J Mol Diagn. 2017 Jan;19(1):99-106.

A Next-Generation Sequencing Strategy for Evaluating the Most Common Genetic Abnormalities in Multiple Myeloma.



Identification and characterization of genetic alterations are essential for diagnosis of multiple myeloma and may guide therapeutic decisions. Currently, genomic analysis of myeloma to cover the diverse range of alterations with prognostic impact requires sequential studies with fluorescence in situ hybridization (FISH), single nucleotide polymorphism arrays, and sequencing techniques, which are costly and labor intensive and require large numbers of plasma cells. To overcome these limitations, we designed a targeted-capture next-generation sequencing approach for one-step identification of IGH translocations, V(D)J clonal rearrangements, the IgH isotype, and somatic mutations to rapidly identify risk groups and specific targetable molecular lesions. Forty-eight newly diagnosed myeloma patients were tested with the panel, which included IGH and six genes that are recurrently mutated in myeloma: NRAS, KRAS, HRAS, TP53, MYC, and BRAF. We identified 14 of 17 IGH translocations previously detected by FISH and three other translocations not detected by FISH and confirmed by breakpoint PCR amplification. This has the advantage of breakpoint identification, which can be used as a target for evaluating minimal residual disease. IgH subclass and V(D)J rearrangements were identified in 77% and 65% of patients, respectively. Mutation analysis revealed the presence of missense protein-coding alterations in at least one of the evaluating genes in 16 of 48 patients (33%). This method may represent a time- and cost-effective diagnostic method for the molecular characterization of multiple myeloma.




Multiple myeloma (MM) is a heterogeneous disease characterized by complex cytogenetic and molecular genetic alterations. Translocations involving the immunoglobulin heavy chain locus (IGH) are present in approximately 50% of patients and have an important role in myeloma because may determine the prognostic outcome.1 These events are routinely assessed by fluorescence in situ hybridization (FISH), but in a small proportion of samples, the partner chromosome cannot be identified. Moreover, FISH studies are costly, require a relatively high amount of separated viable tumor cells and are restricted to the analysis of those abnormalities covered by the specific probes.2 Next generation sequencing (NGS) techniques may overcome these limitations with the additional advantage of identifying the precise localization of the breakpoints, thus allowing to design specific primers for each patient in order to detect the aberrant translocation over disease evolution. In addition to the detection of these genetic abnormalities, capturing and sequencing IGH has the potential of identifying the IgH isotype and the V(D)J clonal rearrangement, required for minimal residual disease (MRD) monitoring, which had to be characterized by PCR and Sanger sequencing.3,4 Lastly, several recurrent mutated genes have been recently described, but their prognostic value remains to be prospectively investigated.5

Therefore, in order to help in the diagnosis, risk-stratification, and management of MM patients, strategies to characterize the genetic and molecular profile of the myeloma clone would be very useful.6

In this study, we developed a NGS capture-based approach to identify IGH translocations, V(D)J clonal rearrangements, IgH isotype, and some of the most common somatic mutations in myeloma patients (involving NRAS, KRAS, HRAS, TP53, MYC and BRAF genes). We tested our methodology in a series of 48 newly diagnosed MM patients, with known FISH and molecular data. The final purpose was to produce a useful laboratory test for the diagnosis and monitoring of MM patients.

First, our panel could confirm 14 out of the 17 translocations previously detected by FISH: t(11;14)[n=7], t(4;14)[n=3], t(14;16)[n=2], and IGH translocations with an unknown partner [n=2]. Novel fusion partners were chromosomes 5 (gene NDUFS4) and 12 (gene SCARB1). Breakpoints in IGH were located in IGHM switching region (57%), in other IGH switching regions (21%), and in the JH region (21%) (Table 1). The characterization of the breakpoints makes possible the design of targeted primers to track these translocations for disease monitoring. Additionally, we found eight translocations that had not been detected by FISH, although only three could be confirmed by PCR [two t(8;14) and one t(2;14)] (Table 2), as well as two MYC-associated translocations not involving IGH.

Furthermore, this approach allowed us to identify the V(D)J/DJ clonal rearrangement in 67% of the patients, including 4 cases that could not have been obtained by Sanger sequencing. The presence of somatic hypermutation of the V(D)J segment or an inaccurate probe design may explain the identification failures and highlights the need of optimization of this part.

As far as IgH isotype was concerned, we could observe deletions between µ switch (S) region and Sγ1 (in 51% of cases), Sγ2 (8%), Sγ3 (19%), Sγ4 (8%) and Sα1 (14%), which reflects the class switch recombination process. All these splicing regions as well as V(D)J and DJ clonal rearrangements can be targets for MRD evaluation too.

Finally, mutation analysis revealed the presence of missense protein-coding alterations in 33% patients. The most frequent variations involved RAS pathway, specifically NRAS and KRAS (mutated in 15% of patients each, and being mutually exclusive). TP53 mutations were present in 4% of patients, although only in one case was associated with a 17p deletion; finally, MYC was altered in 2% of the cases. No other mutations were observed.

In conclusion, our NGS capture-based strategy allows the simultaneous identification of IGH translocations, V(D)J rearrangements, IgH isotype switching, and somatic mutations in myeloma patients, also providing specific targets for MRD monitoring. Nevertheless, this tool still requires further optimization and standardization that will involve both biological and bioinformatics evaluation, with the final aim of being implemented in diagnostic and research laboratories.


Table 1.- Translocation breakpoints identified by targeted sequencing

Table 2.- IGH translocations not detected by FISH

*Confirmed by PCR



1. Sawyer JR. The prognostic significance of cytogenetics and molecular profiling in multiple myeloma. Cancer Genet, 2011, 204:3–12
2. Ross FM, Avet-Loiseau H, Ameye G, Gutiérrez NC, Liebisch P, O’Connor S, Dalva K, Fabris S, Testi AM, Jarosova M, Hodkinson C, Collin A, Kerndrup G, Kuglik P, Ladon D, Bernasconi P, Maes B, Zemanova Z, Michalova K, Michau L, Neben K, Hermansen NEU, Rack K, Rocci A, Protheroe R, Chiecchio L, Poirel HA, Sonneveld P, Nyegaard M, Johnsen HE. Report from the European Myeloma Network on interphase FISH in multiple myeloma and related disorders. Haematologica, 2012, 97:1272–7
3. González D, van der Burg M, García-Sanz R, Fenton JA, Langerak AW, González M, van Dongen JJM, San Miguel JF, Morgan GJ. Immunoglobulin gene rearrangements and the pathogenesis of multiple myeloma. Blood, 2007, 110:3112–21
4. Sarasquete ME, García-Sanz R, González D, Martínez J, Mateo G, Martínez P, Ribera JM, Hernández JM, Lahuerta JJ, Orfão A, González M, Miguel JFS. Minimal residual disease monitoring in multiple myeloma: a comparison between allelic-specific oligonucleotide real-time quantitative polymerase chain reaction and flow cytometry. Haematologica, 2005, 90:1365–72
5. Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, Harview CL, Brunet J-P, Ahmann GJ, Adli M, Anderson KC, Ardlie KG, Auclair D, Baker A, Bergsagel PL, Bernstein BE, Drier Y, Fonseca R, Gabriel SB, Hofmeister CC, Jagannath S, Jakubowiak AJ, Krishnan A, Levy J, Liefeld T, Lonial S, Mahan S, Mfuko B, Monti S, Perkins LM, Onofrio R, Pugh TJ, Rajkumar SV, Ramos AH, Siegel DS, et al. Initial genome sequencing and analysis of multiple myeloma. Nature, 2011, 471:467–72
6. Morgan GJ, Kaiser MF. How to use new biology to guide therapy in multiple myeloma. Hematology Am Soc Hematol Educ Program, 2012, 2012:342–9