Exome sequencing in tracking clonal evolution in multiple myeloma following therapy

Article metrics

Sequencing the tumor genome using next-generation sequencing (NGS) is providing an unparalleled insight into the pathogenesis and progression of the disease. Much of the current focus of NGS in cancer is on defining mutations in the tumor genome at disease presentation, and these findings are central to understanding the molecular mechanisms that underlie pathogenesis. However, additional questions remain about the stability of the tumor clone under therapeutic pressure and these require a distinct second wave of NGS analyses. Understanding how each tumor evolves following therapy will be the key to delivering targeted therapy tailored for individual patients and in developing stratified therapeutic programs. Where a given tumor presents as a highly heterogeneous disease, this introduces a further complexity, requiring each subset of the disease to be evaluated separately in its response to therapy. Multiple myeloma (MM), defined as accumulation of malignant plasma cells in the bone marrow, exemplifies such a tumor and exhibits marked disease heterogeneity at presentation.1, 2 At the outset, aberrant chromosomal markers exist in diagnostic MM samples that are associated with poor prognosis and help in delineating specific disease subsets, including t(4;14) and 1q21 amplification among others,1, 2, 3, 4, 5 suggesting that these markers may be highly relevant to defining specific patterns of progression and clonal response to therapy.

The pivotal NGS study of MM centered on the MM Research Consortium (MMRC) analysis of 38 tumor genomes that identified key somatic mutations, either at disease presentation or at relapse, but not in a paired setting.6 Consequently, the data did not permit identification of molecular changes under therapy in individual patients longitudinally. Subsequent NGS data described further genomic lesions in MM that are associated with defined chromosomal translocations, including t(4;14), and defined subclonal evolution,7 but again only at disease presentation. Importantly, no single gene mutation or combination of mutations has emerged as universal to all MMs at presentation, across tumor subsets, suggesting that multiple molecular pathways will most likely be responsible for the pathogenesis of this disease and further suggesting that clonal changes under therapy are likely to vary between disease subsets in MM, to be mapped fully.

Data are beginning to emerge to meet this mapping need, but at markedly different levels of molecular sensitivities, determined by the technologies used. Very recently, a study based on array comparative genomic hybridization analysis on the 244A Human Genome CGH Microarrays (Agilent) platform evaluated 28 MM cases treated with a range of therapeutic regimens, in paired longitudinal analyses in individual patients.8 This specific technology is inherently limited, defining insertions and deletions of large DNA fragments of >several kb size and assessing copy number variation or copy number abnormality to define allele gain or loss. This study by Keats et al. reported varying patterns of clonal response to therapy in MM, with some cases displaying a relatively stable clone and others showing differing patterns of marked genetic changes.8 Nevertheless, the array comparative genomics hybridization-based study established a necessary ‘broad-brush’ understanding of clonal response to therapy in MM, observing that patients with poor-prognosis chromosomal markers tend to be associated with the largest degree of genetic change.8

Defining the precise pathways of response to therapy in MM cells, however, will require a concise mapping of somatic mutations at the nucleotide level, at the next layer of molecular sensitivity, in coding and non-coding genome-wide regions, and this will be dependent on NGS. Mutated genes determine altered effector molecule functions and mediate mechanisms of relapse and resistance to therapy. These mutations will need to be defined in specific subsets of MM after therapy. This level of NGS analysis has very recently been used to examine a single MM case displaying a t(4;14) abnormality, at different time points pre-and post-therapy.9 These data revealed a clear clonal evolution as disease progresses following therapy and defined key mutations in specific genes.

We extend the use of NGS in tracking the tumor clone in MM after therapy, by examining a separate subset of disease. By focusing on NGS of the tumor exome, we examine somatic mutations that have a direct impact on gene-coding sequences and report on the exome in an index case of MM with 1q21 amplification, comparing disease presentation with relapse in a paired analysis.

The MM case presented as IgGκ stage IIIA and underwent phases of treatment including chemotherapy and autologous transplants (described further in Supplementary Data). Tumor DNA was examined at disease presentation and at first relapse after the start of first-line therapy (year 4; Y4), using exome capture next-generation sequencing with matched T-cell DNA for germline. Sequencing libraries were subjected to Illumina/Solexa NGS (Supplementary Data). Bioinformatic comparison of tumor exomes with germline identified non-synonymous coding variants, representing putative mutations, with a summary of sequence coverage metrics for the three exomes shown in Table 1. A tumor sample was also available after conventional induction chemotherapy in plateau phase (year 1; Y1), which was only examined for selected gene mutations using conventional Sanger sequencing (Supplementary Data; Table 2b). At Y4, routine chromosomal assessment had already revealed the emergence of t(11;14) in 30% of tumor cells as a progression event (Supplementary Data).

Table 1 Summary of sequence coverage metrics for three exomes from the analysis of a multiple myeloma index case displaying 1q21 amplification
Table 2 Novel non-synonymous variants identified in tumor exomes at presentation and relapse in an index multiple myeloma case with 1q21 amplification

By coding exome capture next-generation sequencing results with 0 or 1 to denote the absence or presence of apparent variant (Table 2a), 81 tumor-specific novel non-synonymous variants were identified, of which 33 were shared at presentation and Y4 and 48 were de novo variants at Y4 (and Supplementary Table S1). Of the 81 genes, 14 were mutated in the MMRC cohort, with NRAS (p.Q61K) being identical,6 and all but one gene (WDR73) were also mutated in COSMIC (two identical: NRAS, p.Q61K; TAF1L, p.R1243W) (Supplementary Table S1). To verify fidelity, 20% of novel variants from each 0,1,1 and 0,0,1 data set were randomly Sanger sequenced to reveal a correct positive call of 100% (Table 2a and b), suggesting high confidence in exome capture next-generation sequencing bioinformatic calls. Of 15 confirmed mutations, 50% are predicted to be damaging (Table 2b). Notably, these findings reveal and confirm clonal evolution in MM following therapy, in our case accruing mutations at presentation that have evolved during the course of disease and persist to Y4, and acquiring mutations by Y4 that have arisen after plateau phase (Y1; Table 2b). They parallel the very recent observations from serially tracking the single t(4;14) MM case from presentation to end-stage plasma cell leukemia using whole-genome sequencing.9 Therapy differed in that study, including lenalidomide initially followed by a bortezomib combination. In the former study, 10 variants were common throughout and other clonal variants appeared and disappeared during disease course, with five gene variants associating with transformation to plasma cell leukemia.9 Two mutated genes are common to both studies, CSMD3 (CUB and Sushi multiple domains 3) in presentation and relapse, and SUB1 (SUB1 homolog, S. cerevisiae) associating with Y4 and plasma cell leukemia stage,9 suggesting important roles in tumor progression. However, another gene AFF1 (AF4/FMR2 family, member 1) was mutated at presentation to the plasma cell leukemia phase in the former study,9 and was highlighted as possibly relevant to progression in t(4;14) MM but was absent in our study, pointing to possible disease subset variation. Potential divergence at Y4 (48 variants) in the present study is large, however, as only 33 variants appear at presentation following pathogenesis, suggesting stage-dependent kinetic differences in acquiring mutations that associate with tumor progression in response to therapy.

The present report and the study by Egan et al.9 delineate the nature of individual gene mutations that occur following therapy in MM in two distinct poor prognosis subsets and have initiated the phase of NGS work that will be necessary to elucidate at the gene level the ‘broad-brush’ insight delivered by the study by Keats et al.8 Taken together, our observations and those from the single t(4;14) case9 reveal a disease landscape at relapse in MM that displays complex patterns of genetic mutations that will need to be dissected by NGS in relation to specific therapies. Common mutations, such as CSMD3 and SUB1 as described above, may provide key pointers to essential survival pathways irrespective of type of therapy or disease subset. Whether specific patterns associate with individual markers of poor prognosis in MM will emerge from NGS studies of larger cohorts. These patterns will define the models of clonal evolution, which may be linear or may involve a clone regressing and reappearing by competition. Whether all 1q21 MM cases exhibit an apparent linear mode of evolution under therapy, as suggested by our data, remains to be determined.

It is however evident from these two initial NGS studies that the tumor clone will need to be closely scrutinized after each phase of treatment. These studies also suggest that a multi-target combination therapy may be required to eradicate the genetically variant subclones in any specific tumor population in MM that persist after early therapy to prevent escape.10


  1. 1

    Carrasco DR, Tonon G, Huang Y, Zhang Y, Sinha R, Feng B et al. High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell 2006; 9: 313–325.

  2. 2

    Chesi M, Bergsagel PL . Many multiple myelomas: making more of the molecular mayhem. Hematology Am Soc Hematol Educ Program 2011; 2011: 344–353.

  3. 3

    Hanamura I, Stewart JP, Huang Y, Zhan F, Santra M, Sawyer JR et al. Frequent gain of chromosome band 1q21 in plasma-cell dyscrasias detected by fluorescence in situ hybridization: incidence increases from MGUS to relapsed myeloma and is related to prognosis and disease progression following tandem stem-cell transplantation. Blood 2006; 108: 1724–1732.

  4. 4

    Klein U, Jauch A, Hielscher T, Hillengrass J, Raab MS, Seckinger A et al. Chromosomal aberrations +1q21 and del(17p13) predict survival in patients with recurrent multiple myeloma treated with lenalidomide and dexamethasone. Cancer 2011; 117: 2136–2144.

  5. 5

    Kumar SK, Mikhael JR, Buadi FK, Dingli D, Fonseca R, Gertz MA et al. Management of newly diagnosed symptomatic multiple myeloma: updated mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines. Mayo Clinic Proc 2009; 84: 1095–1110.

  6. 6

    Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC et al. Initial genome sequencing and analysis of multiple myeloma. Nature 2011; 471: 467–472.

  7. 7

    Walker BA, Wardell CP, Melchor L, Hulkki S, Potter NE, Johnson DC et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4;14) and t(11;14) myeloma. Blood 2012; 120: 1077–1086.

  8. 8

    Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E et al. Clonal competition with alternating dominance in multiple myeloma. Blood 2012; 120: 1067–1076.

  9. 9

    Egan JB, Shi CX, 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. Blood 2012; 120: 1060–1066.

  10. 10

    Al-Lazikani B, Banerji U, Workman P . Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol 2012; 30: 679–692.

  11. 11

    Fuentes Fajardo KV, Adams D, Mason CE, Sincan M, Tifft C, NISC Comparative Sequencing Program. Detecting false-positive signals in exome sequencing. Hum Mutat 2012; 33: 609–613.

Download references


This work was funded by EU FP7 Program Project 278706 ‘OVER-MyR’, Leukemia & Lymphoma Research (UK) and Cancer Research UK (JG).

Author information

Correspondence to S S Sahota.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Leukemia website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Weston-Bell, N., Gibson, J., John, M. et al. Exome sequencing in tracking clonal evolution in multiple myeloma following therapy. Leukemia 27, 1188–1191 (2013) doi:10.1038/leu.2012.287

Download citation

Further reading