Supplementary data

Spatio-temporal heterogeneity characterize the genetic landscape of

pheochromocytoma and defines early events in tumorigenesis.

Crona et al.

Patients and samples........................................................................................................... 3Tissue collection............................................................................................................................ 3Sample preparation and nucleic acid extraction...................................................................3

Sample Sequencing............................................................................................................. 4Targeted Deep Sequencing......................................................................................................... 4PCR amplification. fragment separation and Sanger Sequencing....................................5High throughput sequencing of selected PCR amplicons....................................................5

SNP array genotyping and raw data analysis...............................................................7

Pipeline for resolving absolute ploidy and purity in Pheochromocytoma...............7Determination of LogR intensities and B allele frequencies................................................7Copy number classification........................................................................................................ 8Determination of clonal proportions........................................................................................9Inferring allele specific copy number status by establishing relationships between logR and absolute ploidy............................................................................................................. 9

Results................................................................................................................................. 11SNP array Genotyping..............................................................................................................15Results and validation of absolute ploidy and clonal fractions........................................15Estimation of sample tumour cellularity...............................................................................17Comparison of robustness of the determination of variant frequencies........................19

References.......................................................................................................................... 23

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Supplementary Figure 1

Overview of the workflow executed in the current study.

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Patients and samples

Tissue collectionAll tumours samples were obtained from surgical specimens that had been collected

between 1988-2013 at the Unit of Endocrine Surgery, Uppsala Academic Hospital,

Uppsala, Sweden. Inclusion criteria were diagnosis of pheochromocytoma or

paraganglioma confirmed by routine histopathological investigation. Tumour

specimens were dissected directly upon resection and snap frozen in liquid nitrogen.

Tissue samples were then stored in low temperature freezers that maintained an

average temperature of less than -69°C. Peripheral blood was collected at the time of

patient admission for surgery and stored below -20°C.

Sample preparation and nucleic acid extraction Tumour cell content was quantified by light microscopy that was performed by two

independent observers. Selected samples were macro-dissected in order to reduce the

proportion of non-tumour cells. RNA and Genomic DNA were extracted using

DNeasy Blood & Tissue and/or AllPrep DNA/RNA kits (Cat. No. 69506 and 80204,

Qiagen, Hilden, Germany) as instructed by the manufacturer. Reverse transcription

was performed using RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific,

Waltham, MA, USA). Nucleic acid quality and concentrations were assessed using

Nanodrop spectrophotometer (ThermoFischer Scientific, MA, USA).

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Sample Sequencing

Targeted Deep SequencingGenomic DNA from 131 PPGL samples were analysed with targeted next generation

sequencing. For this purpose a a Truseq Custom Amplicon (Illumina Inc. CA. USA)

targeted enrichment kit was designed to cover the protein coding sequences of the

SDHA. SDHB. SDHC. SDHD. SDHAF2. VHL. EPAS1. RET (Exons 8. 10-11 and 13-

16). NF1. TMEM127. MAX and H-RAS (Exons 2 and 3) genes. Genomic enrichment

and library preparation experiments were performed by the SciLifeLab core facility

(http://molmed.medsci.uu.se/SNP+SEQ+Technology+Platform/) as instructed by the

manufacturer (Part# 15027983). The generated 175bp paired end libraries were

indexed. pooled and sequenced twice on a MiSEQ instrument (Illumina Inc) as

instructed by the manufacturer (Part# 15027617). Read mapping to the human

reference sequence HG19 and subsequent variant calling were performed using

MiSEQ Reporter v2.1.43 (Illumina Inc) using default settings. Briefly reads were

mapped using a Smith-Waterman algorithm (reference sequence HG19) and variant

called using the Genome Analysis Toolkit (GATK). Results from generated .bam

and .vcf files were further analysed and visualized using CLC Genomics Workbench

5.51 (CLCbio. Aarhus. Denmark). Variant were processed by (1); excluding variants

not available in both sequencing runs. (2); excluding synonymous variants without a

probable splice site effect. (3); annotation for overlapping information in; Single

Nucleotide Polymorphism database (dbSNP) build 137. Catalogue of Somatic

Mutations in Cancer (COSMIC) (1). database of Human Gene Mutation Data

(HGMD) (2) and Leiden Open (source) Variant Database (LOVD). The impact of

non-synonymous amino acid substitution was further assessed in silico using

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Polymorphism Phenotyping v2 (Polyphen2) (3) and Sorting Intolerant from Tolerant

(SIFT) (4). Variants were classified as Benign. Pathogenic or Unknown (Variant of

Unknown Significance. VUS). Variant frequencies were determined from .bam files

generated by MiSEQ Reporter v2.1.43 using the CLC Genomics Workbench 7.5

quality based variant calling algorithm (lower variant threshold 5%. default settings).

The mean of the variant frequencies generated in sequencing runs 1 and 2 were

calculated and used for further analysis.

Variants predicted as pathogenic were validated with sanger sequencing using

genomic DNA and cDNA when appropriate. Primer sequences can be obtained by

request.

PCR amplification. fragment separation and Sanger Sequencing. Variants located outside the targeted regions could cause truncation of the NF1

protein through aberrant RNA splicing (5) and selecting tumours with LOH

overlapping the NF1 locus have been shown to enrich for NF1 mutated tumours (5,

6).

Primer sequences of NF1 cDNA (NM_001042492.2) were obtained from Welander et

al. (5). Amplified PCR products were sequenced using automated Sanger sequencing

(Beckman Coulter genomics. Takeley. UK). PCR amplified cDNA from tumours and

three normal controls were separated on 2.5% agarose gel. Those in which alternative

lengths of amplicons could not be excluded samples were selected for sequencing.

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High throughput sequencing of selected PCR amplicons. Genomic DNA from the tumours of two patients were selected for additional

validation; patient 8 with a 30bp deletion that was not detected using TSCA libraries

and Patient 29 with low allele frequency. Both variants occurred in exon 1 of the VHL

gene and were amplified by PCR using custom primer sequences designed in house

using NCBI primer. Nextera (Illumina inc) paired end sequencing libraries were

prepared from 1 ng of amplified dsDNA using an enzymatic fragmentation approach.

Briefly amplified DNA was fragmented and tagmented by transposomes. A limited-

cycle PCR reaction amplified the insert DNA and added index sequences. The

libraries were purified using Ampure XP beads (Beckman coulter genomics. CA.

USA). normalized and pooled into a single suspension. The sample pool was

sequenced on a single sequencing run on the MiSEQ (Illumina Inc) instrument.

Library preparation and sequencing were performed by SciLifeLab core facility

(Stockholm node). Read mapping and variant calling were performed using CLC

Genomics Workbench 5.5 mapping algorithm and quality based variants caller with

default settings.

Semi-Quantitative PCR

Experiments were performed in triplicates using SsoAdvanced SYBR Green

Supermix (Bio-Rad laboratories, Hercules, CA, USA) on a CFX96 Real Time system

(Bio-Rad laboratories). Expression of VEGFA was analysed and normalized to

HPRT1 expression. ΔΔCT values were calculated for further analyses.

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SNP array genotyping and raw data analysis. Genomic DNA from a total of 138 tumour samples were analysed with Illumina

infinium dual flourencence Single Nucleotide Polymorphism arrays

HumanOmniExpressExome-8v1_A. Omni1-Quad or Omni2.5-Quad (Illumina. CA.

USA) containing 730’525. 1.140.419 and 2.379.855 probes respectively. DNA

amplification. tagging and hybridization were performed according to the

manufacturer’s instructions and scanned on a HiScanSQ instrument (Illumina). The

experiments were carried out at Science for Life Laboratory core facilities.

SNP&SEQ Technology Platform in Uppsala. Sweden

(http://molmed.medsci.uu.se/SNP+SEQ+Technology+Platform/) according to the

manufacturer’s guidelines. Generated fluorescent raw signals were imported into

Illumina BeadStudio (version 7.5 build 2011) software and normalized against

reference data (ICF) provided by the manufacturer. Nexus copy number beadstudio

plugin was used to export total relative fluorescent intensities (logR) as well as the

relative intensity (BAF) for each individual probe.

Pipeline for resolving absolute ploidy and purity in PheochromocytomaWe utilized a workflow consisting of multiple commercially and publicly available

bioinformatics suites and algorithms as outlined schematically in figure S1.

Determination of LogR intensities and B allele frequenciesFurther analysis of SNP array results were performed using Nexus 7.5 (Biodiscovery.

CA. USA). Briefly data was normalized for GC waves by array specific quantile

systematic correction algorithms that were provided by the manufacturer. A circular

binary segmentation algorithm SNP-RANK was used to call regions with copy

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number events and/or allelic imbalances (significance threshold 10-13). Samples with

Nexus QC score >0.13 were excluded. Marginal events (defined as <100 probes) were

excluded from further analysis in order to reduce false predictions. Generated

segments were reviewed by two independent observers and consensus was reached by

integrating LogR and BAF. BAF values <0.5 were transposed (to 1-BAF) and

homozygous SNP calls excluded. The median value was calculated from both the

upper and lower band. Segment coordinates and the corresponding median logR and

BAF (mean of the two bands) were then exported for further analysis.

Copy number classificationIn order to determine the underlying ploidy and clonal fraction of specific segments

we used a two-step approach. First all segments were analysed using a maximisation

of parsimony model. In regions where multiple different combinations of clonal

fractions and ploidy may explain the observed data the model assumes that the

solution with the smallest number of deviations from diploid have the highest

probability of being correct. Regions with no relative change in LogR and with BAF

0.5 were considered heterozygous copy number neutral (ploidy AB). Regions with

aberrant LogR/BAF calls were assigned classifications: relative decrease in logR and

BAF >0.54 (technical detection limit) were considered a heterozygous loss (ploidy

B/A) whereas a relative increase in logR and BAF >0.54 were considered as a copy

number gain (ploidy AAB/BBA). Segmentations showing decrease in logR with BAF

0.54 were considered a homozygous loss (ploidy 0). Increase in logR and BAF <0.54

were considered a balanced copy number gain (ploidy AABB). Segments with logR

of 0 and BAF >0.54 were considered as copy number neutral loss of heterozygosity

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(ploidy AA/BB). Ploidy classifiers A. B. AB. AAB. BAA. AABB will be denoted as

simple calls in the remains of this manuscript.

Determination of clonal proportionsPloidy and BAF values were used to calculate the proportion of cells (p) harbouring

the particular SCNA (7). Formula (1) was derived where (a) denotes ploidy of allele

A and (b) denotes ploidy of allele B.

(1) p= 1−2 BAF( BAF−1 )∗b+BAF∗a−2 BAF+1

Segments showing p=1 were considered germline events and were considered to be

outside of the scope of this manuscript and removed from the subsequent analyses.(8)

Inferring allele specific copy number status by establishing relationships between logR and absolute ploidyWe hypothesized that a minority of the generated segments could harbour complex

combinations of multiple different chromosomal aberrations with different clonal

fractions. In order to identify such segments we sought to compare each segment to an

empirical dataset. LogR and copy number have been previously described to have a

distinct relationship (9). We ruled that our dataset of segments would have the

robustness to generate a similar relationship. This was based on (I) the expected

robustness of the maximization of the theory of parsimony approach (especially as

majority of our samples had limited number of SCNA) and (II) the validation of our

results from copy number estimation provided by the existing literature.

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Absolute copy number for simple calls were calculated using ploidy and clonal

fraction in accordance with Formulae (2). CN represents the total copy number, p the

fraction of aberrant cells and Pt the ploidity of the segment.

(2) CN=2 (1−p )+ p∗Pt

For all individual samples LogR (X-axis) and copy number (Y-axis) were then plotted

to establish sample specific standard curves. Segments plotted as outliers on the

standard curve were excluded and the remaining values included for logarithmic

regression that was used to generate functions of the form shown in Formulae (3)

describing the relationship between Log R and total copy number. Similar array

specific curves were also produced using all the segments generated on a specific

array.

(3) LogR=A∗ln (CN )−B

For each sample the function from standard curve was used to calculate total copy

number from LogR for all segments. Allele specific copy number status of each

segment could then be estimated through multiplying the calculated total copy

number with the corresponding BAF in accordance with Formulae (4) and (5).

(4) CN A=(1−BAF )∗CN

(5) CN B=BAF∗CN

Allele specific copy number was then used to calculate the fraction of cells in the

sample carrying each aberration (clonal fraction) using Formulae (1.) For each

sample. the tumor purity was estimated to be equal to the highest clonal fraction of the

segments in the sample. Segments with clonal fraction within 10 percentage points of

the highest clonal fraction in a given sample were classified to be fully clonal while

segments with lower clonal fractions were considered to carry subclonal events.

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Results

Table S1. Sequence coverage of targeted NGS

MiSEQ batch

MiSEQ run

Coverage, mean number of

reads

Percentage of targets at X-fold coverage

1X 5X 10X 40X 80X

1 1 201.94 98.1 97.7 97.1 91.3 82.51 2 197.3 98.1 97.6 97.2 90.8 80.12 1 280.3 98.5 98.1 97.3 92.1 84.72 2 284.4 98.5 97.9 97.3 92.1 84.7

Supplementary Table 1Mean Coverage and percentage of targets with X-fold coverage from MiSEQ runs 1 and 2.

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Supplementary Figure 2Summary of the utilized sequencing workflow. All but one mutation previously

detected by Sanger sequencing could be confirmed. There were no additional variants

discovered neither in sequencing rounds number 2 or 3. Analysing the 30bp deletion

not detected by targeted NGS showed that this particular variant was located at the

overlap of two amplicons (data not shown) and could be validated using customized

enrichment. NEXTERA libraries and MiSEQ sequencing. In conclusion all variants

could be validated using two principally different sequencing chemistries.

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Supplementary Figures 3A-B

Sequencing reads and chromatograms from tumour and normal tissue showing (A)

validation of H-RAS, RET and VHL mutations. Figure 3B presents data from patient

29 that had previously been determined as wild type in the VHL locus by Sanger

Sequencing analysis. Targeted NGS detected a pathogenic VHL mutation c.[193T>G].

Multiple samples from geographically different regions from the same primary

tumour was analysed using Sanger sequencing revealing a secondary peak

corresponding to the mutated G. The mutation was validated to have a 14.6%

(sequence depth 58746) allele frequency using Nextera libraries sequenced on a

Miseq instrument.

Supplementary Figure 4Validation of NF1 mutations that was determined to cause a splice site disruption by

in silico analysis. cDNA from tumour tissue were amplified by PCR and separated on

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agarose gel that revealed aberrant fragments lengths for all three cases. Results were

validated by Sanger Sequencing that showed effect on protein structure: c.288+1G>T,

p.Arg69_Gly96del, c2252-2A>G, p.Gly751fs and c.4836-2delA, p.1613_1871del.

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SNP array GenotypingGenotyping was performed with a success rate (defined as a genotyping call rate of

>98% for individual SNPs) of 95%. Median quality score determined by Nexus Copy

number was 0.029 (range 0.1-0.015). A discrete asymmetry between the two

fluorescent dyes was observed. Increasing difference between the two bands

proportional to the distance from 0.5 were noted with the highest differences observed

when the bands were in proximity of 1 and 0. Normalization with established

bioinformatics scripts did not compensate for this asymmetry (10). The performance

of the workflow was tested using BAF derived from either the upper and lower bands

or from each individual single band only. BAF calculations from both bands were

considered to have the most robust performance and were selected for further

analysis.

Results and validation of absolute ploidy and clonal fractionsA total of 1191 segments were generated. 1064 affected one allele and 127 were bi

allelic. Absolute ploidy and purity of two samples could not be resolved even after

considering the interpretations generated by ASCAT and TAPS hence both samples

were excluded from further analysis. Sample and array specific curves were created

from the remaining samples and logarithmic regression was applied to generate

quantitative relationships between LogR and absolute copy number. A small number

segments were outliers and were classified to have unknown copy number state. The

remaining segments were plotted and the extracted function of the standard curve

were in line with that of previous studies (9).

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Supplementary Figure 5. Standard curve showing relationship between copy number

and Log R ratio generated by all segments included samples and arrays. Middle line

equals mean value. Coloured area denotes one standard error.

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Estimation of sample tumour cellularityCorrelation between the estimations of sample purity was found between study

workflow and ASCAT (figure S6. Pearson correlations test R=0.830). Compared to

ASCAT (considered as golden standard) the in house pipeline tended to overestimate

sample purity. Troubleshooting revealed that by analysing BAF derived from the

lower band only the study workflow showed a stronger correlation to ASCAT.

However as this increased the intra sample divergence of clonal fractions we decided

to use BAF derived from both bands. Histopathological investigation showed low

sensitivity to detect samples with low purity as determined from SNP array data

(Figure S7).

Supplementary Figure 6Correlation of study observations and ASCAT analysis in estimating the total fraction

of tumour cells within each individual tumour sample. ASCAT could not determine

the tumour purity for two samples. Pearson correlation test R=0.830.

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Supplementary Figure 7

Correlation of results from histopathological investigation and ASCAT in estimating

the total fraction of tumour cells. Pearson correlation test R=0.658.

Supplementary Figure 8

Correlation between TAPS and the study workflow in the estimation of clonal

fractions for specific segments. Data from sixteen samples were selected because of

high levels of aneuploidy and/or subclonal fractions in order to validate the study

workflow.

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Comparison of robustness of the determination of variant frequenciesOnly samples in which both SNP array and deep sequencing analysis had been

performed on the identical batch of genomic DNA were considered for integrative

analysis. In order to determine the overlap between the two methods we selected

variants present in dbSNP and plotted the corresponding variant frequency (VAF)

generated from deep sequencing to the median transposed BAF observed at the

corresponding location by SNP array. Pearson correlation test confirmed satisfactory

overlap between the two methods (Figure S9A. Pearson correlation R=0.945).

Relative differences between the two methods were plotted against deep sequencing

read coverage. As expected this confirmed that variants with high read coverage

showed less variability than its counterparts (Figure S9B).

Supplementary Figure 9A and B. (A) correlation of allele frequencies of germline

SNPs between SNP array and deeps sequencing methods. (B) relative difference

between SNP array and deep sequencing variant frequencies dependent on deep

sequencing read coverage.

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Determining clonal architecture from integrated data

Allele frequencies of a total of 58 pathogenic mutations were included for further

analysis. Ploidy and purity from overlapping SNP array segments were utilized to

calculate the expected variant frequency of somatic alleles in a scenario were the

pathogenic allele would be present in 100% of cells that harbour the specific

structural variation. The following formula was derived (2).

(2) f =p∗a/ (p∗(a+b)+(1−p)∗2)

(f) denotes the expected variant frequency. (p) proportion of cells harbouring

structural variation. (a) ploidy of allele A (pathogenic allele). (b) ploidy of allele B.

Numbers denotes ploidy calls of alleles A and B.

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Supplementary table 2. Statistical correlations

Mutation subgroup §

Germline or somatic

mutation §§Malignant §§

Laterality (right vs left) §§

Gender, §§

Size (≤60mm/>6

0mm) §§

Age (≤50y/>50

y) §§PCC vs PGL §

p=Tumour Cellularity Study observation 0.003 0.023 0.313 0.444 0.545 0.018 0.154 0.304 ASCAT <0.001 0.042 0.856 0.168 0.782 0.048 0.197 0.878Histopathology 0.006 0.031 0.019 0.301 0.738 0.173 0.546 0.877SCNA events, n 0.023 0.35 0.393 0.121 0.053 0.004 0.023 0.068SCNA fraction of the genome, % 0.415 0.415 0.844 0.907 0.031 0.360 0.072 0.062

Fraction of changes with subclonal status, %

0.923 0.075 0.957 0.343 0.297 0.081 0.107 0.916

Supplementary table 2. Patient statistics calculated from primary tumours

Table shows statistical calculations of mutation subgroup, malignant status and PCC or PGL type to genomic parameters. Tumour cellularity

analysed with the workflow of the current study and ASCAT workflows as well as that of histopathological examination was included. SCNA;

Somatic Copy Number Aberrations, PCC; Pheochromocytoma, PGL; Paraganglioma. §; Kruskal-Wallis test, §§ Mann-Whitney U test.

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Supplementary table 3. Statistics on a tumour lesions basisMutation

subgroup§ Malignant§§ PCC vs PGL§§

PTumour cellularity Study observation <0.001 0.198 0.146 ASCAT <0.001 0.234 0.705 Histopathology 0.002 0.142 0.609SCNA events, n <0.001 <0.001 0.036SCNA fraction of the genome 0.053 <0.001 0.892Fraction of changes with subclonal status 0.114 0.174 0.059

Supplementary Table 3. Statistics on a tumour lesion bases

Table shows statistical calculations of mutation subtype, malignant status and PCC or

PGL type to genomic parameters. Eighty different tumour samples were classified

accordingly to mutation subgroup into; 9 HRAS, 29 NF1, 20 RET and 22 VHL. There

were 35 samples from tumours classified as malignant and 99 from those classified as

non-malignant. One hundred and twenty three samples were derived from PCC and

12 from PGL. Tumour cellularity analysed with study workflow and ASCAT

workflows as well as that of histopathological examination was included. SCNA;

Somatic Copy Number Aberrations. PCC; Pheochromocytoma. PGL; Paraganglioma.

§; Kruskal Wallis Test. §§ Mann-Whitney U test.

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