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Supplementary webappendix

This webappendix formed part of the original submission and has been peer reviewed.

We post it as supplied by the authors.

Supplement to: Not Knudson’s Retinoblastoma: One Hit Cancer Initiated by the MYCN

Oncogene?

Rushlow, D. E., Kennett, J. Y., Mol, B. M. et al. Lancet, submitted.

Webappendix for MYCN ONCOGENE-INITIATED RETINOBLASTOMA

Supplementary Materials

Tissue Samples

Tumors, blood and clinical data were provided for genetic diagnosis of RB1 mutations for clinical

care. Research Ethics Boards (REBs) approvals for the use of de-identified data and tissues, after

clinical analysis was completed, are on file at each participating site. Dr. R. Bremner (Toronto

Western Hospital Research Institute, University Health Network) provided fetal retina mRNA

samples with approval of the UHN REB.

Mutation Analyses

Mutation analysis for RB1 (Gen bank accession # L11910) included DNA sequencing, QM-PCR or

MLPA (MRC-Holland), and RB1 promoter methylation analysis.1

Quantitative Multiplex PCR (QM-PCR)

Copy numbers for KIF14, DEK, E2F3, CDH11, and MYCN were determined with gene-specific

primers2, and additional gene-specific confirmatory primer sets (Table S1), using the Qiagen

Multiplex PCR kit and 19 amplification cycles. Each assay included internal controls from 15q and

12q. We established cut-off values for loss and gain, as illustrated below, using boxplots.

Gene/Exon Primers Primer Sequence Expected Size (bp)

Assay Location

MYCN2-exon2MYC 5x2q cga cgt ggt cac tgt gga g

3164973

MYC 3x2q acg ctc act gtc ctc cga g 5288

KIF KIF 5x24-Cy5 gac aaa tca agc act att tac tc224

55072KIF 3x24 gct tac agt tat ctt gac ttt c 55295

DEK DEK 5x7-Cy5 tta tgc agc cat tgc cga aat c278

14721DEK 3x7 ttc tgg ctg tac ctt ata gca g 14998

E2F3 E2F3 5x7-Cy5 cag act tgg ctt caa cca ac253

88261E2F3 3x7 aag tct tcc acc agt ggg ag 88513

ACVRL1 exon 8 Ex8seq3 cct gtg tag ggt ggg ccc t303ALK5-E8 tgt ttc tct cag tcc cca cc

ACVRL1 exon 5 Ex5seq3 tcc acc ggg ctc acc cac tAL5-E5 tcc cag gtc gag gat aga ga 198

C4 chromosome 15 CY329-5' gcc cct cac ccg cac cta agt329329-3' cca gga tga gag cgg atg gca

Table S1. QM-PCR control primers & additional QM-PCR primers for MYCN, KIF14, DEK and E2F3. The initial primers in use were those cited in Bowles et al. 20072. This table shows additional primer sets used for confirmations.

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Figure S1. Separation between normal and abnormal QM-PCR control results determined

gain and loss cut-off values. On each boxplot, the vertical line marks the maximum and minimum

copy-numbers observed. The box bounds the second and third quartiles (50% of the data points);

horizontal line within the box represents the median. Gain defined as ≥2.50 copies for KIF14, ≥

2.60 for DEK, E2F3 and MYCN, ≤1.60 for CDH11 loss. A second MYCN primer set (MYCN2) was

used to confirm borderline and increased MYCN copy number values in the tumor samples.

3


Multiplex Ligation-Dependent Probe Amplification (MLPA)

MLPA was performed with the SALSA MLPA kit P252-B1 Neuroblastoma (MRC-Holland, The

Netherlands) according to manufacturer’s instructions. Fragment analysis was performed with

GeneMarker software (Softgenetics, USA), DNA copy number ratios of tumor samples were

computed using the matched blood sample or a pool of reference samples. Threshold for gains was

a ratio of ≥ 1.3, < 5 and for high-level amplification, ≥ 5. Absolute copy number was subsequently

calculated by averaging the ratios from different probes for the same gene and multiplying the ratio

by 2.

Single Nucleotide Polymorphism Studies (SNP)

SNP array experiments were performed at ServiceXS (ServiceXS B.V., Leiden, The Netherlands)

using the HumanOmni1-Quad BeadChip (Illumina, Inc., San Diego, CA, U.S.A), according to the

manufacturer's instructions. The BeadChip images were scanned on the iScan system and the data

was extracted into Illumina’s GenomeStudio software v2010.1. The software’s default settings were

used with the cluster file as developed by Illumina for genotype calling. Results were subsequently

exported and copy number analysis was performed with Genomic Segmentation algorithm of Partek

Genomic Suite (Partek Inc., St. Louis, MO, U.S.A.). Paired analysis was performed with DNA from

blood from the corresponding patients as reference.

aCGH Genome Copy Number Analyses

Sub-megabase resolution tiling path BAC array comparative genomic hybridization (CGH)

platform (BC Cancer Research Centre) comprised 26,363 overlapping elements, with an effective

resolution of 79 kbp3,4. Test and reference (male) genomic DNA was competitively co-hybridized to

the array5. aCGH data was analysed for visualization, alignment, and copy-number annotation and

pair-wise plot comparison as described6,7. Replicate spots with standard deviation >0.1 or signal-to-

noise ratios of <1 were excluded. Clones were aligned initially by the human March 2006

(NCBI36/hg18) and then hg19 genome assemblies. Single nucleotide polymorphism DNA

genotyping experiments were performed and analysed as developed by Illumina for genotype

calling and copy number analysis. Paired analysis was performed with DNA from blood from the

corresponding patients as reference.

4


aCGH images (Array-WoRx charge-coupled device (CCD)-based scanner, Applied Precision,

Issaquah, WA) were analysed with Softworx software (Applied Precision), normalized with CGH

Normalize suite6, and visualization, alignment, and copy-number annotation and pair-wise plot

comparison was provided by SeeGH7. Replicate spots with standard deviation >0.1 or signal-to-

noise ratios of <1 were excluded. Clones were aligned initially by the human March 2006

(NCBI36/hg18) and then hg19 genome assemblies. The algorithm CNA HMMer8 segmented the

data and determined alterations.

Single Nucleotide Polymorphism (SNP) Array Analysis

Microarray-based DNA genotyping experiments were performed at ServiceXS (ServiceXS B.V.,

Leiden, The Netherlands) using the HumanOmni1-Quad BeadChip (Illumina, Inc., San Diego, CA,

U.S.A), according to the manufacturer's instructions. The BeadChip images were scanned on the

iScan system and the data was extracted into Illumina’s GenomeStudio software v2010.1. The

software’s default settings were used with the cluster file as developed by Illumina for genotype

calling. Results were subsequently exported and copy number analysis was performed with

Genomic Segmentation algorithm of Partek Genomic Suite (Partek Inc., St. Louis, MO, U.S.A.).

Paired analysis was performed with DNA from blood from the corresponding patients as reference.

Immunohistochemistry

Paraffin-embedded sections were stained for pRB-N-terminus, 1:200 (554136, BD Pharmingen);

pRB-C-terminus, 1:200 (sc-50), and MYCN, 1:100 (sc-56729, (both Santa Cruz)9,10. Human pRB-

N-terminus immunoreactivity was detected using Immunopure DAB substrate kit (Pierce); human

pRB-C-terminus and N-Myc immunoreactivity using Alexa™ 488 Streptavidin conjugate from

Molecular Probes. DAPI was used to visualize nuclei. Slides were mounted using the DAKO-

Cytomation Fluorescent Mounting Medium and visualized using a Leica DMLB microscope.

Brigitte: 11. Supplementary Methods should indicate unique antibody designations or

catalog numbers, so that they are unambiguously identified regardless of whether a

manufacturer will produce more than one antibody to a particular protein.

Cell CultureAll cell lines were grown in a humidified 37°C incubator with 5% CO2 in their respective media

5


with 100 U/ml penicillin and 0.1 mg/ml streptomycin (Invitrogen) in Iscove’s MDM (Invitrogen)

with 10% FBS Gold (PAA Laboratories), 10 mg/L insulin (Sigma-Aldrich) and 0.0004% (v/v)

β-mercaptoethanol.

ImmunocytochemistryAsynchronous cells were adhered onto poly-D-lysine coated slides, and fixed with in 4%

paraformaldehyde for 15 minutes at room temperature, followed by three washes in PBS, then

incubated in 0.5% Triton-X for 10 minutes at room temperature. Blocking was performed for 30

minutes at room temperature in TBS with 10% DAKO Protein Block (DAKO-Cytomation), 1%

BSA and 0.05% Tween-20. Slides were incubated with primary antibodies (Brn3b, Lim1,

Ap2alpha, L/M Opsin, Rhodopsin, Otx2, Glycogen Synthetase, and Chx10 kindly provided by Dr.

Rod Bremner, Toronto Western Hospital Research Institute; CD133, Abcam; MYCN, Santa Cruz;

IgG, mixture of mouse, rabbit and goat IgGs, Invitrogen) in TBS with 1% BSA, 0.05% Tween-20

and 10% Antibody Diluent (DAKO-Cytomation) overnight at 40C, followed by three washes in

TBS with 0.1% BSA and 0.05% Tween-20, then incubated with either mouse, rabbit or goat

biotinylated antirabbit IgG secondary antibodies (1:200, Vector Laboratories) at room temperature

for 1 hour, followed by three washes. Expression was detected with Streptavidin-Alexa-488

(Molecular Probes); nuclei were visualized with DAPI. Slides were mounted with DAKO

Cytomation Fluorescent Mounting Medium and visualized using a Leica DMLB microscope.

Reverse Transcriptase (End-Point) PCR (RT-PCR)

TRIzol (Invitrogen) extracted total RNA was reverse transcribed using random primers and

SuperScript II RT (Invitrogen), then amplified (annealing temperature 60˚C) using KOD DNA

polymerase (Novagen) following manufacturer’s instructions.

Gene Primer Sequence Expected Size (bp)

RB1 5’-ATGCCGCCCAAAACCCCCCGAAAA-3’ 27875’-TCATTTCTCTTCCTTGTTTGAGGT-3’

MYCN 5’-CACAAGGCCCTCAGTACCTC-3’ 2835’-TCTTCTGTGGGGGTGCAT-3’

Ki67 5’-GCTAAAACATGGAGATGTAAT-3’ 6315’-ATTTTGGTCTTGACTTACGC-3’

6


TBP 5’-ACAACAGGCTGCCACCTTAC-3’ 7435’-GCTGGAAAACCCAACTTCTG-3’

CRXD133

5' CCAACTGGAGGAGCTGGAGTGGATGCAGAACTTGACAACGT 3'

2782325 'GGCAGAGGGGGACTGTAGGAATACCTGCTACGACAGTCGTGGT 3'

X-arrestin 5' TGGGGAGGCAGGCATAGCAAGACTGTAGCTGCTAATTC 3' 5933165' CTTTCCGGACAACCAGCCGCATATGTCCTCAGAGCTAGC 3'

Table S2. Primer sequences and expected product sizes used in end-point reverse transcriptase-PCR

analysis.

7


Quantitative Real-Time PCR

cDNA was used in a 7900HT Fast Real-Time PCR system with Universal PCR Master Mix.

TaqMan® Gene Expression Assays (Applied Biosystems) (Supplementary Table 7). The ΔΔCt

method built into the SDS 2.2 software (Applied Biosystems) determined mean relative gene

expression in triplicate, normalized to GAPDH.

Gene Assay ID Reference SequenceAssay Location

Exon Boundary

Amplicon length

RB1 Hs01078066_m1 NM_000321.2 2650 23-24 72KIF14 Hs00978216_m1 NM_014875.2 2589 11-12 109MYCN Hs00232074_m1 NM_005378.4 1087 1-2 81GAPDH Hs99999905_m1 NM_002046.3 160 3-3 122

Table S3. TaqMan® gene assay locations.

Statistical Analyses

A p value of 6×10-45 was obtained using a “Chi-squared goodness-of-fit test”, using default

probabilities of p1=0.0025 for RB1+/+ and p2=0.9975 for RB1-/- samples, with 28/1054 (0.026)

samples identified as RB1+/+, indicating a negligible chance that all RB1+/+ tumors observed were

due to undetected RB1 mutations in two independent RB1 tumor alleles (Results, Page 4; Table 1).

Frequencies of specific genomic changes between the groups were evaluated using Fisher’s exact

test (Supplementary Table 2). For Fig. 2C, total bp altered per region was summed for each tumor

showing that RB1+/+MYCNA tumors have fewer genome alterations than RB1-/- tumors (p=0.033; t-

test with Welch’s adjustment). Normalized intensity values were processed using the Circular

Binary Segmentation algorithm, with data filtered to identify only high-quality (>3 confirmatory

probes), and suitably sized (>25 kbp) regions of clear differential signal relative to normal (|mean-

signal| > 0.1). For Fig. 2D, separate terms were fitted to patient groups using a general linear model.

A contrast matrix was used to identify the number of differentially abundant probes relative to

normal samples, with an empirical Bayes moderation of standard error and a false-discovery rate

adjustment for multiple testing. For Fig. 3A, comparisons of age of diagnosis applied the Wilcoxon

Rank Sum test.

8


Age of Diagnosis Distribution Analyses

To evaluate the age of diagnosis for RB1+/+MYCNA cases, we formally tested the hypothesis that age

at diagnosis is exponentially distributed. Best fitting curves modeling the relationship between age

at diagnosis and the fraction of cases not yet diagnosed were based on least-squares regression

models and maximum likelihood models assuming a Weibull distribution. Each method was

applied to each group under two assumptions: ages at diagnosis have an exponential distribution

(Weibull distribution with shape parameter of 1) and ages have a Weibull distribution with shape

parameter of 2.

We implemented an intercept-only regression model assuming a Weibull distribution with shape

parameter set to 1 (which implies an exponential distribution), estimating the scale parameter using

maximum likelihood estimation and performing a Lagrange Multiplier test of the assumption that

shape = 1, and plotted the best fitting line from the Weibull regression model with shape parameter

set to 1. The same approach was applied to test the hypothesis that ln S = -kT2, or a Weibull

distribution with shape = 2 (derived by Burch11 for describing ages at diagnosis that result from a

‘two-mutation’ mechanism). AND WHAT WAS THE LEAST SQUARES RESULTS….

Best fitting curves modeling the relationship between age at diagnosis and the fraction of cases not

yet diagnosed, based on least-squares regression models and maximum likelihood models assuming

a Weibull distribution. Each method was applied to each group under two assumptions: ages at

diagnosis have an exponential distribution (a Weibull distribution with shape parameter of 1, red

lines) and ages have a Weibull distribution with shape parameter of 2, blue lines. (Knudson

implemented least-squares methodology.) Actual data points (proportion of ages greater than a

particular age) are shown.

(Knudson implemented least-squares methodology.)

Unilateral RB1-/-

TOTAL STUDY

% under months overall

Unilateral MYCNA

% under months overall

% under months

predicted to be MYCNA# #

n 79 1054 100% 17 100%mean AGE Dx 30.1 6.5median AGE Dx 24.0 4.5# under 12 mo 15 200 19% 14 82% 7%# under 8 mo 5 67 6% 11 65% 16%

9


# under 6 mo 3 40 4% 9 53% 22%# under 4 mo 2 27 3% 5 29% 19%

Table S4. Calculation of Impact of Age on Risk for RB1+/+MYCNA Retinoblastoma. Proportions

of children diagnosed at certain ages were calculated from our measured frequencies of the different

types of retinoblastoma tumors. To estimate risk of RB1+/+MYCNA tumor based on age at diagnosis

of non-familial unilateral retinoblastoma, we extrapolated to the 1054 patients screened for RB1

mutations in tumors.

10


Supplementary Figures

Figure S1: RB1+/+MYCNA tumors have narrow amplicons (orange band) surrounding MYCN

(red line), while in RB1-/- tumors, MYCN gain (<10 copies) involves broad regions of chromosome

2p.

11


Supplementary Figure 2. SNP copy number changes in RB1+/+MYCNA retinoblastoma.

Overview of gains and losses as determined by SNP array on samples A1, A2 and A3. Each bar

represents the gains and/or losses of one tumor for the indicated chromosome; gains red; losses

blue.

12


Supplementary Figure 3. Large prominent nucleoli in RB1+/+MYCNA tumors.

Undifferentiated cells, extensive necrosis and apoptosis, little calcification and large, prominent,

multiple nucleoli in RB1+/+MYCNA tumors. In addition, T33 showed Homer-Wright rosettes

characteristic of neuronal embryonic tumors. Hematoxylin-eosin staining.

13


Supplementary Figure 4. RB1+/+MYCNA tumours express pRB and MYCN.

In Panel A, RB1+/+MYCNA retinoblastoma stained positive for pRB (C-terminus and N-terminus

antibodies) and MYCN protein; controls, normal retina and adjacent normal retina (*). RB1-/- tumor

is negative. In Panel B, in primary RB1+/+MYCNA retinoblastomas full-length RB1, MYCN and

Ki67 transcripts are shown by RT-PCR; Ki67 mRNA indicated proliferation; TBP endogenous

control.

14


Supplementary Figure 5. RB1-/- and RB1+/+ MYCNA tumors express retinal cell markers.

RB1-/- (RB1021, 3.9 MYCN copies and WERI-RB1, 2.2 MYCN copies ) and RB1+/+MYCNA (T101,

40 MYCN copies) retinoblastoma cell lines stained positive for retinal cell type markers (green);

IgG, control; Bar, 50μm.

15


SUPPLEMENTARY TABLES

Supplementary Table 1A. Genomic Gain and Loss of KIF14, E2F3, DEK, CDH11 and MYCN

16


17


18


19


20


Supplementary Table 1B. Copy number data for the heat map in Figure 1B.

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22


23


24


25


Supplementary Table 2. Genomic copy-number changes correlated with RB1 status

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Supplementary Table 3A. aCGH alterations sorted by sample.

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28


29


30


31


32


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Supplementary Table 3B. aCGH alteration details hgb18 and 19.

12. Supplementary Table 3. The difference between Tables 3A and 3B is unclear, except for

additional annotation in 3A. Is Table 3A sufficient?

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36


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39


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Supplementary Table 3C. Copy number alterations detected by SNP array analyses

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Supplementary Table 4. RB1+/+MYCNA tumors express RB1, high MYCN and low KIF14

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ADDITIONAL REFERENCES FOR SUPPLEMENTARY MATERIAL 1. Zeschnigk M, Lohmann D, Horsthemke B. A PCR test for the detection of hypermethylated alleles at the retinoblastoma locus [letter]. Journal of medical genetics 1999;36:793-4.2. Bowles E, Corson TW, Bayani J, et al. Profiling genomic copy number changes in retinoblastoma beyond loss of RB1. Genes Chromosomes Cancer 2007;46:118-29.3. Ishkanian AS, Malloff CA, Watson SK, et al. A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet 2004;36:299-303.4. Watson SK, deLeeuw RJ, Horsman DE, Squire JA, Lam WL. Cytogenetically balanced translocations are associated with focal copy number alterations. Human genetics 2007;120:795-805.5. Kennett JY, Watson SK, Saprunoff H, Heryet C, Lam WL. Technical demonstration of whole genome array comparative genomic hybridization. J Vis Exp 2008.6. Khojasteh M, Lam WL, Ward RK, MacAulay C. A stepwise framework for the normalization of array CGH data. BMC Bioinformatics 2005;6:274.7. Chi B, DeLeeuw RJ, Coe BP, MacAulay C, Lam WL. SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data. BMC Bioinformatics 2004;5:13.8. Shah SP, Xuan X, DeLeeuw RJ, et al. Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics (Oxford, England) 2006;22:e431-9.9. Marchong MN, Chen D, Corson TW, et al. Minimal 16q genomic loss implicates cadherin-11 in retinoblastoma. Mol Cancer Res 2004;2:495-503.10. Dimaras H, Khetan V, Halliday W, et al. Loss of RB1 induces non-proliferative retinoma: increasing genomic instability correlates with progression to retinoblastoma. Hum Mol Genet 2008;17:1363-72.11. Burch PR. Natural and Radiation Carcinogenesis in Man. I. Theory of Initiation Phase. Proc R Soc Lond B Biol Sci 1965;162:223-39.

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