 2 1

Log 2 Ratio II

Replicates increase signal to noise ratio and avoid false positives and true negatives But it costs twice as much ! JB Cazier, May 2013

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Potential Issues •  Interpretation –  What to use as a baseline ? i.e. define the Ratio •  Variations in probe coverage: –  Gaps –  Overlapping probes •  Inaccurate reference –  Reference build is inaccurate –  Probes cannot match the locus accurately •  Systematic error –  Autocorrelation with GC content –  Preparation, e.g. genome amplification JB Cazier, May 2013

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Overlapping probes in regions of CNP

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Probes in repeat elements

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SNPs in probes •  The special case of rodents: •  There can be many strain from limited number of founders –  Full sequencing has been limited –  The reference used for the probe generation can be far from the strain tested –  This will lead to failure across the genome

Gauguier et al, in preparation

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Systematic SNPs in probes •  There can be mosaicism –  Grouping of SNPs in specific regions •  Generates systematic drops in hybridization at specific loci •  Can be misinterpreted as deletion –  Be aware of the regions with SNPs •  And correct for the lack of hybridization

–  Design specific probes for the strain

Gauguier et al, in preparation

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Large CNV Surveys •  Two projects were run in parallel to identify and characterize CNVs in Human: –  The Genome Structural Variation Consortium (GSV) •  CNV discovery project to identify common CNVs using aCGH by Nimblegen, •  Detection in 20 CEU, 20 YRI, 1 reference •  Assayed in 450 HapMap samples

–  The Wellcome Trust Case Control Consortium (WTCCC) •  Test for association to diseases of CNVs in the WTCCC –  16,000 cases, WTCCC plus Breast cancer –  3,000 common controls

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The GSV study design

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The GSV study outcome Localization

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Function of CNVs

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The GSV study outcome (II) •  Designed an array with 42 million probes –  cover 11,700 CNV larger than 443 bp –  8,599 validated independently

•  Generate reference genotype for 4,978 on 450 samples •  Identified 30 loci with CNV candidate for influencing phenotype •  Striking effect of purifying selection –  Act on exonic and intronic deletions –  So functional variants should be rare

•  But most of common CNVs are already well tagged by the existing SNParray –  May need to look elsewhere to solve the missing heritability

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The WTCCC study •  Use the WTCCC cohort of 16,000 samples and 3,000 common controls. –  Bipolar, type 1 diabetes, type 2 diabetes, coronary artery disease, hypertension, rheumatoid arthritis, Crohn’s disease + Breast Cancer –  1,500 1958 Birth Cohort and 1,500 National Blood Donor

•  Designed a specific array using GSV set, McCarroll,1M and WTCCC1 –  104,000 probes targeting 12,000 putatitve loci

•  Perform assay using the Agilent platform by Oxford Gene Technology (OGT) against a common pooled reference sample •  Attempt to design a robust pipeline to call all CNV across the different studies –  Use CNVtools by Plagnol and local by Cardin (“Chiamesque”)

http://www.wtccc.org.uk/ccc1/plus_typing_array.shtml JB Cazier, May 2013

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The WTCCC results •  3,900 CNV identified •  3,100 validated after QC

•  Concordance of 99.8% on known 420 duplicates •  Remaining 8,000 CNVs from original selection: –  False positive in discovery –  Too noisy, but genuine –  Genuine but very rare

•  19 CNVs taken forward to replication with Bayes Factor: ~10-4 p-value –  14 failed to replicate either using tagged SNPs or direct typing –  5 associations

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The WTCCC conclusions •  Each CNV behaves uniquely •  Size, genomic location, biological sample type, sample preparation

–  Designed 16 different pipelines •  Key paramaters: –  – 

Normalization Integration of the 10 probes

•  Impossible to define one-pipe-fits all

–  Show importance to have duplicates and large amount of diverse data •  Confirmed the overrepresentation of CNVs in intronic regions •  Confirm the high level of tag with SNP 6.0 or HapMap2 –  MAF > 10% : 75% tagged at r2>0.8 –  MAF 0.8

•  Found few new CNV associated with phenotype JB Cazier, May 2013

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Conclusions of these studies •  Both identified many CNV in the human genome •  Characterization of CNV is very difficult, and not easily stream lined –  Careful interpretation of association results –  Some artifacts will survive confirmation •  Many CNVs co-localize with variants identified by GWAS –  Good functional candidate •  But, most of the common CNVs are already well tagged with SNPs –  This will not bring new common variant in common disease •  i.e. these will not solve the mystery of missing heritability.

•  Still rare CNVs can be associated to diseases, but just as much as SNPs JB Cazier, May 2013

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Success stories •  Autism Pinto D et al. Nature. 2010

•  Obesity

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Autism

Functional impact of global rare copy number variation in autism spectrum disorders. Pinto D, et al. Nature. 2010 Jul 15;466(7304):368-72. JB Cazier, May 2013 DTC Bioinformatics

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Obesity a) 

Affymetrix 6.0 array data for five patients with deletions at 16p11.2 is shown. Log2 ratios of the five samples are highlighted in dark red, with other samples in the same genotyping plate shown in grey. The structure of extensive segmental duplication that extends to the flanking regions is shown.

b) 

Three probands in whom the 16p11.2 SH2B1containing deletion co-segregates with severe earlyonset obesity alone.

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Two probands harbouring larger de novo 16p11.2 deletions that also encompass a known autismassociated locus and are associated with developmental delay and severe early-onset obesity.

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MLPA probes for genes in the region of interest are shown. The MLPA target regions labelled as C are control probes located either on chromosome 16 but outside the deleted region or on other chromosomes. Patient MLPA traces are in red, overlaid upon the normal control MLPA traces in black. Arrows point to the deleted probes.

Large, rare chromosomal deletions associated with severe early-onset obesity. Bochukova EG, et al Nature. 2010 Feb 4;463(7281):666-70 JB Cazier, May 2013

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Success stories a) 

aCGH data showing the location of the 16p11.2 deletion. The data show the log2 intensity ratio for a deletion carrier compared to an undeleted control sample. Grey bars connected by a broken line denote the segmental duplication flanking the deletion region. Vertical bars indicate the positions of the probe pairs used for MLPA validation. Note that CGH and genotyping array probes targeted against segmental duplications may not accurately report copy number due to the increased number of homologous sequences in the diploid state. Genome coordinates are according to the hg18 build of the reference genome.

b) 

MLPA validation of 16p11.2 deletions. Representative MLPA results are shown, illustrating one instance of maternal transmission and two instances of de novo deletions. Genotyping data excluded the possibility of nonpaternity. Each panel shows the relative magnitude of the normalised, integrated signal at each probe location, in order of chromosomal position of the MLPA probe pairs as indicated in (a). Each panel corresponds to its respective position on the associated pedigree, as shown.

A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Walters RG et al Nature. 2010 Feb 4;463(7281):671-5. JB Cazier, May 2013

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What more with CNV then ? •  Copy Number Variations are key in Cancer •  Cancers are typical of somatic variations –  They are therefore mostly unique –  Cannot be tagged –  Relatively common event –  Although still difficult to identify it is essential

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Cancer Schematic illustration of chromosomal evolution in human solid tumor progression. The stages of progression are arranged with the earlier lesions at the top. Cells may begin to proliferate excessively owing to loss of tissue architecture, abrogation of checkpoints and other factors. In general, relatively few aberrations occur before the development of in situ cancer. A sharp increase in genome complexity (the number of independent chromosomal aberrations) in many (but not all) tumors coincides with the development of in situ disease. The types and range in aberration number varies markedly between tumors, HCT116, a mismatch repair–defective cell line T47D, a mismatch repair–proficient cell line64.

Chromosome aberrations in solid tumors Donna G et al. Nature Genetics 34, 369 - 376 (2003)

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Germline vs. Somatic • 

Germline variants –  The aberration exists from the start, and is inherited –  Such variants are more likely to be common Copy Number Polymorphisms, predisposing variants. –  Approach similar to non-cancer studies

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Somatic events –  Aberrations happen during the life-time –  Happen more than once –  Heterogeneous events; => Each cancer is unique –  In Tumours, recurrent aberrations are more likely to be linked to the cancer as a selective advantage ⇒  We want to identify the regions with recurrent events

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4n

4n

4n 4n 4n 6n

5n

3n 6n 5n 4n 4n

3n

3n (2n?) 3n 4n 4n 4n

5n 8n JB Cazier, May 2013

8n DTC Bioinformatics 4n 2 x n 2 x n47

More issues •  Interpretation –  What to use as a baseline ? i.e. define the Ratio •  Within sample baseline of 2 is not an easy assumption anymore •  Heterogeneity of tissue –  Biopsy can be “contaminated” by normal tissue –  Cancer are usually made up of a set of co-existing clones •  CNVs are unique –  Each one has its own breakpoints •  Systematic error –  Preparation, e.g. genome amplification –  Sample quality JB Cazier, May 2013

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Copy Number Variations in Cancer • 

It is possible to analyse tumour samples using classic Copy Number tools, but the results are likely to be unsatisfactory as many model assumptions are violated: –  The normalisation of SNP genotyping data can be affected by tumour samples containing large scale chromosomal alterations. –  Most aberrations do not follow the classic diploidy and cannot fit usual clusters –  So Genotype Calls might be forced on the wrong model AA/AB/BB: • 

Deletions should be 0 or A / B,

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Copy Neutral LOH should be AA/BB

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Triploid should be AAA/AAB/ABB/BBB

–  There can be intra-tumour heterogeneity • 

E.g. Mix of triploid and tetraploid

–  There can be contamination with normal cells (stromal contamination)

Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Korn et al. Nat Genet. 2008 Oct;40(10):1253-60

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A deletion found in tumour AML sample at 8p using unpaired analysis.

Tumour sample vs Baseline 4 2 1

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Same deletion found in corresponding diagnostic AML sample at 8p

Tumour sample vs Baseline 4 2 1

Normal sample vs Baseline 4 2 1

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Need for pairing

4 2 1

Tumour sample vs Baseline

Normal sample vs Baseline 4 2 1

4 2 1

Tumour sample vs Normal sample

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Normal-Tumor Pairs

Removed the outlier, colored by type, paired JB Cazier, May 2013

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Heterogeneity • 

Proportion of Cells, “c,” in a heterogeneous tumour sample harboring a Somatic genetic event

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BAF and the logR ratio plots from one chromosome reveal three somatic hemizygous deletions occurring in three different proportions of cells.

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Frequency distribution showing the number of SNPs included in the somatic deletions by the proportion of cells, “c,” in which these events occur. Some somatic deletions occur in over 80% of cells. Assuming that only cancer cells harbor somatic deletions, the proportion of cancer cells is then estimated as 80% in this sample.

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Schematic illustrating the relationship between the chronology of somatic events during tumorigenesis and the proportion of cancer cells with these events. Early somatic events are present in all (or a great majority of) cancer cells, whereas late somatic events are only present in subsets of cells.

SNP arrays in heterogeneous tissue: highly accurate collection of both germline and somatic genetic information from unpaired single tumor samples. Assié et al Am J Hum Genet. 2008 Apr;82(4):903-15

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Mixing proportion identification •  Estimating copy number and mixing proportions from simulated data using OncoSNP. •  The estimated copy number states and mixing proportions (grey) are comparable to the true values used for the simulations (black). •  In the two regions of copy number 3 that are incorrectly classified as copy number 4, an examination of the Bayes Factor shows that although the data favors the 4n amplification state, there is also strong support for both the true state (3n amplification).

Identification of DNA copy number changes and loss-ofheterozygosity events in heterogeneous tumor samples: a Bayesian Mixtures of Genotypes approach on SNP array data Yau C et al In preparation

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Normal-Tumour Titration • 

intra-tumor heterogeneity (red)

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stromal contamination only (black)

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Both models infer the level of normal DNA contamination with good accuracy up to 50% contamination At higher contamination levels, the stromal contamination only model has superior performance as it is able to borrow strength from all SNPs to infer the contamination level. This provides more power to detect duplications at high contamination levels than the intra-tumor heterogeneity model.

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Identification of DNA copy number changes and loss-ofheterozygosity events in heterogeneous tumor samples: a Bayesian Mixtures of Genotypes approach on SNP array data Yau C et al. In preparation

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Detection of alterations Detecting chromosomal alterations in cancer cell line and tumor samples. The intra-tumor heterogeneity model (red) indicates that approximately 50% of cell contain a different breakpoint location to the others whereas this feature is missed entirely by the stromal contamination only model (black) The near-triploid status of the cell line HT29 is correctly identified and copy number estimates are correctly derived even though the Log R Ratios are centered on zero for the copy number 3 state. The two heterogeneous deletions are separated by an unaltered region, however, there is still good agreement between the mixing proportion estimates given by the intra-tumor heterogeneity and stromal-only models. This suggests we do not pay too severely when assuming independent mixing proportions in the intra-tumor heterogeneity model.

Identification of DNA copy number changes and loss-ofheterozygosity events in heterogeneous tumor samples: a Bayesian Mixtures of Genotypes approach on SNP array data Yau C et al. In preparation

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Recurrent events Overview of all genetic aberrations found with SNP array in 45 adult and adolescent ALL cases. Minimally involved regions are shown to the right of each chromosome. For each type of aberration, each line represents a different case. –  –  –  – 

Blue lines are regions of uniparental disomy, light green lines are hemizygous deletions, dark green lines are homozygous deletions, red lines are copy-number gains.

Note the high frequency of deletions involving chromosomes 9p21.3, 9p13.2, 7p12.2, 12p13.2, and 13q14.2 corresponding to the CDKN2A, PAX5, IKZF1, ETV6, and RB1 loci, respectively. http://www.well.ox.ac.uk/GREVE/ GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples. Cazier J-B, Holmes C, Broxholme J.(2012), Bioinformatics Microdeletions are a general feature of adult and adolescent acute lymphoblastic leukemia: Unexpected similarities with pediatric disease. Paulsson K et al, Proc Natl Acad Sci U S A. 2008 May 6;105(18):6708-13

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Overlap of recurrences •  Aberrations observed on chromosomes 11 and 13 are shown with their bands, a subset of potential target genes in AML and regions of –  gain (red), –  loss (green) –  aUPD (blue). • 

The scale at the bottom shows the length of each chromosome in megabases (Mb). The color gradient above each kind of aberration summarizes the data for that aberration.

•  Beware that GC content can induce systematic falsely identified aberrations Novel regions of acquired uniparental disomy discovered in acute myeloid leukemia. Gupta et al. Genes Chromosomes Cancer. 2008 Sep;47(9):729-39.

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Progression •  CLL

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Typical workflow • 

Normalisation –  GC Content Correction –  Paired –  Unpaired with appropriate baseline

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Determination of Aberrations –  Correct Genotype –  Copy Number

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Identification of recurrent locations Test against germline sample if possible –  Could it be an at-risk variant ?

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Test against known variations Validation –  Identify precisely breakpoints • 

Sequencing

–  Identify the frequency –  Identify the Associated risk –  Perform functional analysis JB Cazier, May 2013

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High Throughput sequencing •  More data –  Better resolution ?

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Array tools extension ? •  Direct application of SNParray tool to HTS “fails” –  Too much noise: •  Very variable Coverage at given location •  No clear BAF defined

•  Develop specific tools on same concept –  e.g. OncoSeq

•  There is more than coverage info in Sequence data JB Cazier, May 2013

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Specifics •  Issue with CNV and Exome –  Difficult to set up a baseline when there is sporadic coverage –  Exome sequencing is not recommended for CNV, or cancer analysis

•  Single-end vs Paired-end –  Breakpoints are more likely covered by the non-sequenced inserts than the reads

•  Cancer samples do not match much of the References, –  –  –  – 

Copy Number Variations Complex rearrangement Large number of mutations Heterogeneity of clones at given “location”

•  Very large level of False Positive with current methods –  Difficult to have a Gold Standard JB Cazier, May 2013

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Methods •  Most NGS-based CNV detection algorithms rely on mapping sequence reads back to a reference genome in search of discrepancies that may provide evidence for different types of variants. •  CNV signatures of all classes (deletions, insertions and duplications) can be obtained from the different features of a single NGS experiment, with varying degrees of sensitivity: –  Read Depth (RD): •  Better suited for determining the absolute copy number •  Often suffer from low breakpoint resolution and lower sensitivity for small variants (