Minimal residual disease monitoring in neuroblastoma · Max M van Noesel4, Jan J Molenaar3, Rogier...

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Minimal residual disease monitoring in neuroblastomavan Wezel, Esther

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Citation for published version (APA):van Wezel, E. M. (2016). Minimal residual disease monitoring in neuroblastoma

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Chapter 2Whole genome sequencing identifies patient-specific DNA minimal residual disease markers in neuroblastoma

J Mol Diagn. 2015 Jan;17(1):43-52

Esther M van Wezel1,2, Danny Zwijnenburg3 , Lily Zappeij-Kannegieter1, Erik Bus1, Max M van Noesel4, Jan J Molenaar3, Rogier Versteeg3, Marta Fiocco5, Huib N Caron2, C Ellen van der Schoot1, Jan Koster3†, Godelieve AM Tytgat2†

† Both authors contributed equally

1. Department of Experimental Immunohematology, Sanquin Research, Amsterdam, The Netherlands

and Landsteiner Laboratory of the AMC, University of Amsterdam, The Netherlands. 2. Department

of Pediatric Oncology, Emma Children’s Hospital, Academical Medical Center, Amsterdam, The

Netherlands. 3. Department of Oncogenomics, Academical Medical Center, University of Amsterdam,

The Netherlands. 4. Department of Pediatric Oncology, Sophia Children’s Hospital, Erasmus Medical

Center, Rotterdam, The Netherlands. 5. Department of Biostatistics, Leiden University Medical Center

and Dutch Childhood Oncology Group The Hague, The Netherlands

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Abstract

Background

PCR-based detection of minimal residual disease (MRD) in neuroblastoma is cur-rently based on RNA markers, however the expression of these targets can vary and only PHOX2B has no background expression. We investigated whether chromosomal breakpoints, identified by whole genome sequencing (WGS), can be used as patient-specific DNA MRD markers.

Methods

WGS data were used to develop large numbers of real-time PCRs specific for tumors of eight patients. These PCRs were used to quantify chromosomal breakpoints in primary tumor and bone marrow (BM) samples. Finally, the DNA breakpoints with the highest abundance were compared with a panel of RNA markers.

Results

By WGS we identified 42 chromosomal breakpoints in tumor samples from eight patients and developed specific qPCRs for each breakpoint. The tumor-specific breakpoints were all present in BM at diagnosis. For one patient slight clonal selection was observed in response to treatment. Positivity of DNA MRD markers preceded disease progression in 4 out of 5 patients, in one patient the RNA markers remained negative. For 16/22 samples the MRD levels determined by RNA and DNA were comparable and in 6/22 samples higher MRD levels were detected by DNA markers.

Conclusions

DNA breakpoints used as MRD targets in neuroblastoma are reliable and stable markers. In addition, this technique might be applicable for detecting tumor cells in other types of cancer.

Patient-specific DNA MRD markers

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Introduction

Neuroblastoma is an extracranial solid tumor of childhood, with a broad spectrum of clinical behavior (1, 2). Despite intensive treatment, high-risk neuroblastoma patients have a poor prognosis and relapse is a frequent occurrence (3-5). Bone marrow (BM) metastases are present in about 50% of patients at diagnosis. Real-time quantitative PCR (qPCR) is a very sensitive technique to detect small numbers of tumor cells in blood or BM (6, 7). Currently, qPCR based minimal residual disease (MRD) detection is based on neuroblastoma specific RNA markers. However, RNA MRD markers can have disadvan-tages, since only PHOX2B has no expression in hematological cells (8). Furthermore, the expression of RNA markers can vary between patients and it is unknown whether these markers are stably expressed during treatment (9). By using RNA markers mRNA levels are measured and not absolute cell numbers. A tumor specific DNA MRD marker would be an interesting alternative, since DNA is more stable than RNA and is not dependent on levels of gene expression (10).The purpose of this study was to investigate whether tumor-specific DNA breakpoints (chromosomal rearrangements such as translocations, deletions, duplications and inver-sion), identified by whole genome sequencing (WGS) of the primary tumor (11), can be used as reliable patient-specific DNA MRD markers.There is increasing evidence supporting the hypothesis that cancer metastases can evolve from a genetic subclone in the primary tumor through a process of clonal selec-tion (12, 13). Selective pressure caused by chemotherapy can lead to the expansion of resistant clones that already existed before the onset of treatment or that are formed as a result of new mutations which were acquired during treatment (14-16). This process of clonal selection might hamper the use of DNA breakpoints as MRD target. Therefore we investigated whether metastases in neuroblastoma lack DNA breakpoints that are detected in the primary tumor and whether clonal selection occurs during treatment. Finally, we compared MRD levels determined by DNA and RNA markers.

Materials and Methods

Patients and samples

From the patients for whom WGS of the primary tumor had been performed, 8 patients were selected meeting the following criteria: stage 4 disease, more than 2 cryopreserved BM samples available and DNA from the primary tumor available (total of 32 samples) (for one patient also 2 PBSC samples were available) (11). Patients were treated at the Emma Children’s Hospital/AMC, Amsterdam or the Sophia Children’s Hospital/EMC, Rot-terdam in the Netherlands. Patient characteristics are shown in Table 1.

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Stored remains were used for this study, after informed consent was given. The study was approved by the Medical Research Ethics Committee of the Academic Medical Center, Amsterdam, The Netherlands.As negative controls, 3 DNA samples from pooled blood samples from healthy individu-als were used in each experiment.Samples were always processed within 24 hours after collection in EDTA tubes and mononuclear cells were cryopreserved in 10% DMSO and stored at -180°C.

Targets from whole genome sequencing data

WGS was performed by Complete Genomics (17) as described by Molenaar et al 2012 (11). Candidate markers were selected from somatic structural variation analyses (CGA tools JunctionDif ) performed on the WGS data for the eight patients. For two patients all candidate markers were evaluated. Based on these results, criteria for target selection were generated (Supplementary tables 1 and 2). Subsequently, the most promising targets (ranging from 2- 9 targets) for six patients were investigated.

DNA extraction and sequencing

Mononuclear cells were isolated from BM and PBSCs by density centrifugation. After washing the cells, DNA was isolated by using the Qiamp DNA blood mini kit (Qiagen, USA). High molecular weight DNA was isolated from tumor tissue by a salt-chloroform extraction method (18). DNA concentrations were determined using the nanodrop

Table 1. Patient Characteristics

Patient ID

Age at diagnosis (months)

Sex* Stage† 1p del‡ MYCN§ Treatment protocol¶ Follow up║

N600 91 M 4 Y N rCOJEC 14, died of progressive disease

N607 3 F 4 Y N NB04 HR + 2x MIBG 110, in CR

N701 54 F 4 Y N NB04 HR + 2x N8 11, died of treatment toxicity in CR

N576 55 F 4 N N 2x MIBG + TOPO + VECI 46, died of progressive disease

N753 26 M 4 N N NB04 HR + 2x MIBG 5, died of treatment toxicity in CR

N540 20 M 4 Y Y 2x MIBG + TOPO + 4xVECI 10, died of progressive disease

N691 73 F 4 Y Y NB04 HR + 2x MIBG 12, died of progressive disease

N718 54 M 4 Y N NB04 HR + 2x MIBG 36, died of progressive disease

Abbreviations:* :F = female, M= male†: Stage according to INSS‡: N=No deletion of 1p, Y = Deletion of 1p§: MYCN: Y= MYCN amplification, MYCN N = No MYCN amplification¶: Treatment protocol: VECI41, rCOJEC42 and NB0443

║: Months from diagnosis


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spectrophotometer. Selected targets were validated by Sanger sequencing of diluted tumor DNA. Because limited amounts of tumor DNA were available, the REPLI-g mini Kit (Qiagen) was used for whole genome amplification of tumor DNA. Primers were designed using Primer3. Oligonucleotides were synthesized by Eurogentec (Liege, Belgium). PCR amplification for sequencing was done on the Veriti Thermal Cycler (Ap-plied Biosystems, USA). Reactions were carried out in 20 μl (10 μl of Genamp fast PCR master mix (Applied Biosystems, USA), 6.5 μl H2O and 1 μl of 10 μM of each primer). PCR products were purified by Exosap-IT (Affymetrix, USA). Sequencing was performed using the BigDyetm Terminator Cycle sequencing Kit (Applied Biosystems).

Real-time quantitative PCR

After validation, primers and probes for qPCR were designed using Primer Express 1.5 (Applied Biosystems) and Oligo 6 (Molecular Biology Insights Inc., USA) (Table 2 for primer and probe sequences). qPCR was performed on the Step-One-Plus (Applied Bio-systems). Reactions were carried out in 20 μl (10 μl Taqman Fast Universal PCR Mastermix (Applied Biosystems), 0.8 μl of 7.5 μM forward and reverse primer and 0.8 μl of 5μM probe and 5μl DNA (100ng/μl)). Initial heating was done for 20 s at 95ºC, followed by 50 cycles of 1 s at 95°C and 20 s at 60°C. To control for DNA input, albumin PCR was performed for each sample. Breakpoint PCRs were performed in triplicate, albumin was performed in duplicate.For each breakpoint PCR the sensitivity was tested on 10 fold serial dilutions of tumor DNA in control DNA. After reaching an efficient sensitivity and PCR efficiency (slope -3.1 till -3.9), patient samples were tested.

Table 2. Primer and probe sequences

Forward Reverse Probe

N701 t(11;17) 5’-AGC-AGA-GCT-ATT-AGT-AAC-TAT-TTT-GAG-CAA-3’

5’-TCT-TTC-CCT-TTT-CAG-AAC-ACA-TCT-T-3’

5’fam-CGG-AAT-CAG-ATC-CTG-AAG-CAT-TAT-TTT-GAA-AAT-3’tamra

Inv4 5’-GAG-GAG-AAA-GGA-CTG-CCT-ACT-AAC-T-3’

5’-GAG-GAG-AAA-GGA-CTG-CCT-ACT-AAC-T-3’

5’fam-CAT-TAT-AGA-ACA-ATT-GGC-CTA-AAC-CTT-GCA-CAG-A-3’tamra

Dup6 5’-CAT-CAA-CAG-GTC-CAG-GAA-GC-3’

5’-ACG-GGA-TCT-AAC-AAT-TCT-ATG-GG-3’

5’fam-TGT-AGA-CAG-ACA-GTT-AAG-ATG-CTT-TTA-TCA-TGG-CAG-A-3’tamra

Dup9 5’-GTT-GAC-TCC-ATT-TTC-GTA-TAC-ATT-CGT-3’

5’-TCC-CTA-TAA-CCA-CTA-TCC-TGA-ATT-TTA-TAA-G-3’

5’fam-AGG-GCA-TAT-ATA-AGT-CAT-AAT-ACA-AAC-CAA-ACA-GCC-AA-3’tamra

Dup9_2 5’-AGG-TTT-CCA-GGG-CTA-AAG-CAG-A-3’

5’-GAG-TAT-GGG-ACC-GCT-GAG-AAA-A-3’

5’fam-TCC-AGT-GTG-AAT-AAG-TCA-AAT-CCA-GAG-ATA-ATA-CA-3’tamra

Dup1 5’-GAG-GTT-TCA-CCA-TGT-TGC-CTG-3’

5’-CAC-TGA-GCC-CTG-ATC-CCA-A-3’

5’fam-TTT-CTT-CCT-GGT-CTC-TCC-TGG-GCA-CA-3’tamra

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Table 2. Primer and probe sequences (continued)


Dup10 5’-TGG-CAG-GGC-CAG-GAT-TTA-3’

5’-CTT-TGA-TGC-ACA-GTA-ACA-GAG-TGC-T-3’

5’fam-ACT-TTC-CAC-ATC-GTT-TTT-ACT-CTG-CCT-AGA-ATA-CTT-ACT-GC-3’tamra

Dup10_2 5’-AGC-CCC-CAG-GGA-ACT-ACT-ATC-TT-3’

5’-AGA-CAG-AAC-TCA-TTG-CCA-ATC-AG-3’

5’fam-ACC-GTA-TCT-ATT-AGT-ACA-ATA-ACA-CAA-CCA-TGT-CCC-T-3’tamra

Inv4_2 5’-CTT-TTT-CAA-GAA-CAT-TAG-CAG-CTT-TAC-T-3’

5’-TCT-AGA-AAT-TCT-ATT-ATG-CAA-ATG-TTG-GTA-3’

5’fam-TAG-TGT-TCA-ATA-ATG-TCA-AAA-ATG-ATC-AAG-TTG-CAC-AC-3’tamra

t(1;21) 5’-CCC-GAG-ATC-ACA-GTA-CCC-TA-3’

5’-CCC-GAG-ATC-ACA-GTA-CCC-TA-3’

5’fam-CCC-CTG-ACG-CTG-ACC-GTG-ACC-TT-3’tamra

N600 Del4 5’-AGT-CAT-AAT-TCA-TCT-TCA-CAC-TGC-AAG-3’

5’-TTA-GCT-CCA-TGT-TGA-GAG-AAA-AAG-G-3’

5’fam-AAC-TTC-AGT-GGC-ATG-GTC-CCC-AGA-GAC-3’tamra

Del17 5’-TTT-ATC-TGA-TGC-TGA-CTC-ATG-GCT-3’

5’-GCT-TTC-CCC-AAG-GCG-G-3’

5’fam-ATC-TGC-GCC-GAG-GTC-GAG-GCT-3’tamra

t(14;19) 5’-CCT-ACT-CTG-TTA-TCT-TCT-GTT-TGT-TCT-ACC-3’

5’-GTA-TCA-TAC-AAG-TAG-AGC-TTG-AGT-GAT-TTT-C-3’

Del14 5’-AAA-ATT-AGC-CGG-GCG-TGG-T-3’

5’-AAG-GAA-AAT-GCA-AAG-GAT-GTG-G-3’

5’fam-CCA-TCT-CCC-AGG-TTC-AGC-CAT-TCT-CCT-3’tamra

Inv5 5’-GCT-CCT-AGT-CAA-AAG-GCT-CAC-ATT-3’

5’-CCA-TTA-AAG-TCT-AGC-TGT-GCC-CAT-3’

5’fam-ACG-CCA-CTG-CAC-TCT-AGC-CTA-GGT-GAC-A-3’tamra

DelX 5’-GGA-TTT-TTA-CCT-CCT-AAA-AAT-CTC-TCA-A-3’

5’-GAG-GTA-CTC-CGG-AAT-GTC-TCC-A-3’

5’fam-AAG-ATG-ATG-ATA-GTT-TGG-ATA-TGG-TGA-GAA-AGG-CAT-3’tamra

t(2;6) 5’-TGC-CCT-TGT-GGA-CCT-TGC-3’

5’-GGT-CCC-TGC-TTC-CCA-CTT-CT-3’

5’fam-AAT-TTA-GAA-GTT-GCT-GTG-TTC-TTG-ATG-GGT-CTG-C-3’tamra

t(11;17) 5’-AAT-CAT-GCC-TTT-ATG-TCC-TCC-AC-3’

5’-GCA-TTT-CCC-CCA-TGC-AAG-3’

5’fam-CTG-AAA-AAT-CGA-GCT-GCA-GAA-AAA-GAT-AAG-GAA-G-3’tamra

Dup5 5’-GGA-AAA-CAG-AAA-GAT-CAT-TTC-ACA-TC-3’

5’-TCA-TTT-CGT-TAT-GTA-CCC-AGT-AGT-CAT-3’

5’fam-CAA-CTA-CAT-GGA-AAC-TGA-AAA-ACC-TGC-TCC-TG-3’tamra

N607 t(1;17) 5’GGC-GGA-AAT-CTG-TCG-TAC-AGA-G-3’

5’-TGC-AGC-CCT-ATC-TGG-TTT-CAA-3’

5’fam-CCA-AGA-TGG-ATA-TAA-TCT-TTT-CAT-CCC-AGA-GGT-G-3’tamra

t(8;17) 5’-AAT-ACA-TAT-TAA-TTT-CTG-AGG-AAC-CAT-CAA-3’

5’-TGG-TGA-TGG-TGG-TTC-CTG-C-3’

5’fam-TGC-CTT-ATA-ATG-AAA-ATG-ACA-AAA-TCA-GAA-AGT-GCC-3’tamra

t(14;19)_1 5’-TGG-GCT-GCC-ACA-ACA-CAC-T-3’

5’-CAC-CAT-GCC-CTG-CTA-ATT-TTT-TA-3’

5’fam-CAT-AGG-GAG-ACC-CCG-CCT-CTA-CAA-ACA-3’tamra

t(8;14)_2 5’-CAG-GTT-GTG-TGG-AGC-ACT-TAC-C-3’

5’-GTC-CCA-GGC-CCT-GCC-TA-3’

5’fam-CCT-CAG-AAC-ACC-TGC-CCC-ACC-CT-3’tamra

t(8;14)_1 5’-TTT-TTC-CAT-GTT-GGT-CAG-GCT-3’

5’-CTC-TGA-ATG-TAA-AAA-CTA-GAT-TAT-AAG-ACT-GAT-TT-3’

5’fam-TGG-TGG-ATC-ACC-TGC-GGT-CG-3’tamra


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Table 2. Primer and probe sequences (continued)


t(1;14)_2 5’-TCA-CCA-GAC-ACG-GAA-TCT-GC-3’

5’-TGG-ACA-CTT-AGG-TTG-TTT-CCA-TTT-T-3’

5’fam-AGG-CTA-GGA-AGT-CCA-AGA-TCA-CAG-TGC-C-3’tamra

t(1;14)_1 5’-TGA-GAC-TTC-CCT-GTG-AGG-TAC-TGA-3’

5’-TTC-CAG-CCT-ATG-AAC-ATT-GTA-TAT-CTT-T-3’

5’fam-AGC-ACT-CAC-GTG-GCT-GTG-TTA-TGT-TTC-C-3’tamra

t(8;14)_3 5’-CCC-CAA-TAA-GGT-GTT-CTC-AGC-A-3’

5’-AAC-CTC-CAC-CTC-CCA-AGT-TCA-3’

5’fam-AGC-TAC-ATG-GGA-GGC-TGA-GAA-GGC-AGA-3’tamra

t(14;19)_2 5’-TCC-CAC-TCT-AAG-TTT-CAT-CGA-GAA-G-3’

5’-GCT-CCC-ACC-TCC-ATG-CAG-3’

5’fam-TTG-GAG-GGA-GGT-CAG-CCG-AGG-G-3’tamra

N576 Dup8 5’-TTT-TTG-TAT-CCT-TTT-CTA-GTG-AGT-CAA-TAT-TAG-3’

5’-CAG-TCA-GGT-TTG-AAC-ATT-TCC-CTA-3’

5’fam-CTC-TCT-TCA-CTG-TGT-AAG-ATG-CTT-TTA-TGA-AAA-TT-3’tamra

t(11;17) 5’-CAT-CCA-CGC-CAA-CAT-CTA-TAA-TTT-T-3’

5’-GGG-CTC-CCC-GGA-CCA-3’

5’fam-AGT-GGG-CGG-TTT-TCT-GCC-TTG-GTG-3’tamra

t(5;14) 5’-CAT-AAG-CAT-TAC-ATT-TAA-ACC-ACC-TAT-ATT-C-3’

5’-AAG-CTT-TAC-AAA-AAC-CCA-CAG-AGG-3’

5’fam-ATC-AAA-GCT-GAC-TGG-TCT-TGT-AGC-ATC-TTC-TCA-3’tamra

N540 t(17;22) CCA-GAC-TCC-CCA-TCA-GGG-T-3’

5’-AAA-GAA-AAT-CTC-ATC-CTT-GAT-GTC-AGT-T-3’

5’fam-TGG-AAC-ATG-GAG-CAT-TTG-TTT-CTT-CAG-TGC-AT-3’tamra

Dup 2 5’-TTT-GGC-CTA-ATA-GTG-TAA-CAT-GGA-AC-3’

5’-TTC-ATT-TAC-CTA-TCA-TCT-GCT-CAT-CTG-3’

5’fam-AGT-GTC-TGA-GTG-AAA-TAA-TAT-TTA-GAT-GAT-GGA-TGG-ACA-A-3’tamra

t(8;22) 5’-TGA-TTG-GGA-GAC-TGG-CTT-CAT-3’

5’-GGG-GTG-CCT-TTT-TAA-GCC-AA

5’fam-CCT-GCC-TGC-CCT-TTG-GTC-AAT-AGT-TG-3’tamra

N718 t(17;19) 5’-GGC-CAT-TTG-TGC-TTT-GCT-G-3’

5’-GTT-GTG-TTT-GCA-GTT-TCC-TGA-ATC-T-3’

5’fam-TCA-CAG-GTG-TTC-TGG-TCC-CGC-AGG-CTC-C-3’tamra

t(2;17) 5’-AAA-CGT-AGA-GGG-TAG-TCC-AGG-G-3’

5’-TGC-TAA-CTA-CCT-GCA-AAA-GGT-G-3’

5’fam-ATT-CCA-GTT-CAG-CAT-TCT-CTC-GTT-CTA-GCA-TTC-3’tamra

N753 Dup 9 5’-CTC-TCC-TTT-CAA-ATA-TGG-TAA-TTA-AGT-CAC-3’

5’-CAT-ATC-AGG-TTC-TAA-GAA-CTT-CAC-ATG-C-3’

5’fam-AAG-CTA-ACT-CCT-GAT-CTC-CTT-CCA-AAC-AGG-CCT-3’tamra

t(11;17) 5’-AGG-CAG-TTG-TTT-CGT-GCA-TTA-G

5’-AGT-TAA-ATC-CAA-GGT-CTT-TGC-AAA-AT-3’

5’fam-TGT-AAG-GCA-ACG-GCT-CCC-GGA-TT-3’tamra

Del 14 5’-CCT-CCC-AAA-GAG-AGA-TGG-TTT-TCT-A-3’

5’-TGT-GCA-TCA-TTC-TTT-AAC-CTT-TAC-AAG-3’

5’fam-AAA-ATA-TTC-AGC-TTC-TCT-GTG-CCT-TGG-TTT-CTT-CA-3’tamra

N691 t(11;17) 5’-ATG-CAG-GGC-TGG-CCT-GA-3’

5’-GTG-ACC-CGT-TCA-AAA-ACT-GCT-C-3’

5’fam-AGG-GAG-AAT-GGC-AGA-GAT-GAT-TTG-ACT-GGA-3’tamra

t(1;2) 5’-TAT-CCC-CAC-TGA-TCT-CAC-CAG-A-3’

5’-CCT-TCC-ATA-GTC-CCT-GCA-CAT-AT-3’

5’fam-AAA-AGT-GTC-AAA-CGC-ACA-GTG-GGA-TAT-TAA-TGA-AT-3’tamra

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RNA Extraction

RNA was isolated from PAX blood RNA tubes (Qiagen) with the PAX blood RNA Kit (Qia-gen) or with Trizol (Invitrogen). cDNA was synthesized from 2-3μg RNA, using random hexamers (25 μM) (Invitrogen), dNTPs (1 mM) (Promega) and M-MLV reverse transcrip-tase (100U) (Invitrogen), in a total reaction volume of 20 μl and incubated at 42°C for 45 min. Finally, the reverse transcriptase was inactivated by heating and the volume was diluted to 120 μl. qPCR for paired-like homeobox 2B (PHOX2B), tyrosine hydroxylase (TH), dopa decarboxylase (DDC), cholinergic receptor nicotinic alpha 3 (CHRNA3) and growth associated protein 43 (GAP43) was performed using β-glucoronidase (GUS) for normalization as described previously (6).

Data analysis

A sample was scored positive if at least one of the three replicates had a Ct value <40 and no amplification was found in control samples. Percentages were calculated relative to albumin and taking PCR efficiency into account, according to the following formula: 2^dCt * 100/ PCR efficiency (of the target).To quantify RNA based MRD, the expression level of the RNA PCR targets were related to the primary tumor, according to the following formula: 2^ddCt (dCt primary tumor − dCt BM) * 100%. For quantification the median of the panel of 5 RNA targets was determined for each sample to minimize the effect of up or down regulation of the PCR targets (6). For each patient the DNA breakpoint with the highest MRD level was chosen at each time point, in analogy to MRD levels in acute lymphoid leukemia (19), to compare RNA and DNA markers.

Results

Development of patient-specific breakpoint PCRs

A large number of structural DNA defects were selected from the WGS data for three patients and a large number of patient-specific PCRs were developed. For these three patients we detected 13 (N600) (Figure 1A), 12 (N701) (Figure 1B), and 27 (N607) (Figure 1C), breakpoints in the primary tumor, as shown in the circos plots in Figure 1. For N607 only 9 out of 27 breakpoints were selected for further analysis, because there was insuf-ficient amount of BM DNA available to study all targets. Four targets for N600 and three targets for N701 could not be sequence-validated or no specific primers and probes could be designed. All other PCRs were specific (no amplification in control samples) and a sensitivity of at least 10-4 was reached for each PCR (Supplementary tables 1A-C and Supplementary figures 1-3). The characteristics of the different targets are shown in Supplementary table 2-4. The efficiency of the qPCRs, as determined by the slope

Patient-specifi c DNA MRD markers

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of the standard curve of diluted tumor DNA, was between 80 and 110 % for all markers (slope -3.1 till -3.9), except for Inv4 (2) (N701), Del X (N600) and t(8;17) (N607). To be able to more precisely quantify the abundance of the diff erent DNA breakpoints for one patient (N600) also standard curves were created using plasmids, each containing one of the specifi c DNA breakpoints (Supplementary fi gure 4). As expected, all standard curve lines were parallel. Moreover the intercepts of the curves were virtually identical.Based on the validation results from these 3 patients criteria for target selection were generated (Supplementary tables 2-4, see fi gure legends for selection criteria). MYCN and chromotripsis regions were excluded, due to the complexity of these regions. Using these criteria we selected breakpoints for an additional 5 patients (N691, N718, N576, N753, N540). For all these patients breakpoint qPCRs were successfully developed and the effi ciencies of all PCRs were comparable and near 100%. All PCRs were specifi c, since no signal was obtained on genomic DNA from normal mononuclear cells and for all PCRs a sensitivity of at least 10-4 was reached (Supplementary fi gures 5-9 and Supplemen-tary tables 1D-H).

A. B.

C.

Figure 1. Structural alterations N600, N701 and N607Circos plots showing the structural variants for A. N600, B. N701 and C. N607.The inner ring represents the copy number variations (green, loss; red, gain) based on coverage of the tumor and lymphocyte genomes. The lines traversing the ring indicate inter- and intrachromosomal rear-rangements identifi ed by discordant mate pairs from paired-end reads.

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Comparison of breakpoint PCRs in primary tumor and BM metastases

For all 8 patients successful PCRs were developed and the DNA breakpoints were quanti-fied in the primary tumor and in BM at diagnosis for 7 patients (for one patient no BM sample at diagnosis was available) (Figure 2). In 4 out of 7 tumors (N600, N607, N701 and N540) a near 10 fold difference in level of positivity between the highest and lowest breakpoints was found, suggesting the existence of different subclones in the primary tumor. Most patients had a BM infiltration at diagnosis between 1 and 10%, as deter-mined by morphology or immunocytology. However patient N701 had very high BM infiltration, whereas patient N753 had low BM infiltration. These percentages determined by morphology or immunocytology were comparable by using the DNA markers (Figure 2). In BM at diagnosis all 39 selected breakpoints were detected for the 7 patients. The relative contribution of the different targets was similar in the primary tumor and bone marrow at diagnosis in all patients, except for N701 and N540. For N701 two out of ten markers (Dup10(1) and t(1;21)) and for N540 one out of three markers (Dup2) did not show a parallel pattern. This indicates that these markers have a slightly higher abun-dance in the primary tumor than in BM at diagnosis. Although some breakpoints were present in only a subset of primary tumor cells, for the 7 patients studied MRD detection at diagnosis was not hampered since all breakpoints were detected in BM at diagnosis.

DNA MRD monitoring during treatment and follow-up

Three out of eight patients were in complete remission at last follow-up (N607, N701, N753) and showed molecular complete remission by DNA markers (Figure 3). However, N701 and N753 died in complete remission from treatment toxicity. Five out of eight patients died of disease progression (N600, N576, N540, N718 and N691). In four of these patients (N600, N576, N540 and N691) the DNA MRD results were positive at the end of treatment, indicating that DNA MRD marker positivity preceded relapse in these four patients. One patient (N718), with a lack of follow-up samples, had negative DNA MRD results 6 months after diagnosis and suffered from a relapse at 36 months.Next we investigated whether the DNA markers within each tumor showed a concor-dant pattern in response to treatment. The three patients where 9 or more targets were followed are most informative in this respect (Figure 3). For N600 (Figure 3A) three follow up BM samples were tested and all nine DNA MRD markers followed the same response pattern. For N607 (Figure 3B) six BM follow up samples were tested. After 10 months all targets were undetectable, and remained as such.For N701 (Figure 3C) four BM follow up samples were tested, showing that, although all markers decreased after start of therapy and became negative after 10 months of treatment, the markers did not show identical responses to treatment. DNA markers dup9 (2) and dup10 (1) showed no response at 4 months (p = 0.004, t-test, for difference in level between 3 and 4 months) but did respond again after 7 months. DNA markers


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Figure 2. Comparison of breakpoint PCRs in primary tumor and BM metastasesThe percentage relative to albumin for several targets in the primary tumor and in bone marrow at diagno-sis for A. N600, B. N607, C. N701, D. N576, E. N753, F. N540 and G. N691. The Y-axis indicates the MRD level as determined by the different targets (percentages were calculated relative to albumin and taking PCR efficiency into account, according to the following formula: 2^dCt * 100/ PCR efficiency (of the target) The dashed line indicates that for these targets the slope of the standard curve was < -3.9.Abbreviations: BM = Bone marrow, Del = Deletion; Inv = Inversion; Dup = Tandem duplication

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Figure 3. Target dynamics for several DNA breakpoints at diagnosis and during treatmentThe dynamics of the different targets for A. N600, B. N607, C. N701, D. N576, E. N753, F. N540, G. N691 and H. N718. On the X-axis the different time points are shown. The Y-axis indicates the MRD level as determined by the different targets (percentages were calculated relative to albumin and taking PCR efficiency into account, according to the following formula: 2^dCt * 100/ PCR efficiency (of the target). MRD level deter-mined by using a panel of RNA markers is indicated by a grey line. Coloured lines represent differences in response. The dashed line indicates that for these targets the slope of the standard curve was < -3.9.All markers for the different samples were tested in triplicate.BM = bone marrow; Dx = Diagnosis; M = months; Del = Deletion; Inv = Inversion; Dup = Tandem duplication


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dup10 (2) and t(1;21) showed an increase in MRD level at 7 months ( p = 0.002, t-test, for the difference in level between 3 and 7 months).Of the five patients, in which less targets were tested (Figure 3D-H), two targets also showed some discordances between DNA markers in response to therapy (N576 and N691) (Figure 3D and G). In N576 one out of three targets became undetectable after 4 months, while the other two targets remained positive even 3 years after diagnosis. Three years and seven months after diagnosis this patient suffered from a systemic relapse. In N691 the first peripheral blood stem cell (PBSC) sample was negative for all markers, but the following BM sample was positive again for one DNA marker. The second PBSC sample was positive for both DNA markers and the patient died hereafter of a systemic relapse.In conclusion, slight differences in response to treatment of the targets were observed in three out of eight patients. To avoid the risk of false negative results caused by clonal selection, at least two DNA breakpoints should be selected.

Comparison of MRD levels for RNA panel and DNA breakpoints

In Figure 4 the correlation between MRD levels determined by the DNA breakpoints and the panel of RNA markers is shown. BM samples (at diagnosis and during follow

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Figure 4. Logarithmic correlation between the PCR results of a panel of RNA markers and a single chromosomal rearrangementResults of PCR based quantification by a chromosomal rearrangement or a panel of RNA markers for 8 pa-tients (22 samples within quantitative range). The X-axis indicates the MRD level determined by the median of a panel of RNA markers relative to the primary tumor. The Y-axis indicates the MRD level determined by a single chromosomal rearrangement with the highest abundance in the primary tumor.

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up) from 8 patients were used. In 16 out of 22 positive samples, results are comparable for these two methods (within 1 log difference). In 6 out of 22 samples there is a differ-ence of >1 log between the two methods. Four samples were from patient N576. For this specific patient there was a major difference in sensitivity between DNA and RNA MRD markers and the RNA PCR became negative after 4 months of treatment (Figure 3). However, two of the DNA targets remained positive even 3 years after diagnosis. Three years and seven months after diagnosis this patient suffered from a systemic relapse. For two other patients one follow-up sample showed a higher MRD level determined by the DNA markers.

Discussion

In the present study, we show that DNA breakpoints identified by whole genome sequencing of the primary tumor can reliably be used as DNA MRD markers. We have investigated whether WGS data can be used to develop tumor-specific qPCRs with opti-mal sensitivity. For all 8 patients specific and highly sensitive qPCRs could be developed.The use of tumor WGS data for the development of patient-specific PCRs for treatment monitoring of cancer has been described before (20,21). However, most studies have investigated the detection of mutations or chromosomal rearrangements in plasma of cancer patients. We show that the detection of chromosomal aberrations is feasible in metastasized or circulating tumor cells, without amplification of germline DNA and that MRD monitoring will not be hampered by clonality. Since the number of somatic muta-tions is significant lower in pediatric malignancies, such as neuroblastoma, as compared with adult cancers, (22) using chromosmal rearrangements could be the way forward for DNA based MRD detection in neuroblastoma.Tumor-specific genetic alterations are ideal markers to stage malignant disease at diag-nosis, to monitor the response to therapy and to detect relapse at an early stage. When recurrent genetic defects are known for a tumor type (such as BCR-ABL in leukemia (23) and the EWSR1 translocations in Ewing sarcoma (24)) it is relatively easy to screen for the presence at diagnosis on either RNA or DNA level, and to use the identified breakpoints for MRD testing. In neuroblastoma only MYCN amplification is a recurrent gene-specific event. Kryh et al have described the use of MYCN amplicon junctions as tumor-specific targets for MRD detection in neuroblastoma (25). In our study the MYCN amplified regions were excluded. MYCN is commonly amplified in double minutes (DM). Given the fact that DMs are divided to daughter cells in an independent manner from the chromosomes and we cannot exclude that further recombination events may also affect DMs on their own, using MYCN amplicon junctions as MRD markers could in theory be a


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risk, since the selected junction may become lost in metastases or during treatment, if further recombinations would occur in one ore more of the DM’s (26).Clonal evolution of the tumor might hamper the application of tumor specific qPCRs. The model of clonal selection suggests that metastases can evolve from a subpopulation in the primary tumor and that during growth, these cells can continue to diversify 16. This was shown in several studies by successfully comparing primary tumors and metastatic lesions (12, 27-29). If for neuroblastoma only rare cells have the ability to metastasize, these cells might not harbor all chromosomal rearrangements detected in the primary tumor. We show for neuroblastoma that, although some DNA breakpoints are present in only a subset of primary tumor cells, in BM at diagnosis all selected breakpoints can be detected. Therefore detection of BM infiltration based on tumor-specific DNA markers is reliable.Cancer development is widely regarded as an evolutionary process involving natural selection and therapies often select for resistance. Resistant clones might eventually cause a relapse (15, 30). If the resistant clone develops from a cell that does not harbor the breakpoint used as MRD marker, this might lead to false negative results. Therefore we extensively studied multiple (9 or 10) breakpoints during treatment for 3 patients. By using DNA breakpoints as clonal markers, clonal selection could not be demonstrated during treatment for two patients (N600 and N607). For one patient slight differences in the response to treatment of the targets occurred (N701). Indicating that genetically dif-ferent cells might respond in a different way to chemotherapy. To avoid the risk to miss subclones, multiple DNA breakpoints should be selected. They should preferably have a high abundance in the primary tumor, because it is likely that those aberrations are early events and therefore exist in the majority of the primary tumor and metastasized cells. Indeed for two patients (N701 and N576) breakpoints with a low abundance in the primary tumor, become undetectable at an earlier time point compared to other markers.In one patients disease progression could be predicted by using the DNA markers, whereas this was not the case for the RNA markers. Overall, the MRD levels determined by DNA markers were comparable to RNA based PCRs in 16 out of 22 samples, while in 6 samples even a higher MRD level was found for the DNA markers. By using RNA markers the MRD level might be underestimated in these samples. However, which method is optimal for MRD detection will have to be investigated by comparing the sensitivity and clinical utility of DNA and RNA markers in paired samples of a large cohort of patients.qPCR, by using RNA markers and GD2-immunocytology, are now being standardized for MRD monitoring and the clinical significance of MRD monitoring has been shown (7, 31, 32). The DNA markers described in this study might be more suitable for MRD monitor-ing, since these markers are truly neuroblastoma-specific and are not dependent on lev-els of gene expression, similar to hypermethylated RASSF1a, as we previously described

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(10). Positive DNA MRD markers on follow-up samples preceded clinical disease activity in 4 out of 5 relapse patients.At present WGS is expensive and the time to complete the sequence analysis are prob-lematic for the application in clinical use. However, as sequence technologies improve and costs decrease, it is likely that WGS of tumors will become common practice in the future (33-35). Future improvements in high-risk neuroblastoma outcomes will require the identification of patient and disease-specific genetic aberrations for risk evaluation and the application of targeted therapies (36, 37). Indeed recently three other groups have reported on next generation seguencing of neuroblastoma tumors (38-40). Mole-naar et al. have performed WGS of 50 stage 4 primary tumors. All of these stage 4 tumors contain several chromosomal aberrations. Therefore, we think this technique is feasible for BM disease monitoring in all stage 4 patients11.We have shown the possibilities for MRD monitoring in a retrospective setting. With the expected improvements in WGS and the increased application of targeted therapies it is anticipated that these tumor specific chromosomal rearrangement can be included in prospective studies. Our findings were restricted to neuroblastoma patients, however this technique might be applicable for the detection and monitoring of circulating and metastatic tumor cells in other types of cancer.In this proof of concept study we show that DNA breakpoints, identified in the pri-mary tumor by WGS, are reliable and stable MRD markers and this is a highly tumor and patient-specific technique. DNA MRD marker positivity preceded disease progression in the patients studied. The clinical applicability and significance of these DNA markers will have to be studied in a larger (prospective) cohort by comparing DNA and RNA markers.


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35. Welch JS & Link DC: Genomics of AML: clinical applications of next-generation sequencing. Hematology. Am. Soc. Hematol. Educ. Program. 2011, 2011: 30-35.

36. Ambros IM, Brunner B, Aigner G, Bedwell C, Beiske K, Benard J, Bown N, Combaret V, Couturier J, Defferrari R, Gross N, Jeison M, Lunec J, Marques B, Martinsson T, Mazzocco K, Noguera R, Schlei-ermacher G, Speleman F, Stallings R, Tonini GP, Tweddle DA, Valent A, Vicha A, Roy NV, Villamon E, Ziegler A, Preuner S, Drobics M, Ladenstein R, Amann G, Schuit RJ, Potschger U & Ambros PF: A multilocus technique for risk evaluation of patients with neuroblastoma. Clin. Cancer Res. 2011, 17: 792-804.

37. Cole KA & Maris JM: New strategies in refractory and recurrent neuroblastoma: translational op-portunities to impact patient outcome. Clin. Cancer Res. 2012, 18: 2423-2428.

38. Cheung NK, Zhang J, Lu C, Parker M, Bahrami A, Tickoo SK, Heguy A, Pappo AS, Federico S, Dalton J, Cheung IY, Ding L, Fulton R, Wang J, Chen X, Becksfort J, Wu J, Billups CA, Ellison D, Mardis ER, Wilson RK, Downing JR & Dyer MA: Association of age at diagnosis and genetic mutations in patients with neuroblastoma. JAMA 2012, 307: 1062-1071.

39. Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, Auclair D, Carter SL, Cibulskis K, Hanna M, Kiezun A, Kim J, Lawrence MS, Lichenstein L, McKenna A, Pedamallu CS, Ramos AH, Shefler E, Sivachenko A, Sougnez C, Stewart C, Ally A, Birol I, Chiu R, Corbett RD, Hirst M, Jackman SD, Kamoh B, Khodabakshi AH, Krzywinski M, Lo A, Moore RA, Mungall KL, Qian J, Tam A, Thiessen N, Zhao Y, Cole KA, Diamond M, Diskin SJ, Mosse YP, Wood AC, Ji L, Sposto R, Badgett T, London WB, Moyer Y, Gastier-Foster JM, Smith MA, Guidry Auvil JM, Gerhard DS, Hogarty MD, Jones SJ, Lander ES, Gabriel SB, Getz G, Seeger RC, Khan J, Marra MA, Meyerson M & Maris JM: The genetic landscape of high-risk neuroblastoma. Nat. Genet. 2013, 45: 279-284.

40. Sausen M, Leary RJ, Jones S, Wu J, Reynolds CP, Liu X, Blackford A, Parmigiani G, Diaz LA, Jr., Papa-dopoulos N, Vogelstein B, Kinzler KW, Velculescu VE & Hogarty MD: Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma. Nat. Genet. 2013, 45: 12-17.

41. Kraker KJ, Hoefnagel KA, Verschuur AC, van EB, van Santen HM & Caron HN: Iodine-131-metaiodo-benzylguanidine as initial induction therapy in stage 4 neuroblastoma patients over 1 year of age. Eur J Cancer 2008, 44: 551-6.

42. Pearson AD, Craft AW, Pinkerton CR, Meller ST & Reid MM: High-dose rapid schedule chemo-therapy for disseminated neuroblastoma. Eur J Cancer 1992, 28A: 1654-9.

43. GPOH-NB2004-HR NB2004-HR, UNI-KOELN-161, NCT00526318, EU-20661. 2014.

Chapter 2

44

Supplementary data

Supplementary table 1

Supplementary table 1A. Quantitave range and sensitivity for all targets tested for N701.

N701 Quantitative range Sensitivity

t(11;17) 10-4 10-5

Inv4 5x 10-4 10-4

Dup6 5x 10-4 10-5

Dup9 5x 10-4 10-5

Dup9_2 10-3 10-5

Dup1 5x 10-4 10-4

Dup10 10-3 10-4

Dup10_2 5x 10-4 10-4

Inv4_2 10-1 5x 10-3

t(1;21) 10-4 10-4

Supplementary table 1B. Quantitave range and sensitivity for all targets tested for N600.


Del4 5x 10-4 10-4

Del17 10-5 10-5

t(14;19) 10-5 10-5

Del14 10-4 10-5

Inv5 10-5 10-5

DelX 10-3 10-4

t(2;6) 10-4 10-5

t(11;17) 10-4 10-5

Dup5 5x 10-4 10-4

Supplementary table 1C. Quantitave range and sensitivity for all targets tested for N607.


t(1;17) 5x 10-4 10-5

t(8;17) 10-3 5x 10-4

t(14;19)_1 10-4 10-5

t(8;14)_2 5x 10-4 10-4

t(8;14)_1 10-4 10-4

t(1;14)_2 5x 10-4 10-5

t(1;14)_1 10-4 10-5

t(8;14)_3 10-4 10-5

t(14;19)_2 10-4 10-5


2

45

Supplementary table 1D. Quantitave range and sensitivity for all targets tested for N576.


Dup8 5x 10-4 10-4

t(11;17) 10-4 10-4

t(5;14) 10-4 10-5

Supplementary table 1E. Quantitave range and sensitivity for all targets tested for N540.


t(17;22) 5x 10-4 10-5

Dup 2 5x 10-4 10-5

t(8;22) 10-4 10-5

Supplementary table 1F. Quantitave range and sensitivity for all targets tested for N718.


t(17;19) 10-4 10-5

t(2;17) 10-4 10-5

Supplementary table 1G. Quantitave range and sensitivity for all targets tested for N753.


Dup 9 5x 10-4 10-5

t(11;17) 10-4 10-5

Del 14 5x 10-4 10-5

Supplementary table 1H. Quantitave range and sensitivity for all targets tested for N691.


t(11;17) 10-4 10-5

t(1;2) 10-4 10-5

Supplementary Table 2.

N600 targets ID Discordantmates

CGH Validated RQ-PCR Slope Interchromosomal

Distance FrequencyInBaselineGenomeSet

Del 4 3719 32 1 Y 1 -3.2 N 1152991 0

Del 17 2109 23 1 Y 1 -3.3 N 178565 0

Del 14 2349 19 1 Y 1 -3.7 N 456426 0

Del X 576 97 1 Y 1 -4.3 N 426278 0

Inv 5 4617 57 0 Y 1 -3.7 N 856041 0

t(4;19) 400 17 1 Y 1 -3.6 Y 0

t(11;17) 2251 32 1 Y 1 -3.4 Y 0

t(2;6) 5068 107 1 Y 1 -3.4 Y 0

Dup 5 1326 30 0 Y 1 -3.9 N 4105648 0

Chapter 2

46

Supplementary Table 2. (continued)




Del 7 3096 40 1 Y 0 N 518417 0

t(4;19)inv 3489 39 1 Y 0 Y 0

Del 11 2621 12 0 Y 0 N 26065 0

Inv 7 382 42 0 N 0 N 4134 0

ID = junction IDDiscordant mates = number of discordant mate pairs. A higher number indicates a higher confidence that junction is presentCGH = Comparative genomic hybridization; 1= clear gain or loss, 0= no change in pattern (no gain/loss)Validated = validated by sequencing; Y= junction validated; N= junction not validatedRQ-PCR 1= successful; 0= failedInterchromosomal Y=Yes N= NoDistance = Distance between de left and right side of the junction (basepairs)Frequency in baseline genome set = Frequency junction is detected in 52 normal genomes used as base-line reference setAll targets meeting the following requirements resulted in a successful PCR: no background in control sam-ples (frequency in baseline = 0), a clear difference in CGH pattern (clear gain or loss), a distance of at least 10000bp (for duplications, inversions and deletions) and number of discordant mates > 15.

Supplementary Table 3.




t(11;17) 2014 22 1 Y 1 -3.2 Y 0

Dup 9 (1) 1035 47 1 Y 1 -3.8 N 48136 0

Dup 9 (2) 1043 16 0 Y 1 -3.7 N 327297 0

Dup 6 1125 87 0 Y 1 -3.9 N 611 0

Dup 1 1482 24 0 Y 1 -3.2 N 52852 0

Inv 4 (1) 427 41 1 Y 1 -3.2 N 2848516 0

Inv 4 (2) 4483 36 1 Y 1 -6.6 N 10947975 0

Dup 10 (1) 1026 10 1 Y 1 -3.8 N 1323850 0

Dup 10 (2) 1027 14 1 Y 1 -3.9 N 1121861 0

t(1;21) 1231 19 1 Y 1 -3.3 Y 0

Inv 7 311 68 0 N 0 N 5649 0

Del 7 2763 36 0 Y 0 N 2927 0

ID = junction IDDiscordant mates = number of discordant mate pairs. A higher number indicates a higher confidence that junction is presentCGH = Comparative genomic hybridization; 1= clear gain or loss, 0= no change in pattern (no gain/loss)Validated = validated by sequencing; 1= junction validated; 0= junction not validatedRQ-PCR 1= successful 2= failedInterchromosomal Y=Yes N= NoDistance = Distance between de left and right side of the junction (basepairs)


2

47

Frequency in baseline genome set = Frequency junction is detected in 52 normal genomes used as base-line reference setAll targets meeting the following requirements resulted in a successful PCR: no background in control sam-ples (frequency in baseline = 0), a clear difference in CGH pattern (clear gain or loss), a distance of at least 10000bp (for duplications, inversions and deletions) and number of discordant mates > 15.

Supplementary table 4.




t(14;19) (1) 53 27 Y 1 -3.6 Y 0

t(8;14) (2) 4548 74 1 Y 1 -3.8 Y 0

t(1;17) 519 103 1 Y 1 -3.3 Y 0

t(8;17) 228 76 1 Y 1 -4.8 Y 0

t(8;14) (1) 260 16 1 Y 1 -3.9 Y 0

t(1;14) (1) 535 15 1 Y 1 -3.2 Y 0

t(1;14) (2) 5021 36 1 Y 1 -3.4 Y 0

t(8;14) (3) 4550 25 1 Y 1 -3.9 Y 0

t(14;19) (2) 796 61 1 Y 1 -3.9 Y 0

ID = junction IDDiscordant mates = number of discordant mate pairs. A higher number indicates a higher confidence that junction is presentCGH = Comparative genomic hybridization; 1= clear gain or loss, 0= no change in pattern (no gain/loss)Validated = validated by sequencing; 1= junction validated; 0= junction not validatedRQ-PCR 1= successful; 2= failedInterchromosomal Y=Yes N= NoDistance = Distance between de left and right side of the junction (basepairs)Frequency in baseline genome set = Frequency junction is detected in 52 normal genomes used as base-line reference setAll targets meeting the following requirements resulted in a successful PCR: no background in control sam-ples (frequency in baseline = 0), a clear difference in CGH pattern (clear gain or loss), a distance of at least 10000bp (for duplications, inversions and deletions) and number of discordant mates > 15.

Chapter 2

48

Supplementary Figure 1.

0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 6

2 8

3 0

3 2

3 4

S ta n d a rd c u rv e N 6 0 0 D e l 4

Q u a n tity (c o p ie s )

Ct

va

lue

s lo p e : - 3 .2 1 4 Y - in te r c e p t: 2 2 .8 5 R 2 : 0 .9 8 5 E f f% : 1 0 4 .7 2 8

0 .0 0 0 0 1 0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 4

2 6

2 8

3 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 6 0 0 D e l 1 4

Q u a n tity (c o p ie s )C

t v

alu

e

s lo p e : - 3 .6 9 7 Y - in te r c e p t: 2 0 .8 7 2 R 2 : 0 .9 8 1 E f f% : 9 6 .4 2 2

0 .0 0 0 0 1 0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 4

2 6

2 8

3 0

3 2

3 4

3 6

3 8



Ct

va

lue

s lo p e : - 3 .2 7 4 Y - in te r c e p t: 2 1 .4 9 1 R 2 : 0 .9 8 9 E f f% : 1 0 2 .0 3 1

0 .0 0 1 0 .0 1 0 .12 4

2 6

2 8

3 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 6 0 0 D e l X


Ct

va

lue

s lo p e : - 4 .3 1 8 Y - in te r c e p t: 2 0 .5 5 6 R 2 : 0 .9 9 E ff% : 7 0 .4 3 6

0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 4

2 6

2 8

3 0

3 2

3 4

3 6

3 8

S ta n d a rd c u r v e N 6 0 0 D u p 5


Ct

va

lue


0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 4

2 6

2 8

3 0

3 2

3 4

3 6

3 8

4 0

S ta n d a r d c u r v e N 6 0 0 In v 5


Ct

va

lue


0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 4

2 6

2 8

3 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 6 0 0 t (2 ;6 )


Ct

va

lue

s lo p e : - 3 .4 3 4 Y - in te r c e p t: 2 1 .2 9 1 R 2 : 0 .9 9 1 E ff% : 9 5 .5 2 3

0 .0 0 0 1 0 .0 0 1 0 .0 12 4

2 6

2 8

3 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 6 0 0 t(1 1 ;1 7 )


Ct

va

lue

s lo p e : - 3 .1 7 4 Y - in te r c e p t: 2 3 .3 0 2 R 2 : 0 .9 9 E ff% : 1 0 6 .5 4 9

0 .0 0 0 0 1 0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 2

2 4

2 6

2 8

3 0

3 2

3 4

3 6

3 8



Ct

va

lue

s lo p e : - 3 .3 6 5 Y - in te r c e p t: 2 0 .7 1 R 2 : 0 .9 9 2 E ff% : 9 8 .2 3 7

Standard curves of the different targets (Del 4, Del 14, Del 17, Del X, Dup 5, Inv 5, t(2;6), t(11;17), t(14;19) tested for N600. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.


2

49


0 .0 0 0 0 1 0 .0 0 0 1 0 .0 0 1 0 .0 12 8

3 0

3 2

3 4

3 6

3 8

4 0



Ct

va

lue

s lo p e : -3 .1 8 Y - in te rc e p t: 2 3 .3 3 2

R 2 : 0 .9 7 1 E ff% : 1 0 6 .2 9 3

0 .0 0 0 1 0 .0 0 1 0 .0 12 8

3 0

3 2

3 4

3 6



Ct

va

lue

s lo p e : -3 .2 4 8 Y - in te rc e p t: 2 3 .0 9 9

R 2 : 0 .9 8 E ff% : 1 0 3 .1 5 8

0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 8

3 0

3 2

3 4

3 6

3 8



Ct

va

lue


R 2 : 0 .9 9 E ff% : 7 8 .8 4 1

0 .0 0 1 0 .0 1 0 .12 6

2 8

3 0

3 2

3 4

S ta n d a r d c u rv e N 7 0 1 D u p 1 0 (2 )


Ct

va

lue

s lo p e : -3 .8 Y - in te rc e p t: 2 2 .9 4 1

R 2 : 0 .9 6 6 E f f% : 8 3 .2 8 9

0 .0 0 1 0 .0 13 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 7 0 1 In v 4


Ct

va

lue


R 2 : 0 .9 5 2 E ff% : 1 0 6 .2 1 8

0 .0 0 1 0 .0 1 0 .13 2

3 4

3 6

3 8

4 0

4 2

4 4

S ta n d a r d c u r v e N 7 0 1 In v 4 (2 )


Ct

va

lue


R 2 : 0 .9 2 3 E f f% : 4 1 .0 7 8

0 .0 0 0 1 0 .0 0 1 0 .0 1

3 0

3 2

3 4

3 6

3 8

S ta n d a r d c u r v e N 7 0 1 t (1 ;2 1 )


Ct

va

lue


R 2 : 0 .9 7 6 E ff% : 1 0 0 .3 9 9

0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .12 6

2 8

3 0

3 2

3 4

3 6

3 8



Ct

va

lue


R 2 : 0 .9 8 E ff% : 8 2 .1 1 6

0 .0 0 1 0 .0 1 0 .12 6

2 8

3 0

3 2

3 4

3 6

3 8

S ta n d a rd c u r v e N 7 0 1 D u p 9 (2 )


Ct

va

lue


R 2 : 0 .9 8 E ff% : 8 6 .5 6 1

0 .0 0 0 1 0 .0 0 1 0 .0 1

3 0

3 2

3 4

3 6

3 8



Ct

va

lue


R 2 : 0 .9 7 6 E ff% : 1 0 0 .3 9 9

Standard curves of the different targets (t(11;17, Dup 1, Dup 6, Dup 9, Dup 9 (2), Dup 10, Dup 10 (2), Inv 4, Inv4 (2), t(1;21)) tested for N701. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.

Chapter 2

50


0 .0 0 0 1 0 .0 0 1 0 .0 1

2 8

3 0

3 2

3 4

3 6



Ct

va

lue


R 2 : 0 .9 8 9 E ff% : 1 0 3 .1 6 3

0 .0 0 1 0 .0 1

2 8

3 0

3 2

3 4

S ta n d a rd c u rv e N 6 0 7 t (1 ;1 4 ) (2 )


Ct

va

lue


R 2 : 0 .9 8 2 E f f% : 9 7 .7 8 8

0 .0 0 1 0 .0 1 0 .12 5

3 0



Ct

va

lue


R 2 : 0 .9 9 8 E f f% : 1 0 0 .6 9

0 .0 0 0 1 0 .0 0 1 0 .0 13 0

3 2

3 4

3 6

3 8



Ct

va

lue


R 2 : 0 .9 8 4 E ff% : 8 2 .9

0 .0 0 1 0 .0 12 8

3 0

3 2

3 4

3 6



Ct

va

lue


R 2 : 0 .9 8 3 E f f% : 8 3 .8 4 5

0 .0 0 0 1 0 .0 0 1 0 .0 1

2 8

3 0

3 2

3 4

3 6



Ct

va

lue


R 2 : 0 .9 9 2 E f f% : 7 9 .3 1 4

0 .0 0 1 0 .0 1

3 8

4 0

4 2

4 4

4 6



Ct

va

lue


R 2 : 0 .9 3 7 E f f% : 6 1 .2 3 4

0 .0 0 0 1 0 .0 0 1 0 .0 12 8

3 0

3 2

3 4

3 6

3 8



Ct

va

lue


R 2 : 0 .9 7 5 E f f% : 9 8 .5 4 1

0 .0 0 0 1 0 .0 0 1 0 .0 1

2 8

3 0

3 2

3 4

3 6

S ta n d a rd c u r v e N 6 0 7 t (1 4 ;1 9 ) (2 )


Ct

va

lue


R 2 : 0 .9 8 6 E f f% : 7 9 .4 5 5

Standard curves of the different targets (t(1;14), t(1;14)(2), t(1;17), t(8;14), t8;14)(2), t(8;14)(3), t(8;17), t(14;19), t(14;19)(2) tested for N607. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.

Patient-specifi c DNA MRD markers

2

51


Standard Curves N600

100 10 1 0.1

16

18

20

22

24

26

28

30

32

Del 17

t(4;19)

Dup 5

t(2;6)

Del 4

Del 14Inv 5

Del X

Ct

32A

N600

Primary tumor Bone marrow diagnosis

10

100

1000

10000

100000

Del 17

t(4;19)

Dup 5

t(2;6)

Del 4

Del 14Inv 5

Alb

Del XNum

ber o

f cop

ies

B

Standard Curves N600

100 10 1 0.1

16

18

20

22

24

26

28

30

32

Del 17

t(4;19)

Dup 5

t(2;6)

Del 4

Del 14Inv 5

Del X

Ct

A

N600

Primary tumor Bone marrow diagnosis

10

100

1000

10000

100000

Del 17

t(4;19)

Dup 5

t(2;6)

Del 4

Del 14Inv 5

Alb

Del XNum

ber o

f cop

ies

100000B

PCR amplifi ed products of the 10 diff erent targets were inserted into a plasmid vector using TOPO TA Clon-ing kit (Invitrogen), according to the instructions of the manufacturer. Sequencing was performed to con-fi rm cloning of the correct PCR product. For 1 target cloning could not be performed successfully.For the remaining targets the total number of copies was calculated and diluted to 100.000 copies. 10-fold serial dilutions were made and standard curves were tested using qPCR (supplementary fi gure 4A). Based on these standard curves the number of copies in the primary tumor and bone marrow samples were cal-culated (supplementary fi gure 4B). Similar results were obtained when quantifi cation was done relative to albumin.


0 .0 0 1 0 .0 1

2 8

3 0

3 2

3 4



Ct

va

lue

s lo p e : 3 .0 9 8 Y - in te rc e p t: 2 2 .7 0 2

R 2 : 0 .8 9 5 E ff% : 1 1 0 .2 7 6

0 .0 0 0 1 0 .0 0 1 0 .0 12 4

2 6

2 8

3 0

3 2



Ct

va

lue

s lo p e : 3 .1 3 2 Y - in te rc e p t: 2 0 .0

R 2 : 0 .9 5 5 E ff% : 1 0 8 .6 0 1

0 .0 0 0 1 0 .0 0 1 0 .0 12 4

2 6

2 8

3 0

3 2



Ct

va

lue

s lo p e : 3 .1 3 2 Y - in te rc e p t: 2 0 .0

R 2 : 0 .9 5 5 E ff% : 1 0 8 .6 0 1

Standard curves of the diff erent targets (Dup 8, t(5;14), t(11;17)) tested for N576. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR effi ciency are shown.

Chapter 2

52


0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 4

2 6

2 8

3 0

3 2



Ct

va

lue

s lo p e : -3 .5 6 1 Y - in te rc e p t: 1 9 .5 6

R 2 : 0 .9 7 8 E ff% : 9 0 .9 1

0 .0 0 0 1 0 .0 0 1 0 .0 1

2 4

2 6

2 8

3 0

3 2



Ct

va

lue


R 2 : 0 .9 8 4 E ff% : 1 1 9 .4 4 8

0 .0 0 1 0 .0 1 0 .12 4

2 6

2 8

3 0

3 2



Ct

va

lue


R 2 : 0 .9 8 3 E f f% : 9 9 .9 3 9

Standard curves of the different targets (t(17;22), t(8;22), Dup 2) tested for N540. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.


0 .0 0 0 1 0 .0 0 1 0 .0 12 6

2 8

3 0

3 2

3 4

3 6



Ct

va

lue


R 2 : 0 .9 5 8 E f f% : 9 9 .7 6 4

0 .0 0 0 1 0 .0 0 1 0 .0 12 6

2 8

3 0

3 2

3 4



Ct

va

lue

s lo p e : -3 .0 7 8 Y - in te rc e p t: 2 0 .4 1

R 2 : 0 .9 4 1 E ff% : 1 1 1 .2 8 3

Standard curves of the different targets (t(2;17), t(17;19)) tested for N718. Standard curves from 10 fold se-rial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.


0 .0 0 0 1 0 .0 0 1 0 .0 12 8

3 0

3 2

3 4

3 6



Ct

va

lue


R 2 : 0 .9 1 6 E f f% : 8 0 .8 9 2

0 .0 0 1 0 .0 12 6

2 8

3 0

3 2

3 4



Ct

va

lue

s lo p e : -4 .1 8 Y - in te rc e p t: 1 9 .4 8

R 2 : 0 .9 9 E ff% : 7 9 .4 5 3

0 .0 0 0 1 0 .0 0 1 0 .0 12 4

2 6

2 8

3 0

3 2



Ct

va

lue


R 2 : 0 .9 8 4 E ff% : 1 0 0 .0 3 5

Standard curves of the different targets (Del 14, Dup 9, t(11;17)) tested for N753. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.


2

53


0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 6

2 8

3 0

3 2

3 4

3 6

S ta n d a r d c u r v e N 6 9 1 t (1 ;2 )


Ct

va

lue


R 2 : 0 .9 4 2 E ff% : 1 1 3 .0 8 8

0 .0 0 0 1 0 .0 0 1 0 .0 1 0 .1

2 4

2 6

2 8

3 0

3 2

3 4

3 6


Q u a n tity (c o p ie s )C

t v

alu

e


R 2 : 0 .9 8 6 E f f% : 9 4 .7 3 5

Standard curves of the different targets (t(1;2), t(11;17)) tested for N691. Standard curves from 10 fold serial dilutions of tumor DNA in control DNA are shown. Slope, R2 and PCR efficiency are shown.

Minimal residual disease monitoring in neuroblastoma · Max M van Noesel4, Jan J Molenaar3, Rogier...

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Transcript of Minimal residual disease monitoring in neuroblastoma · Max M van Noesel4, Jan J Molenaar3, Rogier...