High-Throughput Copy Number Profiling by Digital Multiplex Ligation-Dependent Probe Amplification in Multiple Myeloma

Accepted Manuscript High-Throughput Copy Number Profiling by Digital Multiplex Ligation-Dependent Probe Amplification in Multiple Myeloma Szabolcs Kosztolanyi, Richard Kiss, Lilit Atanesyan, Ambrus Gango, Karel de Groot, Maryvonne Steenkamer, Pal Jakso, Andras Matolcsy, Bela Kajtar, Laszlo Pajor, Karoly Szuhai, Suvi Savola, Csaba Bodor, Donat Alpar PII:

S1525-1578(18)30073-4

DOI:

10.1016/j.jmoldx.2018.06.004

Reference:

JMDI 715

To appear in:

The Journal of Molecular Diagnostics

Received Date: 13 February 2018 Revised Date:

9 April 2018

Accepted Date: 19 June 2018

Please cite this article as: Kosztolanyi S, Kiss R, Atanesyan L, Gango A, de Groot K, Steenkamer M, Jakso P, Matolcsy A, Kajtar B, Pajor L, Szuhai K, Savola S, Bodor C, Alpar D, High-Throughput Copy Number Profiling by Digital Multiplex Ligation-Dependent Probe Amplification in Multiple Myeloma, The Journal of Molecular Diagnostics (2018), doi: 10.1016/j.jmoldx.2018.06.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT High-Throughput Copy Number Profiling by Digital Multiplex Ligation-Dependent Probe Amplification in Multiple Myeloma

Szabolcs Kosztolanyi,* Richard Kiss,† Lilit Atanesyan,‡ Ambrus Gango,† Karel de Groot,‡

Szuhai,¶ Suvi Savola,‡ Csaba Bodor,† and Donat Alpar,†§

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Maryvonne Steenkamer,‡ Pal Jakso,§ Andras Matolcsy,† Bela Kajtar,§ Laszlo Pajor,§ Karoly

From the 1st Department of Internal Medicine,* Clinical Center, University of Pecs, Pecs, Hungary; the MTA-SE Lendulet Molecular Oncohematology Research Group,† 1st

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Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary; MRC-Holland, Amsterdam,‡ The Netherlands; the Department of

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Pathology,§ University of Pecs Medical School, Pecs, Hungary; and the Department of Cell and Chemical Biology,¶ Leiden University Medical Center, Leiden, The Netherlands

Footnote: S.K., R.K., and L.A. contributed equally.

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Short running head: digitalMLPA in multiple myeloma

Funding: Supported by the Hungarian National Research, Development and Innovation Office - NKFIH (IDs: K_16 #119950 and NVKP_16-1-2016-0004), the New National Excellence

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Program of the Ministry of Human Capacities (ID: ÚNKP-17-4-III-SE-9), and the Momentum

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Research Grant of the Hungarian Academy of Sciences (ID: LP95021).

Disclosures: L.A., K.G., M.S., and S.S. are employees of the MRC-Holland. The digitalMLPA probemix and reagents were provided by MRC-Holland.

Correspondence: Donat Alpar, MTA-SE Momentum Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, 26 Ulloi Str, H-1085 Budapest, Hungary. e-mail: [email protected]

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Abstract Multiple myeloma (MM) is a genetically heterogeneous disease with diverse clinical outcome.

Copy

number

alterations

(CNAs)

including

whole

chromosome

and

subchromosomal gains and losses are common contributors of the pathogenesis and have

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demonstrated prognostic impact in MM. We tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA), a novel technique combining MLPA and next-generation sequencing, to detect disease-related CNAs. Copy number status at 371

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genomic loci were simultaneously analyzed in 56 diagnostic bone marrow samples which

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were also examined by conventional MLPA and interphase fluorescence in situ hybridization (iFISH). On average, digitalMLPA identified 4.4 subchromosomal CNAs per patient. The increased number of probes as compared to conventional MLPA allowed a detailed mapping of CNAs, especially on chromosome 1 where 24 different patterns were observed in 38

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patients harboring loss(1p) and/or gain(1q). IFISH, MLPA, and digitalMLPA results at loci investigated by multiple methods showed a congruency of 95%. Besides precise characterization of hyperdiploid karyotypes not efficiently achievable by iFISH or MLPA,

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digitalMLPA unraveled 156 CNAs not detected by the other two methods in 45 patients (80%). Additionally, we provide proof-of-principle that digitalMLPA is able to detect known

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point mutations, in this case the BRAF V600E. Our study demonstrates the robustness of digitalMLPA to profile CNAs and to screen point mutations in MM, which could efficiently be utilized in myeloma diagnostics.

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Introduction Multiple myeloma (MM) is currently an incurable malignant bone marrow disorder characterized by unleashed proliferation of plasma B-cells.1,2 The diverse genomic landscape

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of MM has extensively been profiled in large-scale studies using fluorescence in situ hybridization (FISH), single nucleotide polymorphism-array (SNP-array), and next-generation sequencing (NGS).3-11 The presence and combinations of various copy number abnormalities

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(CNAs) were reported to have prognostic significance in MM6,12,13; therefore, development of efficient and rapid methods allowing the comprehensive screening of disease-relevant

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CNAs in clinical diagnostics is of high priority.

Multiplex ligation-dependent probe amplification (MLPA) is an established multiplex PCRbased technique primarily developed for the detection of CNAs.14 The relatively straightforward protocol i) needs only 50ng of DNA input, ii) is able to analyze CNAs at single

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exon resolution, iii) provides results within 24 hours, iv) is applicable even to the analysis of formalin-fixed, paraffin-embedded specimens, and v) has low reagent costs.15,16 In addition, it does not require highly specialized equipment, only a thermocycler and a capillary

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electrophoresis instrument. MLPA has extensively been tested on clinical patient samples in

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various hematological malignancies, such as acute lymphoblastic leukemia, chronic lymphocytic leukemia, follicular lymphoma, chronic myeloid leukemia, myelodysplastic syndrome, and acute myeloid leukemia.17-22 We reported the first MLPA study in MM and the robustness of the approach was later confirmed by other groups.23-25 The number of genomic loci that can simultaneously be analyzed in one conventional MLPA reaction is however, limited to 55 to 60, typically including at least eight silent reference regions which are needed for data normalization and interpretation of the results.15

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ACCEPTED MANUSCRIPT Recently, a novel technique called digitalMLPA has been developed which combines the advantages of NGS and MLPA.26 digitalMLPA works based on the same principles as conventional MLPA with the difference that specific adapters allowing sequencing of the amplified probes on Illumina instruments are attached to the PCR products during the

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protocol. After sequencing, copy number status of the loci of interest are determined by relative read number quantification of the various amplicons. A major advantage of digitalMLPA is the greatly increased number of probes allowing the investigation of

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hundreds of genomic loci in a single reaction. In this study, we explored the power of

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digitalMLPA to detect both subchromosomal and chromosomal CNAs in MM by comparing its performance to iFISH and conventional MLPA. Our results suggest that digitalMLPA is a robust and fast high-throughput technique to detect disease-relevant key CNAs and known point mutations; hence, it could efficiently be included in the routine workflow of myeloma

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diagnostics.

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Patient samples

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Materials and Methods

Diagnostic bone marrow samples from 56 patients diagnosed with multiple myeloma based on the International Myeloma Working Group’s criteria were investigated in this study.27 Immunophenotypic characterization including CD45-PerCP-Cy5.5, CD19-PE-Cy7, CD38-FITC, CD138-APC, and CD56-PE markers was performed by five-color flow cytometry (CyFlow® space, Partec GmbH, Germany). In cases, where plasma cell purity dropped below 20% to 30%, immunomagnetic cell enrichment (BD IMagTM, BD Biosciences, CA) was applied according to the manufacturer’s instructions using CD56 or CD138 antibody depending on 4

ACCEPTED MANUSCRIPT the CD56 expression of the myeloma cells, followed by a second immunophenotyping based on CD38 and CD138 expressions to assess the efficacy of the enrichment. Peripheral blood samples from healthy volunteers and bone marrow samples from patients without malignant disorder were used as negative controls for all genetic analyses. Ethical

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Committee approval and written informed consent from the patients were obtained for the study, which was conducted in accordance with the Declaration of Helsinki.

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digitalMLPA

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Genomic DNA was extracted from fixed cells stored in 70% ethanol using a Biorobot EZ1 system (Qiagen, Valencia, CA), also considering and avoiding potential pitfalls that could have had an adverse effect on the performance of digitalMLPA and MLPA protocols (www.mlpa.com; last accessed February 13, 2018). Forty nanograms of DNA was subjected

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to each digitalMLPA reaction using a test version of the D006 probemix (lot X1-0613), which has recently been developed by the MRC-Holland (Amsterdam, The Netherlands), and provided to collaborating laboratories for testing and validation. The probemix included i)

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268 target probes for regions recurrently altered by CNAs in MM; ii) one probe for the

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specific detection of BRAFV600E mutation; iii) 105 reference probes hybridizing to copy number stable regions, and iv) 128 internal control probes for sample identification, as well as quantity and quality assessment Supplemental Table S1. Reference probes were used for data normalization and together with a subset of the target probes, for the identification of whole chromosome gains and losses. This karyotyping set of probes covered all chromosomes at 194 different loci near the telomere, centromere, or the middle of the arm. digitalMLPA protocol started with mixing sample DNA with unique barcode solution followed by sample denaturation and the addition of digitalMLPA probes with buffer to the

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ACCEPTED MANUSCRIPT mixture. Each probe comprised two or three oligonucleotides with a specific 25 to 50bp hybridizing sequence. Probe oligonucleotides binding to a specific locus were designed to hybridize adjacently; therefore, if perfectly hybridized, could be ligated into a single molecule using ligase-65 enzyme. Each ligated probe was then amplified by a universal

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primer pair compatible with all Illumina sequencing platforms. After amplification, samplespecific products from different reactions were pooled, diluted, denatured, and loaded into a MiSeq v3 standard flow cell (Illumina) for 115bp single-read sequencing.

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After quality assessment of the exported FASTQ files, assignment of the reads to

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digitalMLPA probes and subsequent data analysis were performed in two main consecutive steps using an in-house software (MRC-Holland). First, read number for each probe was normalized by the median read number generated from reference probes hybridizing to usually conservative regions in the same genome (intra-sample normalization). The relative

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read number generated for each probe was then compared to the matching values obtained in all reference samples (inter-sample normalization). The final probe ratio value, called ‘dosage quotient’, was around 1.0 if the region of interest was unaffected by CNA, whereas

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an increased or decreased value indicated the presence and level of gain or loss,

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respectively. Tumor cell purity as measured by flow cytometry was also considered at the interpretation of the results. For example, a dosage quotient of 0.6 was interpreted as mono- or biallelic loss if the plasma cell ratio in the sample was 80% or 40%, respectively. In addition, a normal dosage quotient range (average ± 3SD) was calculated for each digitalMLPA probe based on values measured in the negative control samples. In patient samples, only dosage quotient values falling outside this normal range were considered as losses or gains. CNAs were interpreted as being subclonal if multiple consecutive probes had dosage quotients clearly outside the normal range but without reaching the expected level

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ACCEPTED MANUSCRIPT of monoallelic loss as calculated based on plasma cell purity and also, as compared to other affected loci within the same specimen. Oligonucleotides of the digitalMLPA probe designed to detect V600E mutation of the BRAF kinase were only ligated and thus, the corresponding sequencing reads produced if the point mutation was present in the sample. Detailed

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general laboratory and bioinformatic protocols have recently been published.26

MLPA

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MLPA reactions were performed as previously described.23 Briefly, 150ng input genomic

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DNA was denatured and hybridized using the SALSA P425 version A1 probemix (MRCHolland) containing 42 probes targeting regions affected by recurrent CNAs of prognostic significance in MM: 1p32 (FAF1, CDKN2C, PLPP3 and DAB1), 1p21, 1q21.3 (CKS1B), 1q23.3, 5q31.3, 12p13.31, 13q14 (RB1, DLEU1/DLEU2), 16q12 (CYLD), 16q23 (WWOX), and 17p13

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(TP53) (Table 1). The reactions including negative control samples, were performed according to the vendor’s instructions. The amplified probes were analyzed using an ABI 3730 DNA analyzer (Life Technologies, Bleiswijk, The Netherlands) and GeneMarker v1.95

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software (SoftGenetics, LLC., State College, PA). After intra-sample and inter-sample

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normalizations, copy number status at each locus was estimated as described previously,21 also considering the tumor cell ratio measured by flow cytometry and the variability of probe performance in the negative control samples as mentioned above.

Interphase FISH (iFISH) iFISH was performed on archived cells fixed in Carnoy-solution. Selected DNA loci were in situ stained for visualizing Δ13 (LSI D13S319 SO/13q34 SG), deletion of the TP53 gene (LSI TP53 SO/CEP17 SG), abnormalities of chromosome 5 (LSI D5S23 SG/EGR1 5q31 SO),

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ACCEPTED MANUSCRIPT disruption of the IGH locus (LSI IGH DC BA) and in case of positivity for the latter aberration, for the specific identification of IGH translocations most frequently occurring in MM: LSI IGH/FGFR3 DC DF, LSI IGH/CCND1 XT DC DF, and LSI IGH/MAF DC DF (Vysis, Downers Grove, IL). FISH procedures were performed following the vendor’s recommendations and the

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signal pattern of 200 nuclei were evaluated using Zeiss Axioplan2ie MOT (Metasystems, Altlussheim, Germany) and Zeiss AxioImager A1 microscopes (Carl Zeiss Technika Kft, Budapest, Hungary). For the cell-based detection of loss(1p) and gain(1q), BAC clones

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specific for regions 1p32, 1p21, and 1q21 were selected, labeled, and validated as described

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previously.23 FISH signal patterns were evaluated by following the European Myeloma Network’s recommendations.28 All patient samples were screened by two independent investigators.

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Pyrosequencing

Detection and quantification of V600E substitution in the BRAF kinase gene was performed with the PyroMark Q24 system (Qiagen GmbH, Hilden, Germany) using 100ng input DNA and

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following the vendor’s protocol. Codon 600 was amplified, followed by immobilization of the

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biotinylated PCR products on Streptavidin-coated Sepharose High Performance beads. After bead capture on a vacuum workstation and multiple washing steps, single-stranded DNA suitable for pyrosequencing was generated by denaturation. Sequencing was performed in reverse direction producing CAC wild-type sequence in normal samples and CTC genotype in case of V600E mutation. Results were analyzed using the PyroMark Q24 software.

Droplet Digital PCR

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ACCEPTED MANUSCRIPT Validation of BRAF V600E mutation with an increased sensitivity was achieved by using droplet digital PCR (ddPCR). The reaction was performed with 50 ng input DNA and with commercially available BRAF assays specific for the wild type (dHsaCP2000028) and mutant (dHsaCP2000027) targets, following the manufacturer’s protocol. Droplets were created by

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the QX200 Droplet Generator and reading was completed with the QX200 Droplet Digital PCR (ddPCR) system (Bio-Rad, Hercules, CA). Results were analyzed using the Bio-Rad QuantaSoft software. The BRAFmut allelic burden was determined as fractional abundance

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(FA) based on the percentage ratio between the number of mutant DNA molecules (a) and

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the number of mutant (a) plus wild-type (b) molecules detected

(FA = a/(a+b)).

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Statistical analysis

Congruency between digitalMLPA results and iFISH as well as MLPA data was evaluated with

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Results

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Fisher’s exact test using the SPSS 15.0 software (SPSS Inc., Chicago, IL).

digitalMLPA provided informative results at 372 genomic loci for each patient sample. Two target probes showed a standard deviation of >0.09 across the negative control samples; therefore, have been excluded from the downstream analysis. The laboratory protocol was completed within 24 hours including overnight hybridization of the digitalMLPA probes as well as loading of the pooled libraries into the MiSeq instrument. Using a standard flow cell with v3 chemistry, the 115bp single-read run took approximately 9.5 hours and produced on average 1,169±184 sequencing reads per probe.

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Detection of numerical chromosome aberrations In total, 210 whole chromosome aberrations were identified by digitalMLPA including 65 losses and 145 gains in 47 patients (84%). Monosomy 13 proved to be the most abundant

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alteration with the majority of defects occurring among the non-hyperdiploid cases. The vast majority (94%) of trisomies was observed in the 20 patients displaying hyperdiploid karyotype (Figure 1). In these cases, additional copies of odd-number chromosomes were

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predominantly detected (Supplemental Figure S1) with chromosomes 3 and 9 displaying

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trisomy the most commonly, followed by chromosomes 11, 19, 15, 5, 7, and 21 (Supplemental Figure S2).

Identification of subchromosomal copy number aberrations

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digitalMLPA unveiled 246 subchromosomal CNAs (Supplemental Figure S3 and Supplemental Table S2). Gain(1q) was the most frequently occurring lesion among those detected in more than three patients followed by loss(1p), loss(8p), loss(16q), loss(12p), loss(14q), gain(8q),

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gain(Xq), loss(13q), loss(6q), gain(14q), loss(17p), loss(20p), loss(22q), loss(5q), gain(6p), and

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gain(9q) (Table 2). The average number of CNAs per case was 4.4 (range: 0 to 13) with 3.7 in the hyperdiploid subgroup and 4.8 among the non-hyperdiploid patients. digitalMLPA detected at least one subchromosomal CNA in 53/56 cases (95%) with 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, and 13 aberrations identified in 9, 7, 7, 4, 8, 6, 4, 1, 1, 4, 1, and 1 case, respectively (Supplemental Table S2). In 10 patients, biallelic deletions were observed affecting the CDKN2C, FAF1, BIRC3, TRAF3, CYLD, and TP53 genes in 2, 1, 2, 3, 2, and 1 case, respectively (Supplemental Table S3).

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ACCEPTED MANUSCRIPT Detailed mapping of chromosome 1 alterations digitalMLPA probes covered 44 genomic loci along chromosome 1, with 19 and 25 probes hybridizing to the short and long arm, respectively. Hence, we were able to scrutinize the extension, location, and allelic burden of CNAs affecting chromosome 1 (Figure 2).

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Abnormalities were detected in 38 samples with loss(1p) and gain(1q) occurring in 20 and 33 cases, respectively. Aberrations on both arms were observed in 15 patients. A subset of the probes was only altered on the short and long arm in 15 and seven patients, respectively. In

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the remaining cases, all probes including the ones located near the pericentromeric or

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telomeric regions of the arm demonstrated alterations. Non-contiguous lesions within a chromosome arm were observed in three patients. Subclonal and biallelic loss(1p) occurred in 1 and 2 cases, respectively, whereas gain(1q) resulting in more than three copies as indicated by high dosage quotient values and confirmed by iFISH was detected in 12 cases.

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CNAs with various allelic burdens were detected in patients #21, #25, and #47. On the short arm, deletions most commonly included genes located in regions 1p12 (FAM46C) and 1p21 (DPYD and COL11A1) followed by those in region 1p32 (FAF1 and CDKN2C). Gains most

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frequently affected regions 1q21 (BCL9, ANP32E, MCL1, NUP210L, ADAR, and CKS1B) and Investigating various features of the CNAs along

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1q23 (SLAMF7, NUF2, and PBX1).

chromosome 1, a high variability with 24 distinct patterns in terms of location, allelic burden, and extension was observed.

Comparison of digitalMLPA, MLPA, and iFISH results To validate the novel digitalMLPA method and to assess its performance in the light of assays currently applied in routine diagnostics, digitalMLPA data were compared to conventional MLPA and iFISH results of the 56 patient samples. MLPA analyzed a subset of the aberrations

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ACCEPTED MANUSCRIPT investigated by digitalMLPA but with probes having different ligation sites, and detected 121 specifically targeted aberrations in 49 patients (88%) with Δ13 observed most frequently followed by gain(1q), loss(1p), loss(16q), loss(12p), loss(17p), and loss(5q) (Table 2). iFISH identified 95 unbalanced aberrations with Δ13, gain(1q), loss(1p), loss(17p), and loss(5q)

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detected in 36, 32, 17, 6, and 4 patients, respectively. Loss(12p) and loss(16q) were not analyzed by iFISH; therefore, the performance of digitalMLPA to that of MLPA and iFISH could directly be compared based on 6 and 4 specifically screened abnormalities,

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respectively. Combined comparison of the three methods revealed concordant results at

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319/336 test data points (56 patients x 6 aberrations), corresponding to a congruency of 95% (Figure 3). Systematic pairwise comparison of these methods to detect individual aberrations also confirmed a statistically high congruency (Fisher’s exact test: P < 0.0001, Supplemental Table S4). A major underlying reason for discrepancies was the increased

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number of probes in the digitalMLPA probemix providing higher confidence at CNA calling and covering additional, recurrently altered loci as compared to iFISH and MLPA (eg, ETV6 and CDKN1B on chromosome 12p). A representative example demonstrating the

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concordance of the three methods to detect CNAs is shown in Figure 4.

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digitalMLPA provided comprehensive profiling of numerical chromosome aberrations associated with hyperdiploid karyotype in 20 patients, a dataset not delivered by iFISH and MLPA experiments. In addition, 58 whole chromosome alterations in non-hyperdiploid cases and a total of 156 subchromosomal CNAs in 45 patients including both hyperdiploid and non-hyperdiploid cases were exclusively identified by digitalMLPA, primarily due to its higher number of target sites as compared to iFISH and MLPA. On the other hand, iFISH identified 28 balanced chromosome aberrations, outside the technical possibilities of MLPA and digitalMLPA, including IGH translocations with FGFR3-MMSET, CCND1, c-MAF, and unknown

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ACCEPTED MANUSCRIPT partners in 8, 10, 4, and 6 cases, respectively. An overview of the most frequent aberrations detected by the three methods is shown in Table 2, whereas a detailed summary of all aberrations identified among the 56 patients is provided in Supplemental Table S2.

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Screening for BRAFV600E mutation

A probe specifically designed to cover exon 15 in the BRAF gene and to produce PCR products exclusively if mutation c.1799T>A (p.V600E) is present was included in the D006

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digitalMLPA probemix. BRAF V600E mutation was detected by digitalMLPA in two patients

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(#51 and #55) whereas pyrosequencing could unambiguously validate the positivity only in patient #51. Since presence of the mutation was confirmed in both patients in an independent digitalMLPA reaction, mutation screening was also performed in patient #55 using ddPCR which by providing higher sensitivity, corroborated the digitalMLPA result

Discussion

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(Supplemental Figure S4 and S5).

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Multiple myeloma has a heterogeneous genomic landscape and diverse clinical outcome.

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Several studies focused on finding associations between these two phenomena to efficiently stratify patients into risk groups and predict response to particular therapies with an ultimate goal to improve personalized treatment strategies. Our knowledge about the disease-related genetic lesions has significantly expanded, largely due to extensive molecular cytogenetic studies and the advent of high-throughput sequencing technologies.29 Hyperdiploidy and IGH translocations represent primary genetic events in MM and form a well-established backbone of the biological classification by determining the two major genetic subgroups.30 Hyperdiploid status in general is associated with a more favorable 13

ACCEPTED MANUSCRIPT outcome but not all trisomies seem to display the same prognostic impact11; therefore, profiling the concomitantly occurring chromosome duplications across the whole chromosome set has its own value. In addition, a massive but comprehensively characterized set of subchromosomal CNAs and single nucleotide variants diversifying the genomic

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landscape of the disease has been unraveled providing several informative prognostic indicators and a couple of predictive genetic markers for clinical practice.3,6,12,31,32 Recommended screening tests for some of these alterations such as loss(17)(p13) and

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gain(1)(q21) have been incorporated into the recommendation guidelines of the

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International Myeloma Working Group.33,34

In this study, the performance of the novel digitalMLPA technique was tested with NGS readout to detect recurrent numerical chromosome alterations, subchromosomal CNAs and a point mutation in MM. Although, the median number of aberrations detected per patient

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was lower as compared to previous results gained with less targeted techniques, frequencies of the identified abnormalities were mostly in the range observed by large-scale genomewide studies.8,35,36 Of note, digitalMLPA detected at least one genetic aberration in all but

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one patient (Supplemental Table S2). The vast majority of whole chromosome changes and

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63% (156/246) of the subchromosomal CNAs revealed by digitalMLPA were not detected by MLPA or iFISH due to the limitations in coverage of these conventional methods as described previously. In addition to all prognostically relevant CNAs, digitalMLPA analyzed aberrations of therapeutic targets as for example MCL1 and SLAMF7 as well as predictive markers such as CRBN loss adversely influencing treatment response to immunomodulatory drugs.31 Furthermore, digitalMLPA identified activating V600E mutation of the BRAF kinase in two samples demonstrating the potential and versatile application of the method, including the detection of targetable point mutations.37

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ACCEPTED MANUSCRIPT Biallelic inactivation of recurrently altered tumor suppressor genes has recently been suggested as a key driver mechanism of disease progression in MM.38,39 With the D006 digitalMLPA probemix, homozygous losses of CDKN2C/FAF1, BIRC3, TRAF3, and TP53 were detected which were reported to be affected by biallelic deletions in MM and being enriched

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at the time of relapse. Notably, homozygous deletions in CYLD which was found in a patient in a previous study40 but was not analyzable by the targeted FoundationOne Heme panel in a recent study38 could also be identified.

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The nearly 400 probes in the digitalMLPA probemix i) allowed the investigation of

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genomic positions not analyzable with the currently available off-the-shelf MLPA and iFISH kits; ii) provided additional confidence to call CNAs at loci which were analyzed by MLPA but with a significantly lower number of probes; and iii) provided the opportunity to scrutinize aberrations with higher resolution. For example, chromosome 1 was covered with roughly as

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many digitalMLPA probes as the number of all target probes in the P425 MLPA probemix allowing us to uncover a high level of heterogeneity in terms of location, extension, and allelic burden of CNAs. This latter finding is not only biologically interesting; besides the

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biallelic deletions on arm 1p mentioned above, CKS1B (1q21) amplification, as compared to gain of this gene, was reported to have negative impact on the survival of patients treated in

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the MRC Myeloma IX trial,24 indicating a profound clinical relevance of differential allelic burden quantification.

From technical point of view, the average number of generated sequencing reads per digitalMLPA probe exceeded 1,000 in our study, thus providing sufficient data for precise read quantification and downstream CNA assessment. Reliable CNA detection is actually feasible with only approximately 600 reads/digitalMLPA probe on average26; therefore, over 70 test samples excluding reference controls can simultaneously be analyzed in a single

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ACCEPTED MANUSCRIPT MiSeq run using the D006 probemix containing ~500 myeloma specific and experimental control probes. This allows the rapid screening of large archives for research purposes whereas in a diagnostic laboratory where the turn-around time is usually of high importance, flow cells with lower data output might provide the best choice (eg, MiSeq Standard v2: 42

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samples and MiSeq Micro v2: 10 samples). Alternatively, digitalMLPA libraries can be pooled and sequenced with other amplicon or hybrid-capture based targeted libraries designed for comprehensive detection of single nucleotide variants and indels.

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The technical repertoire typically implemented in clinical diagnostic workflows to profile

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the genetic/genomic background of patients with MM includes one or more of the following methods: DNA index measurement, karyotyping, FISH, array-based methods, and/or NGS. DNA index only provides information about the ploidy status without specifically identifying gained or lost chromosomes. Karyotyping gives an overview of the entire chromosome set

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but it is time-consuming, has profoundly limited genomic resolution (3 to 10Mb), and all negative results need cautious interpretation due to the potential under-representation of plasma cells conferred by their typically slower proliferation rate as compared to other cell

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compartments in the same bone marrow aspirate. Therefore, iFISH is currently the gold

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standard method to detect cytogenetic abnormalities in MM. However, the number of genetic loci that can simultaneously be analyzed with this technique is constrained to 4 or 5 due to spectral limitations and diagnostic iFISH tests usually have a resolution between 100kb and 1Mb, potentially hampering the detection of small CNAs. Comparatively, the number of simultaneously analyzable genomic targets is two orders of magnitude higher with digitalMLPA, allowing a comprehensive copy number profiling down to single exon level (average hybridizing sequence length in the reported assay: 70bp, range: 59 to 88bp) as well as sensitive point mutation detection in a single reaction. Compared to molecular array

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ACCEPTED MANUSCRIPT approaches, such as array comparative genomic hybridization or SNP-array, digitalMLPA is more scalable, currently allowing the pooled end-to-end analysis of 192 samples in 36 hours using NextSeq instrument (Illumina) for sequencing. Owing to its specific probe composition, digitalMLPA allows both high-resolution scrutiny of genomic driver regions typically affected

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in MM and a comprehensive detection of aneusomies and large CNAs. Therefore, besides providing genome-wide information, it is more targeted and disease adapted than commercially available array tests. As compared to other, genome-wide NGS applications

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commonly aiming to infer CNAs, such as whole-genome and exome sequencing, digitalMLPA

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provides an increased throughput coupled with a highly rationalized sequencing power requirement thus offering a more economical diagnostic solution with computationally less demanding data processing and evaluation. Considering the genomic landscape of MM in terms of somatic mutation and CNA prevalence, this entity seems to be located in the middle

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of the scale with having more aberrations than several other hematological cancers but fewer than the majority of solid tumors.41 Therefore, it provides a plausible niche for highly multiplexed, still targeted approaches to screen genetic driver alterations in individual

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patient samples in routine clinical diagnostics. Along this line, co-segregation of an increased number of known prognostically adverse CNAs was found to be associated with inferior

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clinical outcome6,12; and digitalMLPA assay is well suited to provide this kind of prognostic information in one reaction. Besides its advantages, digitalMLPA also has a number of limitations, such as the inability to detect copy number neutral rearrangements, to provide information at single-cell level or to distinguish subclonal biallelic losses from clonal monoallelic deletions in all cases. Similar to other diagnostic techniques, digitalMLPA is also sensitive to very low plasma cell purity; thus, the quantification of plasma cell ratio, potential inclusion of enrichment steps, and the

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ACCEPTED MANUSCRIPT use of high-quality DNA in the lower range is highly beneficial. In rare cases, decrease of the dosage quotient of a particular probe may not reflect the presence of a CNA; mutations located near the ligation site hindering the probe annealing can also be causal. To prevent related misinterpretations, multiple probes in close genomic proximity are usually applied. In

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ambiguous cases, validation of the results with a second method is recommended.

To our knowledge, this is the first paper on the application of digitalMLPA in MM. Our results suggest the novel method is reliable and can provide comprehensive profiling of

SC

disease-related unbalanced genetic aberrations as well as detect specific point mutations

M AN U

with a short turn-around time. Based on these features, digitalMLPA could represent a valuable addition to diagnostic methods currently used for the genetic characterization of MM. The input requirement (≥20ng DNA) of the assay is in the range of other low-input NGS protocols and the number of probes used in a single reaction can theoretically be further

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extended. This inherent flexibility of the assay guarantees that digitalMLPA will be able to keep pace with any potential expansion of the panel comprising driver alterations with prognostic and predictive relevance in MM. Performance of digitalMLPA should certainly be

EP

confirmed by further independent studies before its widespread implementation in the

AC C

routine clinicopathological workflow.

18

ACCEPTED MANUSCRIPT Acknowledgements We thank Agnes Lacza and Maria Kneif for their excellent technical assistance. S.S. initiated and supervized the development of the D006 digitalMLPA assay. D.A. designed the study experiments. C.B. and D.A. supervized the study. S.K. provided patient samples.

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P.J., B.K., and L.P. provided flow cytometry data as well as the pathological diagnoses. L.A., M.S., and S.S. designed and developed the digitalMLPA D006 assay. K.G. analyzed raw data from digitalMLPA results. S.K., R.K., L.A., A.G., P.A.K., K.G., M.S., S.S., B.K., K.S., C.B., and D.A.

SC

generated and/or analyzed experimental data. S.K. and D.A. wrote the paper. All authors

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EP

TE D

M AN U

critically reviewed and approved the final draft of the manuscript.

19

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HE;

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Report

from

the

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ACCEPTED MANUSCRIPT 39. Thanendrarajan S, Tian E, Qu P, Mathur P, Schinke C, van Rhee F, Zangari M, Rasche L, Weinhold N, Alapat D, Bellamy W, Ashby C, Mattox S, Epstein J, Yaccoby S, Barlogie B, Hoering A, Bauer M, Walker BA, Davies FE, Morgan GJ: The level of deletion 17p

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M,

Valdez

R,

Promiscuous mutations

Trent

J,

Stewart

AK,

Carpten

M AN U

Barrett

SC

Mancini C, Brents LA, Kumar S, Greipp P, Dispenzieri A, Bryant B, Mulligan G, Bruhn L,

activate

J,

Bergsagel

the noncanonical

PL: NF-

kappaB pathway in multiple myeloma. Cancer Cell 2007, 12:131-144. 41. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al:

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Signatures of mutational processes in human cancer. Nature 2013, 500:415-421.

27

ACCEPTED MANUSCRIPT Table 1. MLPA probes included in the SALSA MLPA P425-A1 probemix.

Target Regions

Partial sequence (24 nucleotides

Probe length

adjacent to the ligation site)

(bp)

Gene / exon

FAF1, exon 4

5’-GGACCTGCATTT-AATCCAGCAAGT-3’

287

1p32.3

CDKN2C, exon 3

5’-TGCTGGAGTTTC-AAGCTGATGTTA-3’

172

1p32.2

PPAP2B, exon 1

5’-CCCCTTGGACTT-TAGAACGATTTA-3’

161

1p32.2

DAB1, exon 14

5’-CACAAACTGTTA-TGCCTTTGCCAG-3’

362

1p32.2

DAB1, exon 2

5’-GACGATTCCTGA-CTCGTGGCCCCG-3’

436

1p31.3

LEPR, exon 4

5’-TGCTTTCGGAGT-GAGCAAGATAGA-3’

196

1p31.2

RPE65, exon 14

5’-CAGAATCAGGAG-ATAAGCAGGCTT-3’

148

1p21.3

DPYD, exon 5

5’-TATGCCACTGAA-GAGGGACCCATT-3’

208

1p21.1

COL11A1, exon 45

5’-TTTCAGGGTGAA-ATTGGTGAGCCG-3’

306

1q21.3

CKS1B, exon 1

5’-TTTGGCCGCTGA-GGGCACAAGGAA-3’

244

1q21.3

CKS1B, exon 2

5’-TTCTGTTACAGA-CATGTCATGCTG-3’

221

1q23.3

NUF2, exon 1

5’-CTGCAGACAGAC-GCCTTCTCCATC-3’

256

1q23.3

NUF2, exon 14

5’-AGCAGAGGACTC-CTATGCTAAGAT-3’

262

1q23.3

RP11-541J2

5’-ATCGTTGAACCA-CACGCTCCTGAC-3’

215

RP11-541J2

5’-TTTTCTTGTTGA-TGGCAGACTTGG-3’

400

SC

M AN U

TE D

EP

AC C

1q23.3

RI PT

1p32.3

1q23.3

RP11-541J2

5’-GAACATCCCATA-ATGGATTTGAAG-3’

321

1q23.3

RP11-541J2

5’-CCCTCATCCCTA-CCCTAGAGTCAC-3’

280

1q23.3

RP11-480N10

5’-CTCTGAGAATCT-CCTCAAGAAGCC-3’

154

1q23.3

PBX1, exon 9

5’-ACTGGAGGTCGA-AGCAATCAGCAA-3’

332

5q31.3

PCDHA1, exon 1

5’-GTATACAGAGTC-CACTTGTTAGAG-3’

454

5q31.3

PCDHAC1, exon 1a

5’-GGGACTGTGTTA-TTCCGAGTTCAA-3’

409

28

ACCEPTED MANUSCRIPT PCDHB2, exon 1

5’-ATCCCAGTATCA-GCGAGATACGGG-3’

483

5q31.3

PCDHB10, exon 1

5’-TGGCTGTAACCA-ACTAGGAAATAA-3’

184

5q31.3

SLC25A2, exon 1

5’-ACAGCAGGAAGA-TGATGATGAAAC-3’

227

5q31.3

PCDHGA11, exon 1

5’-CACAACCAACCA-GCTCGAGAAACC-3’

340

12p13.31

CD27, exon 3

5’-CCATCACTGCCA-ATGCTGAGTGTG-3’

142

12p13.31

VAMP1, exon 4

5’-CTCCTGTTCTGA-GGAAGTGGGGCT-3’

179

12p13.31

NCAPD2, exon 2

5’-CCTGTAGGAGTA-GAATGGCTCCCC-3’

292

12p13.31

NCAPD2, exon 32

5’-GCACAGATCCTA-GGAAGTCTGTTC-3’

427

12p13.31

CHD4, exon 40

5’-ACCACCTCCACC-GCTGAGCAGTGA-3’

355

12p13.31

CHD4, exon 2

13q14.2

RB1, exon 7

13q14.2

RB1, exon 27

13q14.3

DLEU2, exon 2

13q14.3

DLEU1, down

5’-CCTTTTAATAGG-ATCTCTCCTGGA-3’

378

16q12.1

CYLD, exon 2

5’-TGGTTCTACACA-GCCACCCGGAGT-3’

472

16q12.1

CYLD, exon 19

5’-AGGCTGAATCAT-AAATATAACCCA-3’

268

5’-AACCACCAAGGA-CGGCTGGGTTTA-3’

347

SC

M AN U

5’-ATATGGATGCAC-TTTTGAACAACA-3’

299

5’-TTCATTTCAGTT-AATGCTATGTGT-3’

232

5’-ACACGAATGCAA-AAGCAGAAAATG-3’

462

5’-CGCATGCGTAAA-AATGTCGGGAAA-3’

190

TE D

EP

WWOX, exon 1a

AC C

16q23.1

RI PT

5q31.3

16q23.1

WWOX, exon 8

5’-GGCCTGGAGACC-ACCTTTCAAGTG-3’

385

17p13.1

TP53, exon 11

5’-TTCCGAGAGCTG-AATGAGGCCTTG-3’

391

17p13.1

TP53, exon 8

5’-CTGTCCTGGGAG-AGACCGGCGCAC-3’

136

17p13.1

TP53, exon 5

5’-CAAGATGTTTTG-CCAACTGGCCAA-3’

251

PEX13

5’-TGAGGATGACCA-TGTAGTTGCCAG-3’

499

Reference Regions 2p16.1

29

ACCEPTED MANUSCRIPT ZEB2

5’-ATGTGACAAGAC-ATTCCAGAAAAG-3’

313

4q25

CFI

5’-ATTCTGACTGCT-GCACATTGTCTC-3’

166

6p12.2

PKHD1

5’-TCTCAAGCTGAT-TCTGGAACGGCT-3’

274

8q13.3

EYA1

5’-ATGGCACAAGCT-TCAGTACCCCTC-3’

238

10p13

OPTN

5’-TGAGGATGGTCA-TGGTTTCCAGGT-3’

418

10q11.2

DKK1

5’-ACTCGGTTCTCA-ATTCCAACGCTA-3’

370

18p11.2

RNMT

5’-TACAATGAACTT-CAGGAAGTTGGT-3’

490

18q11.2

NPC1

5’-GACGAGTCTGTG-GATGAGGTCACA-3’

122

21q21.1

USP25

5’-CTGTGAGCGATT-TGCCCGAATCAT-3’

202

22q12.3

LARGE

M AN U

SC

RI PT

2q22.3

5’-GTGGCCATGAAG-CACATCAGCACT-3’

130

Note: RP11-541J2 and RP11-480N10 are BAC clones that span this particular region. These probes are located in a very gene-poor region. The nearest gene, starting at a distance of

AC C

EP

TE D

approximately 100 kb telomeric from the 154 nt probe, is PBX1.

30

ACCEPTED MANUSCRIPT Table 2. Frequency of recurrent genetic abnormalities detected by iFISH, MLPA, and digitalMLPA. iFISH

MLPA

digitalMLPA iFISH + MLPA + digitalMLPA

t(4;14)(p16;q32)

14%

NA

NA

14%

t(11;14)(q13;q32)

18%

NA

NA

18%

t(14;16)(q32;q23)

7%

NA

NA

t(14;?)(q32;?)

11%

NA

NA

hyperdiploidy

29%

30%

36%

loss(1p)

30%

34%

36%

gain(1q)

57%

59%

loss(3p)

NA

NA

loss(4p)

NA

NA

loss(5q)

5%

7%

gain(6p)

NA

loss(6q)

NA

loss(8p)

NA

7%

M AN U

SC

11%

36%

36%

59%

4%

4%

5%

5%

5%

7%

NA

7%

7%

NA

14%

14%

NA

25%

25%

NA

20%

20%

TE D

59%

EP NA

AC C

gain(8q)

RI PT

Abnormality

gain(9q)

NA

NA

7%

7%

loss(11q)

NA

NA

5%

5%

gain(11q)

NA

NA

5%

5%

loss(12p)

NA

18%

23%

25%

Δ13

64%

63%

63%

66%

loss(14q)

NA

NA

21%

21%

31

ACCEPTED MANUSCRIPT NA

NA

14%

14%

loss(16q)

NA

21%

25%

25%

loss(17p)

11%

11%

11%

14%

loss(17q)

NA

NA

4%

4%

gain(17q)

NA

NA

4%

4%

loss(18p)

NA

NA

5%

loss(20p)

NA

NA

11%

loss(20q)

NA

NA

5%

loss(22q)

NA

NA

9%

-X

NA

NA

gain(Xq)

NA

NA

-Y

NA

NA

RI PT

gain(14q)

5%

11%

M AN U

SC

5% 9%

25%

25%

18%

18%

9%

9%

TE D

iFISH, interphase fluorescence in situ hybridization; MLPA, multiplex ligation-dependent

AC C

EP

probe amplification; NA, not analyzed.

32

ACCEPTED MANUSCRIPT Figure Legends Figure 1. Overview of numerical chromosome aberrations detected by digitalMLPA. Patients are clustered based on genetic subgroups of MM, indicated with black bars, as determined by iFISH, MLPA, and digitalMLPA. Whole-chromosome aberrations were

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identified by a karyotyping subset of the probes covering all chromosomes near the telomere, centromere, or the middle of the arm. HD: Hyperdiploidy; S: digitalMLPA dosage

SC

quotient indicated subclonal alteration.

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Figure 2. Patterns of chromosome 1 abnormalities as detected by digitalMLPA. Forty-four loci along chromosome 1 were analyzed. Loss(1p) and/or gain(1q) with 24 different patterns were detected in 38 patients harboring chromosome 1 abnormalities (68%). B: biallelic loss based on significantly decreased level of dosage quotient and with iFISH confirmation; M:

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digitalMLPA dosage quotient indicated the gain of multiple copies with iFISH confirmation at a subset of loci; S: digitalMLPA dosage quotient indicated subclonal alteration.

EP

Figure 3. Congruency of iFISH, MLPA, and digitalMLPA results. Investigation of loss(1p),

AC C

gain(1q), loss(12p), Δ13, loss(16q), and loss(17p) provides concordant results in 95%, 98%, 91%, 95%, 96%, and 95% of the cases, respectively. Values indicate the number of patients showing positivity for the particular aberration. Loss(12p) and loss(16q) were not analyzed by iFISH.

Figure 4. Multiple copy number abnormalities detected by digitalMLPA, MLPA, and iFISH in a demonstrative patient sample (#16). digitalMLPA reveals a gain including the whole long arm of chromosome 1 (analyzed loci: 1q21.1-1q44) as well as losses of whole chromosomes

33

ACCEPTED MANUSCRIPT (13, 14 and Y) and various subchromosomal segments (3p, 5q, 12p, 16q, and 17p). A biallelic loss including TP53 is observed on the short arm of chromosome 17. MLPA results obtained by investigating a subset of the loci show congruency with digitalMLPA data. Notably, digitalMLPA detects loss(12p) in a region comprising ETV6 and CDKN1B, not covered by

RI PT

MLPA. Gain(1q), loss(5q), monosomy 13, and loss(17p) are also unveiled by two-color FISH probe sets. Biallelic loss(17p) is however, not revealed by iFISH, most likely due to the small size of the deletion on one of the alleles. Although the corresponding FISH probe covers a

SC

region of 172kb within 17p13, the biallelic loss is only detected by digitalMLPA in a 10kb

M AN U

region within the TP53 gene. For the analysis of chromosome 1, FISH probes were generated for loci 1p21 (BAC clone RP11-421L21; closest gene analyzed by digitalMLPA and MLPA: COL11A1) and 1q21 (BAC clone RP11-307C12, covering gene CKS1B). BAC clones were labeled with nick translation using biotin or digoxigenin dUTP and visualized by avidin-FITC

TE D

or antidigoxigenin-rhodamine. Chromosomes 5, 13, and 17 were investigated by commercially available Vysis probe kits (Abbott Laboratories, IL). iFISH signal patterns with 4',6-diamidino-2-phenylindole counterstain were evaluated using a MetaSystems MOT2

AC C

EP

microscope system (objective: 63x).

34

Supplemental Table S3. Biallelic deletions detected by digitalMLPA. Patient ID Chromosome arm Genes involved #21 1p CDKN2C #47 1p FAF1, CDKN2C #25 11q BIRC3 #35 11q BIRC3 #23 14q TRAF3 #40 14q TRAF3 #51 14q TRAF3 #32 16q CYLD #50 16q CYLD #16 17p TP53

AC C

EP

TE D

M AN U

SC

CDKN2C - Cyclin dependent kinase inhibitor 2C; FAF1 - Fas associated factor 1; BIRC3 - Baculoviral IAP Repeat Containing 3; TRAF3 - TNF Receptor Associated Factor 3; CYLD - CYLD Lysine 63 Deubiquitinase; TP53 - Tumor Protein P53.

RI PT

ACCEPTED MANUSCRIPT

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0

9

4

35

36

0

23

1

42

0

RI PT

33

loss(17p)

negative positive

negative positive

negative

0

12

2

5

1

21

0

42

1

49

17

negative 0

Fisher’s exact test: P < 0.0001 * Not analyzed by iFISH.

3

32

1

NA

36

0

23

NA

P < 0.0001

M AN U

P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 iFISH positive negative positive negative positive negative positive negative positive negative positive negative NA

34

1

NA

NA

5

1

NA

2

19

NA

NA

1

49

TE D

positive

1

loss(16q)*

SC

negative 0

Fisher’s exact test:

digitalMLPA

19

NA

EP

positive

AC C

digitalMLPA

Supplemental Table S4. Comparison of digitalMLPA results to MLPA and iFISH data. loss(1p) gain(1q) loss(12p)* Δ13 MLPA positive negative positive negative positive negative positive

P < 0.0001

NA

P < 0.0001

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT