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A. Mikulasova et al.
switching.3 However, abnormal breaks in the switch regions, resulting from AID activity, result in IGH translo- cations.4
Secondary translocations involving MYC, located on 8q24.21, also occur in MM and are associated with dis- ease progression and increased expression of MYC.5-8 MYC encodes a transcriptional regulator and has been shown to be involved in proliferation, differentiation, protein synthesis, apoptosis, adhesion, DNA repair, chro- mosomal instability, angiogenesis, and metastasis.9-13 Translocations and high expression of MYC are associat- ed with poor outcome, especially in MM where it is a marker of aggressive disease.5,14 MYC can be deregulated by a range of different mechanisms including chromoso- mal rearrangement,5,6 copy-number gain/amplification,15,16 protein stabilization,17 via secondary messengers involved in MYC transcription18 or miRNA such as PVT1.19,20
The frequency of MYC rearrangements seen in newly diagnosed MM (NDMM) varies from 15% to 50%, and is dependent on the method used to identify it.5,6,21,22 The data are consistent with MYC rearrangements being rare in the asymptomatic stages, such as monoclonal gam- mopathy of uncertain significance and smoldering myelo- ma,21 and increases as the disease progresses, with a high incidence (>80%) in myeloma cell lines.22-24
MYC rearrangements are not only seen in MM, but are also frequent in lymphomas, where they have been extensively studied.25,26 In Burkitt’s lymphoma and diffuse large B-cell lymphoma t(8;14), rearrangements between IGH and MYC have also been shown to result from abnormal class switch recombination.27 The relevance of AID in these rearrangements is supported by data from IL-6 transgenic mice which also develop MYC/IGH rearrangements in B cells. Rearrangements, however, do not occur if the mice are also deficient in AID, indicating that class switch recombination via AID is key in generat- ing these rearrangements.4,28 In MM, while karyotypic abnormalities similar to those observed in Burkitt’s lym- phoma are seen, variant structures can also be detected, suggesting that the mechanism of rearrangement in MM may not be identical to that in lymphoma.29 Indeed, MYC rearrangements are not considered to be predominantly primary translocations in MM, as they often develop at later stages of the disease;22 whereas in lymphoma they are considered to be primary events.27
We and others have previously shown that MYC translocations result in the juxtaposition of immunoglob- ulin loci super-enhancers to MYC resulting in its overex- pression.6,30 However, the details of breakpoint locations, the presence of copy-number abnormalities, and the chromatin landscape of the rearrangement have not been well-characterized. In the present study, we analyzed a large dataset of 1,267 NDMM patients to determine the genomic architecture of MYC rearrangements and their effect on the expression of this proto-oncogene.
Methods
Patients' samples and next generation sequencing
A total of 1,267 NDMM patients were included in this study after giving informed consent and the study was approved by the Institutional Review Board at the University of Arkansas for Medical Sciences. Plasma cells were isolated from bone marrow aspirates by magnetic-activated cell sorting using CD138+ marker,
AutoMACS Pro (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany) or Robosep (STEMCELL Technologies, Vancouver, BC, Canada). DNA from peripheral blood was used as a control sam- ple for each patient to exclude germline variants. Three paired- end read sequencing platforms were combined without overlap- ping patients: targeted sequencing, whole exome sequencing, and low depth, long insert whole genome sequencing (Online Supplementary Methods). Additional expression data were available through either gene expression microarrays (Affymetrix, Santa Clara, CA, USA) or RNA-sequencing. An overall summary of methods, number of patients and external datasets are shown in Online Supplementary Figure S1. Patients’ characteristics are sum- marized in Online Supplementary Table S1 and MYC region capture is illustrated in Online Supplementary Figure S2.
Patient-derived xenografts
Patient-derived xenografts were generated by passaging pri- mary patient CD138+ selected cells through the previously described SCID-rab myeloma mouse model.31 Tumors were dis- sected from the mouse, and pieces dispersed into a single cell population using a Kontes disposable tissue grinder. Cells were filtered through a 70 μm sterile filter, washed twice in PBS, treat- ed with red cell lysis buffer, washed twice more, and treated immediately with Annexin V-coated magnetic beads (Miltenyi Biotec), resulting in a population of cells with a viability >95%, as checked by flow cytometry. Passaged cells underwent CD138+ selection before being processed for 10x Genomics whole genome sequencing, RNA-sequencing, and chromatin immunoprecipitation-sequencing (ChIP-seq).
Chromatin immunoprecipitation-sequencing
Chromatin immunoprecipitation-sequencing was performed on the myeloma cell lines KMS11 and MM.1S as well as a PDX sample with an MYC rearrangement identified by whole genome sequencing. 1x107 cells per mark were fixed in a 1% formaldehyde solution, followed by the addition of glycine to a final concentration of 0.125 M. Cells were washed and resus- pended in PBS containing 0.5% lgepal with 1% PMSF, before being pelleted and frozen at -80oC. ChIP-seq for the histone marks H3K4me1, H3K4me3, H3K9me3, H3K27me3, H3K27Ac, and H3K36me3 (Active Motif, Carlsbad, CA, USA), as well as the super-enhancer proteins BRD4 and MED1 (Bethyl, Montgomer, TX, USA), and the transcription factor MYC (Santa Cruz Biotechnology, Dallas, TX, USA) were performed by Active Motif. Controls without antibody input were performed to ensure data quality.
Data analysis
Data analysis was performed as described previously, with minor differences between sequencing modalities.32 For details see Online Supplementary Methods.
Statistical analysis
Basic statistical analysis was performed using GraphPad Prism 7.01 (GraphPad Software, San Diego, CA, USA), R 3.4.4 and/or RStudio 1.1.442. Fisher’s exact test, the Mann-Whitney U test, Spearman’s rank correlation and Log-Rank test with Benjamini- Hochberg adjustment were used for data analysis. P≤0.05 was considered statistically significant.
Data access
Sequencing data have been deposited in the European Genomic Archive under the accession numbers EGAS00001001147, EGAS00001002859, or at dbGAP under Accession phs000748.v5.p4.
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