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The combination of WGS and RNA-Seq is superior to conventional diagnostic tests in multiple myeloma: Ready for prime time?

Open AccessPublished:January 10, 2020DOI:https://doi.org/10.1016/j.cancergen.2020.01.001

      Highlights

      • Assessing translocations involving the IGH locus we observed a 96% concordance of FISH (fluorescence in situ hybridization) and WGS results.
      • Assessing copy number alterations (CNA) we observed and a 92% overall concordance of FISH and WGS.
      • WGS analysis resulted in the identification of 17 additional MYC-translocations that were missed by the initial FISH based workup.
      • RNA-Seq followed by supervised clustering grouped patients in their expected genetically defined subgroup and prompted the assessment of WGS data in cases that were not congruent with conventional FISH.
      • The simultaneous analysis of CNA, SV and small nucleotide variants (SNV) allowed the evaluation of mutations in relation to common cytogenetic findings.

      Abstract

      The diagnosis and risk stratification of multiple myeloma (MM) is based on clinical and cytogenetic tests. Magnetic CD138 enrichment followed by interphase FISH (fluorescence in situ hybridisation) is the gold standard to identify prognostic translocations and copy number alterations (CNA). Although clinical implications of gene expression profiling (GEP) or panel based sequencing results are evident, those tests have not yet reached routine clinical application. We set up a single workflow to analyse MM of 211 patients at first diagnosis by whole genome sequencing (WGS) and RNA-Seq and validate the results by FISH analysis. We observed a 96% concordance of FISH and WGS results when assessing translocations involving the IGH locus and an overall concordance of FISH and WGS of 92% when assessing CNA. WGS analysis resulted in the identification of 17 additional MYC-translocations that were missed by FISH analysis. RNA-Seq followed by supervised clustering grouped patients in their expected genetically defined subgroup and prompted the assessment of WGS data in cases that were not congruent with FISH. This allowed the identification of additional IGH-translocations and hyperdiploid cases. We show the reliability of WGS an RNA-Seq in a clinical setting, which is a prerequisite for a novel routine diagnostic test.

      Keywords

      Introduction

      Multiple myeloma (MM) is a malignant plasma cell (PC) disorder with broadly varying clinical symptoms and outcome [
      • Brenner H.
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      • Pulte D.
      Recent major improvement in long-term survival of younger patients with multiple myeloma.
      ]. The international scoring system (ISS) is a widely used model for clinical prognostication which is based on b2-microglobulin and albumin to identify 3 risk groups in newly diagnosed patients [
      • Greipp P.R.
      • San Miguel J.
      • Durie B.G.
      • Crowley J.J.
      • Barlogie B.
      • Blade J.
      • et al.
      International staging system for multiple myeloma.
      ]. Cytogenetic testing by fluorescence in situ hybridisation (FISH) following magnetic CD138 enrichment yields important prognostic information and is recommended by the International Myeloma Working Group and the European Myeloma Network [
      • Sonneveld P.
      • Avet-Loiseau H.
      • Lonial S.
      • Usmani S.
      • Siegel D.
      • Anderson K.C.
      • et al.
      Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group.
      ,
      • Caers J.
      • Garderet L.
      • Kortum K.M.
      • O’Dwyer M.E.
      • van de Donk N.
      • Binder M.
      • et al.
      European Myeloma Network recommendations on tools for the diagnosis and monitoring of multiple myeloma: what to use and when.
      . The minimal requirement is the assessment of del(17p) and t(4;14) at diagnosis but analyzing gains in 1q, del(1p), t(14;16) and t(14;20) are recommended. Translocations involving the IGH locus are prognostic [
      • Morgan G.J.
      • Walker B.A.
      • Davies F.E.
      The genetic architecture of multiple myeloma.
      ,
      • Sawyer J.R.
      The prognostic significance of cytogenetics and molecular profiling in multiple myeloma.
      and have been shown to predict response to treatment as a predictive biomarker [
      • Avet-Loiseau H.
      • Leleu X.
      • Roussel M.
      • Moreau P.
      • Guerin-Charbonnel C.
      • Caillot D.
      • et al.
      Bortezomib plus dexamethasone induction improves outcome of patients with t(4;14) myeloma but not outcome of patients with del(17p).
      ,
      • Touzeau C.
      • Dousset C.
      • Le Gouill S.
      • Sampath D.
      • Leverson J.D.
      • Souers A.J.
      • et al.
      The bcl-2 specific BH3 mimetic ABT-199: a promising targeted therapy for t(11;14) multiple myeloma.
      ,
      • Kumar S.
      • Kaufman J.L.
      • Gasparetto C.
      • Mikhael J.
      • Vij R.
      • Pegourie B.
      • et al.
      Efficacy of venetoclax as targeted therapy for relapsed/refractory t(11;14) multiple myeloma.
      ]. Moreover del(17p) and +1q are associated with inferior outcome and other copy number alterations (CNA) impact prognosis [
      • Avet-Loiseau H.
      • Li C.
      • Magrangeas F.
      • Gouraud W.
      • Charbonnel C.
      • Harousseau J.L.
      • et al.
      Prognostic significance of copy-number alterations in multiple myeloma.
      ,
      • Avet-Loiseau H.
      • Attal M.
      • Campion L.
      • Caillot D.
      • Hulin C.
      • Marit G.
      • et al.
      Long-term analysis of the IFM 99 trials for myeloma: cytogenetic abnormalities [t(4;14), del(17p), 1q gains] play a major role in defining long-term survival.
      . Novel strategies integrate clinical findings and FISH results to better define a patient's risk at diagnosis [
      • Avet-Loiseau H.
      • Durie B.G.
      • Cavo M.
      • Attal M.
      • Gutierrez N.
      • Haessler J.
      • et al.
      International Myeloma Working G. combining fluorescent in situ hybridization data with ISS staging improves risk assessment in myeloma: an International Myeloma Working Group collaborative project.
      ,
      • Neben K.
      • Jauch A.
      • Bertsch U.
      • Heiss C.
      • Hielscher T.
      • Seckinger A.
      • et al.
      Combining information regarding chromosomal aberrations t(4;14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation.
      . The analysis of large microarray studies have shown that based on their gene expression profiles (GEP) MM patients comprise different subgroups with distinct clinical outcomes [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ,
      • Shaughnessy Jr., J.D.
      • Haessler J.
      • van Rhee F.
      • Anaissie E.
      • Pineda-Roman M.
      • Cottler-Fox M.
      • Hollmig K.
      • Zangari M.
      • Mohiuddin A.
      • Alsayed Y.
      • Grazziutti M.
      • Epstein J.
      • Crowley J.
      • Barlogie B
      Testing standard and genetic parameters in 220 patients with multiple myeloma with complete data sets: superiority of molecular genetics.
      . These results formed the basis to combine molecular findings, FISH results and clinical prognostic factors with a high power to classify patients according to their risk [
      • Kuiper R.
      • van Duin M.
      • van Vliet M.H.
      • Broijl A.
      • van der Holt B.
      • El Jarari L.
      • et al.
      Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.
      ]. In recent years whole genome sequencing (WGS) and targeted sequencing studies have further shed light onto recurrent mutations in MM at diagnosis [
      • Lohr J.G.
      • Stojanov P.
      • Carter S.L.
      • Cruz-Gordillo P.
      • Lawrence M.S.
      • Auclair D.
      • et al.
      Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.
      ,
      • Chapman M.A.
      • Lawrence M.S.
      • Keats J.J.
      • Cibulskis K.
      • Sougnez C.
      • Schinzel A.C.
      • et al.
      Initial genome sequencing and analysis of multiple myeloma.
      ,
      • Bolli N.
      • Avet-Loiseau H.
      • Wedge D.C.
      • Van Loo P.
      • Alexandrov L.B.
      • Martincorena I.
      • et al.
      Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.
      ]. Even the clonal evolution of mutations at relapse has been addressed [
      • Kortum K.M.
      • Mai E.K.
      • Hanafiah N.H.
      • Shi C.X.
      • Zhu Y.X.
      • Bruins L.
      • et al.
      Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and RAS pathway genes.
      ]. Specific mutations are associated with resistance to therapy and could serve as predictive markers in the future [
      • Mulligan G.
      • Lichter D.I.
      • Di Bacco A.
      • Blakemore S.J.
      • Berger A.
      • Koenig E.
      • et al.
      Mutation of NRAS but not KRAS significantly reduces myeloma sensitivity to single-agent bortezomib therapy.
      ]. However, despite clear prognostic and predictive value neither GEP nor panel based sequencing have reached clinical utilization at this point, which is mainly due to high cost and low availability. As WGS and RNA-Seq become more available, integration into routine diagnostics seems feasible. This is especially true as conventional FISH is a labor intensive technique that relies on the yield of PC and their quality following CD138 enrichment. We set up a single workflow to analyse MM at first diagnosis by WGS and RNA-Seq to assess the reliability and additional value of those novel techniques as a framework for a routine diagnostic approach. Based on 211 clinical samples we show that WGS and RNA-Seq are reliable diagnostic tools that can replace conventional FISH diagnostics in close furture. We further provide evidence that our proposed diagnostic workup is superior to conventional diagnostic testing.

      Patients and methods

      All patients gave their written informed consent for scientific evaluations. The study was approved by the Internal Review Board and adhered to the tenets of the Declaration of Helsinki. The cohort comprised 211 cases with MM (116 male, 95 female) diagnosed at our institution between 2011 and 2017. The median age at diagnosis was 67 years (range 26–91 years) and the median bone marrow PC count was 48% (11–97%). All patients were evaluated by FISH following CD138 enrichment as described before [
      • Bacher U.
      • Haferlach T.
      • Kern W.
      • Alpermann T.
      • Schnittger S.
      • Haferlach C.
      Correlation of cytomorphology, immunophenotyping, and interphase fluorescence in situ hybridization in 381 patients with monoclonal gammopathy of undetermined significance and 301 patients with plasma cell myeloma.
      ]. In short: the number of FISH probes being applied to samples depended on the amount of PC obtained following magnetic separation. Screening was performed for the following unbalanced alterations: del(13)(q14), del(17)(p13), +3, +9, +11, and +15, del(1)(p32), +1q21. In addition, FISH was performed for the following reciprocal rearrangements: t(4;14)(p16;q32)/IGH-FGFR3, t(14;16)(q32;q23)/IGH-MAF, and t(11;14)(q13;q32)/IGH-CCND1. In case there was suspicion for another IGH rearrangement, investigation with an IGH/14q32 break-apart probe was added. Where appropriate, additional FISH analysis with probes for the detection of t(6;14)(p21;q32)/IGH-CCND3, t(14;20)(q32;q12)/IGH-MAFB, t(8;14)(q24;q32)/IGH-MYC or MYC rearrangements were carried out. FISH probes were applied in the following sequence to determine the most relevant loci in cases with low numbers of evaluable PC: 13q, 17p, IGH-FGFR3, IGH-MAF, IGH-CCND1, del(1)(p32), +1q21, +3, +9, +11, and +15 (Fig. 1). For a detailed overview of patient characteristics and FISH results see Table 1.
      Fig. 1
      Fig. 1Diagnostic algorithm. Shown is the diagnostic workflow as routinely performed (left) with a specific sequence to apply FISH probes to detect the most important chromosomal aberrations in a diagnostic bone marrow sample of a myeloma patient. The right panel depicts the proposed novel diagnostic approach using a combined WGS and RNA-Seq workflow. The two workflows yield diagnostic information which is analysed and compared in this study.
      Table 1Patient characteristics.
      total n211
      Sex
       Female n (%)95 (45)
       Male n (%)116 (55)
      Age years, median (range)67 (26–91)
      %plasmacells in bone marrow (range)48 (11–97)
      Diagnostic workup
       Cytomorphology n (%)211 (100)
       Immunophenotpying145 (69)
       FISH211 (100)
       t(11;14) analyzed/positive (%)185/53 (28)
       t(4;14)169/24 (14)
       t(14;16)175/6 (3)
       t(6;14)130/7 (5)
      MYC rearranged142/13 (9)
       Hyperdiploid n (%)161/81 (50)
       del(1p32)181/10 (6)
       del(13q)211/102 (48)
       del(17p)211/21 (10)
       +1q21200/87 (44)
      FISH – fluorescence in situ hybridization.

      Whole genome sequencing and RNA-Seq

      WGS was performed for all patients on CD138 purified cells (median 4.3 × 106 per patient, range 2.5–5.2 × 106). DNA and RNA purification yielded a median of 14 µg (range 3–69 µg) and 2.8 µg (range 0.6–10 µg) respectively. For WGS 1 µg DNA served as input, for RNA-Seq 250 ng RNA. WGS libraries were prepared with the TruSeq PCR free library prep kit and 150 bp paired-end sequences were generated on a NovaSeq 6000 or HiSeqX instrument with a median of 100x coverage (Illumina, San Diego, CA). The Illumina whole genome sequencing pipeline (version 5.0) and tumor-normal app (version 3.0) were used for variant calling of small nucleotide variants and structural variants. In brief, WGS reads were mapped to human reference genome (GRCh37) using Isaac aligner [
      • Raczy C.
      • Petrovski R.
      • Saunders C.T.
      • Chorny I.
      • Kruglyak S.
      • Margulies E.H.
      • Chuang H.Y.
      • Kallberg M.
      • Kumar S.A.
      • Liao A.
      • Little K.M.
      • Stromberg M.P.
      • Tanner S.W
      Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms.
      ] (version 03.16.02.19). Structural variants were then called using Manta caller [
      • Chen X.
      • Schulz-Trieglaff O.
      • Shaw R.
      • Barnes B.
      • Schlesinger F.
      • Kallberg M.
      • Cox A.J.
      • Kruglyak S.
      • Saunders C.T
      Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.
      ] (version 0.28.0). Single nucleotide variants and small indels (SNVs) were called using Strelka software [
      • Kim S.
      • Scheffler K.
      • Halpern A.L.
      • Bekritsky M.A.
      • Noh E.
      • Kallberg M.
      • et al.
      Strelka2: fast and accurate calling of germline and somatic variants.
      ] (version 2.4.7). The tumor-normal app used a mixture of genomic DNA from multiple anonymous donors as unmatched-normal controls. To remove potential germline variants, all SNVs with PASS flag were queried against the gnomAD database (https://gnomad.broadinstitute.org/), and variants with global population frequencies >1% where excluded. 47 genes recurrently mutated in MM were selected for evaluation [
      • Kortum K.M.
      • Mai E.K.
      • Hanafiah N.H.
      • Shi C.X.
      • Zhu Y.X.
      • Bruins L.
      • et al.
      Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and RAS pathway genes.
      ]. SNVs in TP53 where queried against the UMD TP53 database [
      • Leroy B.
      • Anderson M.
      • Soussi T.
      TP53 mutations in human cancer: database reassessment and prospects for the next decade.
      ] and only variants reported as (possibly/likely) pathogenic were retained. For all other genes only SNVs reported as (likely) pathogenic in ClinVar [
      • Landrum M.J.
      • Lee J.M.
      • Benson M.
      • Brown G.R.
      • Chao C.
      • Chitipiralla S.
      • et al.
      ClinVar: improving access to variant interpretations and supporting evidence.
      ] were considered. SV calls, were filtered for at least 2 paired read support and for PASS flag. Copy number alterations (CNAs) were called using GATK4 (Broad Institute [
      • Van der Auwera G.
      Somatic variation discovery with GATK4.
      ]).
      For transcriptome analysis total RNA was sequenced on a NovaSeq 6000 system generating a median of 50 million 100 bp paired-end reads per sample. The resulting estimated gene counts were pre-processed and normalized, applying Trimmed mean of M-values normalization method. Normalized counts per million (CPM) were used as a proxy of gene expression in each sample. Gene expression differences were assessed using the edgeR package with false discovery rate correction for multiple testing. Supervised hierarchical clustering was used to group patients according to their gene expression profile into known subgroups of MM. The variant interpreter (Illumina, San Diego, CA) was used to analyze and reevaluate the WGS data for individual cases.

      Results

      Routine diagnostic bone marrow samples of all 211 patients were investigated by FISH analyses following magnetic CD138 separation (Fig. 1). With FISH we detected recurrent translocations in 102/211 patients (48%): 28% t(11;14), 14% t(4;14), 5% t(6;14), 3% (14;16) and 9% MYC-rearranged. 8/102 patients (8%) had 2 translocations all with an additional t(8;14). Deletions of 13q, 17p and gains in 1q were observed in 48%, 10% and 44% respectively. 81/161 patients (50%) were diagnosed with a hyperdiploid karyotype (as defined by ≥3 chromosomal gains). Table 1 gives an overview of clinical characteristics and FISH diagnosis of all analysed patients.
      We used the genome wide information based on WGS data to detect patient specific CNA. Trisomies, tetrasomies and deletions are readily detectable using the variant interpreter software (Illumina, example supplemental fig. 1A). For an integration of WGS into routine diagnostics an automated analysis of WGS data is desirable. To this end we constructed a web based toolbox that is based on published algorithms (MANTA and GATK4) and can identify structural variants (SV), copy number alterations (CNA), single nucleotide variants and small indels (SNV). An example of results yielded for two patients is given in supplemental figure 1B (CNA) and 1C (SV, translocations). Supplemental figure 1D depicts an integrated visualization of SV, CNA and SNV.

      Detection of IGH translocations by WGS

      As translocations involving the IGH locus are common in multiple myeloma and associated with prognosis [
      • Morgan G.J.
      • Walker B.A.
      • Davies F.E.
      The genetic architecture of multiple myeloma.
      ,
      • Sawyer J.R.
      The prognostic significance of cytogenetics and molecular profiling in multiple myeloma.
      and response to treatment [
      • Avet-Loiseau H.
      • Leleu X.
      • Roussel M.
      • Moreau P.
      • Guerin-Charbonnel C.
      • Caillot D.
      • et al.
      Bortezomib plus dexamethasone induction improves outcome of patients with t(4;14) myeloma but not outcome of patients with del(17p).
      ,
      • Touzeau C.
      • Dousset C.
      • Le Gouill S.
      • Sampath D.
      • Leverson J.D.
      • Souers A.J.
      • et al.
      The bcl-2 specific BH3 mimetic ABT-199: a promising targeted therapy for t(11;14) multiple myeloma.
      ,
      • Kumar S.
      • Kaufman J.L.
      • Gasparetto C.
      • Mikhael J.
      • Vij R.
      • Pegourie B.
      • et al.
      Efficacy of venetoclax as targeted therapy for relapsed/refractory t(11;14) multiple myeloma.
      ] our first aim was to assess whether WGS can reproduce the results obtained by FISH analyses. Following alignment and variant calling WGS data was analysed by our semi automated tools as described above. This approach allowed us to detect 98/102 translocations that had been previously identified by FISH resulting in an overall concordance of 96% (Fig. 2 and supplemental Table 1). Specifically we detected 24/24 of t(4;14), 6/7 of t(6;14), 11/12 of t(8;14), 51/53 of t(11;14) and 6/6 of t(14;16) cases by WGS. Moreover, by conventional FISH 12 patients had an IGH (n = 4) or MYC (n = 8) translocation with an unknown partner chromosome. We identified all 12 translocations by WGS. NFKB1 and MYCN were identified as partner genes of IGH in one case each. Fig. 3A gives an overview of the detection of IGH-rearrangements by FISH and WGS and a potential reason for the discrepant results. In the majority of the cases the discrepancies resulted from insufficient material for FISH analyses (i.e. bad quality slides). The t(8;14) was the most common translocation detected by WGS only (8/20, 40%). MYC translocations are prognostically relevant [
      • Walker B.A.
      • Wardell C.P.
      • Brioli A.
      • Boyle E.
      • Kaiser M.F.
      • Begum D.B.
      • et al.
      Translocations at 8q24 juxtapose MYC with genes that harbor superenhancers resulting in overexpression and poor prognosis in myeloma patients.
      ] and we assessed the detection rate for all recurrent MYC rearrangements (t(2;8), t(8;14) and t(8;22)) by FISH analysis compared to WGS. We show that out of 30 MYC rearrangements as detected by WGS 17 were missed by FISH (43%) and an additional 4 (13%) were not analysed due to insufficient material for FISH (Fig. 3B).
      Fig. 2
      Fig. 2Concordance of WGS and FISH results. Circos plot with copy number alterations and structural variations of the 211 patients’ genomes as assessed by WGS and FISH. First two tracks show copy number alterations based on WGS data across chromosomes for all 211 patients. Gains (green) and losses (red) are visible across the entire cohort, specifically trisomies of chromosomes 3, 5, 7, 9, 11, 15 and 19 and deletions on 1p and chromosome 13 are visible. Third and fourth track give a comparison of the read-out by our gold standard FISH panel. While only a small part of the genome is covered the typical gains and deletions are visible. Lines in centrum of circle show detected translocations involving IGH gene. Number of patients with respective translocations detected by WGS out of the number detected by FISH are given along lines.
      Fig. 3
      Fig. 3Detection of IGH rearrangements by FISH and WGS. A) IGH rearrangements as detected by FISH probes and by WGS. For each translocation the number of concordant results of FISH and WGS, the number of WGS detection only and the number of FISH detection only is given. Probable reasons for discordant results are given. B) MYC- translocations as detected by WGS. The corresponding number of positive and negative FISH results is given in relation to translocations detected by WGS.

      Validation of translocations by RNA-Seq

      The typical translocations should all result in the overexpression of the downstream target genes. We analysed the expression of FGFR3, NSD2, MAF, CCND1, CCND2, CCND3 and MYC using the RNA-Seq results obtained for each patient. As expected t(4;14) resulted in an elevated expression of FGFR3 and NSD2, t(14;16) in an elevated expression of MAF, t(11;14) in an elevated expression of CCND1 and t(6;14) in an elevated expression of CCND3 (Fig. 4A). As MAF is a transcriptional regulator of CCND2, t(14;16) also leads to an increased expression of CCND2 (Fig. 4A). Cases harboring IGH-MYC translocations as detected by FISH showed a significantly higher MYC expression as compared to IGH-MYC negative cases (Fig. 4B, left panel), however high MYC expression was also evident in a large number of non-MYC translocated cases. This held true when including the 17 patients with MYC-translocations that were detected by WGS only (Fig. 4B, right panel).
      Fig. 4
      Fig. 4Validation of FISH by RNA Seq. A) The expression of FGFR3, NSD2, MAF, CCND1 and CCND3 are given comparing patients with the corresponding translocation (by FISH analysis) with the patients without the respective translocation. B) The expression of MYC is given comparing patients with MYC translocation t(8;14) by FISH and the patients without MYC translocation (left panel). The MYC expression is given for patients with all recurrent MYC translocations (t(2;8), t(8;14), t(8;22)) as detected by WGS compared to the patients without detectable MYC translocations by WGS (right panel).

      Detection of copy number alterations by WGS

      As typical chromosomal aberrations that impact prognosis of MM are copy number alterations [
      • Avet-Loiseau H.
      • Li C.
      • Magrangeas F.
      • Gouraud W.
      • Charbonnel C.
      • Harousseau J.L.
      • et al.
      Prognostic significance of copy-number alterations in multiple myeloma.
      ,
      • Avet-Loiseau H.
      • Attal M.
      • Campion L.
      • Caillot D.
      • Hulin C.
      • Marit G.
      • et al.
      Long-term analysis of the IFM 99 trials for myeloma: cytogenetic abnormalities [t(4;14), del(17p), 1q gains] play a major role in defining long-term survival.
      • Kumar S.
      • Fonseca R.
      • Ketterling R.P.
      • Dispenzieri A.
      • Lacy M.Q.
      • Gertz M.A.
      • et al.
      Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics.
      , we next assessed the concordance of FISH results and WGS regarding CNA. We were able to identify 679/740 (91.8%) copy number alterations detected by FISH (Fig. 2 and Supplemental Table 2. In detail these were 100/103 del(13q), 17/21 del(17p), 10/10 del(1p) and 79/87 + 1q. Concordance rates for trisomies 3, 5, 9, 11, 15 and 19 were 80/91, 75/87, 92/97, 87/97, 53/55, and 86/92, respectively. CNAs that were not detected by WGS were related to a tetraploid karyotype (38/61) or to a measured clone size below 20% (11/61).

      Reproducible Gene expression signatures by RNA-Seq

      Our results show that WGS is a valid technique that can reproduce FISH results with a high accuracy. Several groups have shown that gene expression profiles (GEP) allowed a classification of myeloma according to molecular genetic testing with prognostic relevance [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ,
      • Shaughnessy Jr., J.D.
      • Haessler J.
      • van Rhee F.
      • Anaissie E.
      • Pineda-Roman M.
      • Cottler-Fox M.
      • Hollmig K.
      • Zangari M.
      • Mohiuddin A.
      • Alsayed Y.
      • Grazziutti M.
      • Epstein J.
      • Crowley J.
      • Barlogie B
      Testing standard and genetic parameters in 220 patients with multiple myeloma with complete data sets: superiority of molecular genetics.
      ,
      • Kuiper R.
      • van Duin M.
      • van Vliet M.H.
      • Broijl A.
      • van der Holt B.
      • El Jarari L.
      • et al.
      Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.
      ]. We were interested if the additional implementation of RNA-Seq allows classification into published subgroups and could thus further help the diagnostic process. To this end we addressed the classification by Zhan et al., who defined 7 MM subgroups (CD-1: CCND1/CCND2 subgroup 1, CD-2: CCND1/CCND2 subgroup 2, HY: hyperdiploid subgroup, LB: low bone disease subgroup, MF: MAFA/MAFB subgroup, MS: MMSET subgroup, PR: proliferation subgroup) based on GEP [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ]. In their report four groups (CD-1/CD-2, MF, MS) were genetically defined by recurrent translocations and one by hyperdiploidy (HY). The study used 700 differentially expressed transcripts to separate the groups. We applied RNA-Seq on all 211 samples following magnetic CD138 separation. 400 of the reported transcripts were recovered in our RNA-Seq analysis (GEPSeq). A supervised clustering approach grouped all 211 patients into the respective GEPSeq groups at the following frequencies: CD-1 (5%), CD-2 (25%), HY (30%), MF (5%), MS (11%), LB (14%), PR (10%). An overview of the clustering result is given in Fig. 5A . As 5 groups are genetically defined we used this information to validate our findings with conventional FISH: CD-1 and CD-2 GEP groups are defined by translocation t(11;14) or t(6;14), while MS is defined by t(4;14) and MF by t(14;16). A circos plot shows the characteristic association of GEPSeq group and translocation (Fig. 5B). Specifically 55/62 (89%) of patients that were allocated to CD-1/CD-2 had the characteristic translocation. Moreover 23/24 (92%) of patients in GEPSeq group MS and 5/10 (50%) of patients in GEPSeq group MF were diagnosed by FISH with the respective translocation. GEP allocates patients with hyperdiploidy to group HY and 51/63 (81%) patients in HY were found to have a hyperdiploid karyotype by FISH (Fig. 5B and Table 2). Addressing the two GEP groups that are not genetically defined we observed +1q gains, del(13q) and hyperdiploid cases in the LB group and in the PR group we observed del(13q), del(1p), +1q and hyperdiploid cases.
      Fig. 5
      Fig. 5Gene expression by RNA-Seq and supervised clustering classifies patients according to known groups. A) By RNA-Seq 400/700 published transcripts
      [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ]
      were recovered and used in a supervised clustering approach to classify patients according to gene expression in the respective published GEP groups (CD-1: CCND1/CCND2 subgroup 1, CD-2: CCND1/CCND2 subgroup 2, HY: hyperdiploid subgroup, LB: low bone disease subgroup, MF: MAFA/MAFB subgroup, MS: MMSET subgroup, PR: proliferation subgroup) Shown is the result of the supervised clustering and the final GEPSeq group allocation. B) 5 out of 7 GEP groups are genetically defined by recurrent translocations (CD-1, CD-2, MF, MS) or hyperdiploidy (HY). A circos plot gives an overview of patients with the respective chromosomal aberration that were allocated to the expected GEPSeq groups.
      Table 2Patients according to supervised GEPSeq clustering and FISH results.
      GEP subgroupCD-1CD-2HYLBMFMSPR
      FISH
      t(11;14), n94101002
      t(4;14), n00000231
      t(14;16), n1000500
      t(6;14), n0510001
      hyperdiploid, n065112057
      GEP: gene expression by RNA-Seq, FISH: fluorescence in situ hybridisation, CD-1: CCND1/CCND2 subgroup 1, CD-2: CCND1/CCND2 subgroup 2, HY: hyperdiploid subgroup, LB: low bone disease subgroup, MF: MAFA/MAFB subgroup, MS: MMSET subgroup, PR: proliferation subgroup.
      Table 3Patients with discrepant results of FISH and GEP.
      n%Additional WGS resultReason for inaccurate FISHDiscrepancy including WGS (%)
      CD-11/10100n.d.1/10 (10)
      CD-26/52121 additional t(11;14)insufficient material for FISH5/52 (10)
      HY12/631910 hyperdiploid casesinsufficient material for FISH2/63 (3)
      MF5/10501 IGH-MYC, 1 IGH-MYCN, 2 IGH-rearrangementspotentially confounding IGH rearrangements5/10 (50)
      MS23/2441 additional t(4;14)insufficient material for FISH0/24 (0)
      GEP: gene expression by RNA-Seq, FISH: fluorescence in situ hybridisation, CD-1: CCND1/CCND2 subgroup 1, CD-2: CCND1/CCND2 subgroup 2, HY: hyperdiploid subgroup, MF: MAFA/MAFB subgroup, MS: MMSET subgroup, n.d. not defined.
      We specifically queried the WGS data for patients with discrepant FISH and GEPSeq results (Table 3): 10/12 patients that were allocated to HY by GEPSeq results and could not be assigned by FISH due to insufficient material for complete testing, showed typical hyperdiploidy based on WGS data resulting in a 97% final concordance of GEPSeq and karyotype. One patient in group MS without FISH data for t(4;14) could be confirmed by WGS as harbouring the translocation (concordance 100%). Interestingly in all five patients that were allocated to the MF group by GEPSeq two had an IGH-MYC or IGH-MYCN rearrangement respectively and two had another IGH rearrangement involving chromosome 8q (Table 3). Further analysis revealed that for those two cases the downstream gene was MAFA. There was one patient allocated to GEP subgroup CD-1 that did not have a translocation t(6;14) or t(11;14) by FISH or WGS, interestingly this patient harbored a t(14;16)/IGH-MAF. As MAF is a transcriptional regulator of CCND2, t(14;16) also leads to an increased expression of CCND2 (see Fig. 4) which could partially explain this gene expression phenotype. There were 6 patients allocated to GEP subgroup CD-2 that did not have a t(6;14) or t(11;14). For one of those 6 patients detailed WGS analyis revealed a t(11;14) which was potentially missed by initial FISH evaluation due to scarce material. For the remaining 5 patients, none had a t(6;14) or t(11;14) by WGS. For 3/5 patients we detected other genomic aberrations: trisomies, del(13q), del (17p) and +1q21, all were hyperdiploid cases.

      Identification of gene mutations by WGS

      In the recent years many studies have addressed the mutational landscape of MM. Common recurrently mutated genes include KRAS, NRAS, TP53 and BRAF [
      • Lohr J.G.
      • Stojanov P.
      • Carter S.L.
      • Cruz-Gordillo P.
      • Lawrence M.S.
      • Auclair D.
      • et al.
      Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.
      ,
      • Chapman M.A.
      • Lawrence M.S.
      • Keats J.J.
      • Cibulskis K.
      • Sougnez C.
      • Schinzel A.C.
      • et al.
      Initial genome sequencing and analysis of multiple myeloma.
      ,
      • Bolli N.
      • Avet-Loiseau H.
      • Wedge D.C.
      • Van Loo P.
      • Alexandrov L.B.
      • Martincorena I.
      • et al.
      Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.
      ]. In our analysis we used WGS to initially call CNA and translocations, however small variants are readily detectable in the WGS dataset. We evaluated 47 recurrently mutated genes as defined by the German Multiple Myeloma Study Group [
      • Kortum K.M.
      • Mai E.K.
      • Hanafiah N.H.
      • Shi C.X.
      • Zhu Y.X.
      • Bruins L.
      • et al.
      Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and RAS pathway genes.
      ]. In our cohort the most frequently mutated genes were KRAS (26%), NRAS (23%), TP53 (8%), BRAF (4%) and ATM (2%) (Fig. 6) , which is in line with published data [
      • Lohr J.G.
      • Stojanov P.
      • Carter S.L.
      • Cruz-Gordillo P.
      • Lawrence M.S.
      • Auclair D.
      • et al.
      Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.
      ]. The combined analysis of mutations and FISH data allowed us to establish the frequency of mutations in relation to common cytogenetic findings: TP53 mutations were significantly associated with del(17p) (p < 0.0001), NRAS mutations were significantly associated with WGS defined MYC rearrangements (p = 0.015) and KRAS mutations with t(11;14) (p = 0.018).
      Fig. 6
      Fig. 6Simultaneous detection of recurrent mutations in the combined WGS/RNA-Seq workflow. A) We analyzed 47 genes recurrently mutated in MM
      [
      • Kortum K.M.
      • Mai E.K.
      • Hanafiah N.H.
      • Shi C.X.
      • Zhu Y.X.
      • Bruins L.
      • et al.
      Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and RAS pathway genes.
      ]
      in our WGS dataset. Given are mutations in the depicted genes in respect to the obtained FISH results.

      Discussion

      Here we report on the first study that aimed to validate WGS and RNA-Seq results with gold standard FISH diagnostics. We show that there is a high concordance of FISH and WGS results, that WGS is superior in the detection of MYC translocations and that RNA-Seq results cluster patients into genetically defined subgroups. The convenient simultaneous analysis of recurrently mutated genes might have future implications for study design and selecting treatment options.
      WGS is an emerging powerful technique that without doubt will have an important effect on routine diagnostics. WGS analysis of MM has been reported, and focused on the detection of recurrently mutated genes [
      • Lohr J.G.
      • Stojanov P.
      • Carter S.L.
      • Cruz-Gordillo P.
      • Lawrence M.S.
      • Auclair D.
      • et al.
      Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.
      ,
      • Chapman M.A.
      • Lawrence M.S.
      • Keats J.J.
      • Cibulskis K.
      • Sougnez C.
      • Schinzel A.C.
      • et al.
      Initial genome sequencing and analysis of multiple myeloma.
      . While Lohr et al. [
      • Lohr J.G.
      • Stojanov P.
      • Carter S.L.
      • Cruz-Gordillo P.
      • Lawrence M.S.
      • Auclair D.
      • et al.
      Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.
      ] have analysed focal deletions and amplifications only few studies addressed the detection of translocations by WGS and even fewer in MM [
      • Suzuki T.
      • Tsurusaki Y.
      • Nakashima M.
      • Miyake N.
      • Saitsu H.
      • Takeda S.
      • et al.
      Precise detection of chromosomal translocation or inversion breakpoints by whole-genome sequencing.
      ,
      • Liang D.
      • Wang Y.
      • Ji X.
      • Hu H.
      • Zhang J.
      • Meng L.
      • et al.
      Clinical application of whole-genome low-coverage next-generation sequencing to detect and characterize balanced chromosomal translocations.
      ,
      • Egan J.B.
      • Shi C.X.
      • Tembe W.
      • Christoforides A.
      • Kurdoglu A.
      • Sinari S.
      • et al.
      Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides.
      ]. To our knowledge this is the largest study that used WGS in MM to identify clinically relevant translocations involving the IGH locus and CNA and validated the results with FISH data. We observed an overall high concordance of FISH and WGS in the detection of IGH-translocations and CNA, which is a prerequisite to use WGS in the future in a routine diagnostic approach. There was however a number of discrepant results in our data when comparing FISH and WGS results. The main reason for discrepancies were poor quality slides/scarce material for initial FISH testing and the small clone size in case of missed CNA or translocations by WGS. The detection of small clones or subclones could potentially be enhanced by higher coverage sequencing strategies.
      A recent report used a targeted sequencing panel to examine MM patients and was able to detect IGH-translocations and MM specific mutations in parallel [
      • Jimenez C.
      • Jara-Acevedo M.
      • Corchete L.A.
      • Castillo D.
      • Ordonez G.R.
      • Sarasquete M.E.
      • et al.
      A next-generation sequencing strategy for evaluating the most common genetic abnormalities in multiple myeloma.
      ]. This technique however misses chromosome wide CNA, which are addressed by our approach (concordance of FISH and WGS 92%). Interestingly most IGH translocations that were detected by FISH are rediscovered by WGS analysis. IGH-MYC translocations were missed by FISH in a high number of cases and could only be established by WGS. Moreover less common MYC translocations t(2;8) and t(8;22) were missed by FISH analysis in a relevant number of cases. This is in line with a previous report showing that multiple testing for MYC-translocations results in an increased detection rate, and that the MYC breakpoint can vary greatly therefore hindering successful detection by FISH break apart probes [
      • King R.L.
      • McPhail E.D.
      • Meyer R.G.
      • Vasmatzis G.
      • Pearce K.
      • Smadbeck J.B.
      • et al.
      False-negative rates for MYC fish probes in B-cell neoplasms.
      ]. Gene expression by RNA-Seq could validate the IGH translocations in case of CCND1, MAF, CCND3, FGFR3 and NSD2 [
      • Pawlyn C.
      • Davies F.E.
      Toward personalized treatment in multiple myeloma based on molecular characteristics.
      ]. Moreover the translocation t(14;16) leads to an overexpression of MAF and through transcriptional regulation to an overexpression of CCND2 [
      • Bergsagel P.L.
      • Kuehl W.M.
      • Zhan F.
      • Sawyer J.
      • Barlogie B.
      • Shaughnessy Jr., J.
      Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma.
      ]. MYC translocations result in a constant MYC overexpression, however also a subset of cases without MYC translocation reveals increased MYC expression. In lymphoma a complex regulation of MYC expression is established: MYC breakpoints are associated with MYC expression [
      • Wilda M.
      • Busch K.
      • Klose I.
      • Keller T.
      • Woessmann W.
      • Kreuder J.
      • et al.
      Level of MYC overexpression in pediatric Burkitt’s lymphoma is strongly dependent on genomic breakpoint location within the MYC locus.
      ] and hiqh MYC protein expression was observed in a subgroup of non-MYC translocated patients [
      • Aukema S.M.
      • van Pel R.
      • Nagel I.
      • Bens S.
      • Siebert R.
      • Rosati S.
      • et al.
      MYC expression and translocation analyses in low-grade and transformed follicular lymphoma.
      ].
      As array based GEP in MM results in prognostic relevant signatures [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ,
      • Shaughnessy Jr., J.D.
      • Haessler J.
      • van Rhee F.
      • Anaissie E.
      • Pineda-Roman M.
      • Cottler-Fox M.
      • Hollmig K.
      • Zangari M.
      • Mohiuddin A.
      • Alsayed Y.
      • Grazziutti M.
      • Epstein J.
      • Crowley J.
      • Barlogie B
      Testing standard and genetic parameters in 220 patients with multiple myeloma with complete data sets: superiority of molecular genetics.
      ,
      • Kuiper R.
      • van Duin M.
      • van Vliet M.H.
      • Broijl A.
      • van der Holt B.
      • El Jarari L.
      • et al.
      Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.
      ] we addressed whether RNA-Seq could successfully reproduce GEP results by array. We show that our RNA-Seq results classify patients according to their expression profile and match the predicted genetic subgroup [
      • Zhan F.
      • Huang
      • Colla Y.
      • Stewart S.
      • Hanamura J.P.
      • Gupta I.
      • Epstein S.
      • Yaccoby J.
      • Sawyer S.
      • Burington J.
      • Anaissie B.
      • Hollmig E.
      • Pineda-Roman K.
      • Tricot M.
      • van Rhee G.
      • Walker F.
      • Zangari R.
      • Crowley M.
      • Barlogie J.
      • Shaughnessy B.
      • Jr J.D.
      The molecular classification of multiple myeloma.
      ]. Interestingly when analysing patients with incongruent results of FISH and GEPSeq we were able to identify the underlying chromosomal aberration by WGS in the majority of cases. On one hand this result underscores that a parallel workflow yields a higher accuracy and shows on the other hand that RNA-Seq readily reproduces GEP by array analysis in MM samples. This is especially valuable as it will allow the integration of GEP by RNA-Seq into future prognostication systems [
      • Kuiper R.
      • van Duin M.
      • van Vliet M.H.
      • Broijl A.
      • van der Holt B.
      • El Jarari L.
      • et al.
      Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.
      ]. An elegant recent RNA-Seq analysis showed that a 7-gene expression signature can serve as a biomarker in MM and differentiate patients who are more likely to respond to bortezomib or lenalidomide containing regimens [
      • Chapman M.A.
      • Sive J.
      • Ambrose J.
      • Roddie C.
      • Counsell N.
      • Lach A.
      • et al.
      RNA-seq of newly diagnosed patients in the Padimac study leads to a bortezomib/lenalidomide decision signature.
      ]. Our detailed diagnostic approach can easily be modified to incorporate this signature.
      In our analysis we were able to achieve a coverage of 100x for WGS and a median of 50 million reads per sample for RNA-Seq. In order to reduce cost lower coverage sequencing with high accuracy is desirable. Several reports have addressed low coverage sequencing strategies and have yielded high detection rates of translocations and CNA, although this was not yet done in MM [
      • Liang D.
      • Wang Y.
      • Ji X.
      • Hu H.
      • Zhang J.
      • Meng L.
      • et al.
      Clinical application of whole-genome low-coverage next-generation sequencing to detect and characterize balanced chromosomal translocations.
      ,
      • Dong Z.
      • Zhang J.
      • Hu P.
      • Chen H.
      • Xu J.
      • Tian Q.
      • et al.
      Low-pass whole-genome sequencing in clinical cytogenetics: a validated approach.
      • Kader T.
      • Goode D.L.
      • Wong S.Q.
      • Connaughton J.
      • Rowley S.M.
      • Devereux L.
      • et al.
      Copy number analysis by low coverage whole genome sequencing using ultra low-input DNA from formalin-fixed paraffin embedded tumor tissue.
      . The inherent problem with low coverage strategies is that small clones are missed. This was also the case in our analysis: in several cases CNA were missed by WGS in patients with a clone size below 20% and translocations in patients with clone size below 10%. As sequencing costs decrease with time high coverage analysis will identify clinically important subclones in diagnostic samples and therefore increasing coverage rather than reducing it might be the future strategy. However detailed experimental approaches comparing high coverage vs low coverage strategies have yet to validate this notion.
      An advantage of WGS over FISH is that once a minimum number of plasma cells are purified WGS provides reproducible results that can repeatedly be queried for genome wide CNA and translocation, while FISH allows only the evaluation of a limited number of loci and FISH results are highly dependent on the quality of purified plasma cells. This was illustrated in our direct comparison: while several samples were not sufficient to use all FISH probes and resulted in an insufficient diagnostic test, a valid reproducible WGS and RNA-Seq analysis was feasible in all samples. Moreover, FISH slides are only stable for a limited amount of time and repeated testing is therefore limited. In addition to chromosomal aberrations WGS yields information on small variants, that would require additional purified plasma cells if incorporated into a parallel FISH and targeted sequencing strategy [
      • Ruiz-Heredia Y.
      • Sanchez-Vega B.
      • Onecha E.
      • Barrio S.
      • Alonso R.
      • Martinez-Avila J.C.
      • et al.
      Mutational screening of newly diagnosed multiple myeloma patients by deep targeted sequencing.
      ]. Our simultaneous analysis of CNA, IGH-translocations and somatic mutations in MM allowed us to assess the genetic context of the detected mutations. We confirmed a recent report where del(17p) frequently co-occured with TP53 mutations [
      • Chin M.
      • Sive J.I.
      • Allen C.
      • Roddie C.
      • Chavda S.J.
      • Smith D.
      • et al.
      Prevalence and timing of TP53 mutations in del(17p) myeloma and effect on survival.
      ], which is of clinical relevance as bi-allelic inactivation of TP53 is associated with inferior outcome [
      • Thanendrarajan S.
      • Tian E.
      • Qu P.
      • Mathur P.
      • Schinke C.
      • van Rhee F.
      • et al.
      The level of deletion 17p and bi-allelic inactivation of TP53 has a significant impact on clinical outcome in multiple myeloma.
      ]. Moreover it was recently shown that mutations in NRAS but not KRAS were associated with a reduced response to bortezomib treatment [
      • Mulligan G.
      • Lichter D.I.
      • Di Bacco A.
      • Blakemore S.J.
      • Berger A.
      • Koenig E.
      • et al.
      Mutation of NRAS but not KRAS significantly reduces myeloma sensitivity to single-agent bortezomib therapy.
      ]. These results could be re-interpreted in light of our findings as NRAS mutations were significantly associated with MYC rearrangements in our report. Therefore, the resistance mechanism potentially attributable to NRAS mutations could also be attributed to MYC-rearrangements, that are known to cause inferior outcome in MM [
      • Walker B.A.
      • Wardell C.P.
      • Brioli A.
      • Boyle E.
      • Kaiser M.F.
      • Begum D.B.
      • et al.
      Translocations at 8q24 juxtapose MYC with genes that harbor superenhancers resulting in overexpression and poor prognosis in myeloma patients.
      ].
      Novel diagnostic approaches that make use of cell free tumour DNA (liquid biopsy) are increasingly incorporating sequencing strategies. Several studies have successfully shown the detection of recurrent somatic mutations and copy number variations in MM patients by analysing cell free myeloma DNA [
      • Manier S.
      • Park J.
      • Capelletti M.
      • Bustoros M.
      • Freeman S.S.
      • Ha G.
      • et al.
      Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma.
      ,
      • Gerber B.
      • Manzoni M.
      • Spina V.
      • Bruscaggin A.
      • Lionetti M.
      • Fabris S.
      • et al.
      Circulating tumor DNA as a liquid biopsy in plasma cell dyscrasias.
      ,
      • Mithraprabhu S.
      • Khong T.
      • Ramachandran M.
      • Chow A.
      • Klarica D.
      • Mai L.
      • et al.
      Circulating tumour DNA analysis demonstrates spatial mutational heterogeneity that coincides with disease relapse in myeloma.
      ]. We provide a validated sequencing approach, including the detection of recurrent translocations, that will increase the diagnostic value of such techniques. Moreover as the whole genome and complete RNA expression is covered our approach can rapidly incorporate novel resistance mechanisms such as PSMB5 mutations [
      • Barrio S.
      • Stuhmer T.
      • Da-Via M.
      • Barrio-Garcia C.
      • Lehners N.
      • Besse A.
      • et al.
      Spectrum and functional validation of PSMB5 mutations in multiple myeloma.
      ]. And novel clinical master protocols rely on genomic data to decifer actionable genomic alterations and allow the allocation of patients to effective treatment options [
      • Auclair D.
      • Lonial S.
      • Anderson K.C.
      • Kumar S
      Precision medicine in multiple myeloma: are we there yet?.
      ].
      In summary WGS and RNA-Seq provide a powerful combination that allow the identification of prognostic and predictive genetic markers relevant today with the potential to easily incorporate upcoming novel biomarkers. The additional value of this diagnostic approach has to be evaluated in prospective studies.

      Funding

      JMHS was sponsered by a grant from Torsten Haferlach Leukämiediagnostikstiftung.

      Authorship

      AH and CH designed the study. AH, CH, SOT, JMHS and WW interpreted the data. AH and CH wrote the manuscript. MM, WW and SH did molecular analyses. TH was responsible for cytomorphologic analyses, CH for cytogenetic and FISH analyses and WK for immunophenotyping. All authors read and contributed to the final version of the manuscript.

      Declaration of Competing Interest

      The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

      Acknowledgements

      We thank all patients and clinicians for their participation in this study and all co-workers in our laboratory for their excellent technical assistance.

      Appendix. Supplementary materials

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