Highs and lows of t(4;14) in multiple myeloma

In this issue of Blood, Stong et al show that true high-risk t(4;14) multiple myeloma (MM) patients can be identified by using the coordinates of the translocation breakpoints in the NSD2 gene.¹ The authors provide an elegant and detailed characterization of a single genetic alteration that improves our understanding of disease biology and prediction of clinical outcomes (see figure).

Among the first reports showing the presence of t(4;14) in MM were those published in 1997 by Chesi et al² and Richelda et al.³ One year later, Chesi et al⁴ demonstrated that the t(4;14) was an interesting example of an IgH translocation that simultaneously dysregulates 2 genes with oncogenic potential: FGFR3 and MMSET, which is currently named NSD2. In 2001, Fonseca et al showed that the t(4;14) was strongly associated with chromosome 13 abnormalities.⁵ This finding was confirmed in the comprehensive analysis performed by Stong et al,¹ which further uncovered a constellation of copy number alterations and somatic mutations that were enriched in t(4;14) patients. Most interestingly, FGFR3 mutations were exclusive to these and absent in non-t(4;14) patients, but such mutations had no impact in survival.¹ 

In 2001 and 2003, Rasmussen et al⁶ and Keats et al⁷ concluded that, in MM, t(4;14) is an adverse prognostic factor irrespective of FGFR3 expression. Notably, Stong et al¹ confirmed this finding and uncovered that expression of NSD2 was also unrelated to poor outcome. In 2013, Walker et al⁸ performed whole genome sequencing and identified breakpoint locations upstream of the NSD2 gene or within the coding sequence. Other groups have suggested a potential association between expression of NSD2 truncated isoforms (resulting from breakpoint locations within the coding sequence) and a poor prognosis, but the study from Stong et al, performed in the largest cohort of 258 t(4;14) newly diagnosed MM patients (153 discovery and 105 independent replication), showed unequivocally that only those with a breakpoint within the NSD2 gene and downstream of the translation start site (coined as “late disruption”; 31%) have a dismal overall survival.¹ Patients with a breakpoint between the transcription and translation start site (“early disruption”; 23.5%) and upstream (“no disruption”; 45.5%) of the NSD2 gene displayed progressively longer survival.¹ Importantly, risk stratification according to the 3 breakpoint regions was superior to that achieved with previously identified NSD2 truncated isoforms.¹ Thus, an NSD2 breakpoint analysis is the way forward to identify high-risk t(4;14) patients.

The authors have probably generated the largest dataset on t(4;14) MM, which includes whole genome and RNA sequencing data. The latter were used to analyze fusion NSD2 transcripts, which confirmed in most patients the correlation between the no disruption or early disruption and full-length fusion transcripts, as well as between late disruption and truncated fusion transcripts.¹ Further investigation from this group using data from RNA sequencing will be an important sequel of this article, hopefully identifying novel therapeutic targets for t(4;14) MM. The identification of true high-risk t(4;14) may prove extremely useful for the initial use of targeted therapy for this genetic risk group. The median overall survival of patients with no disruption, early disruption, and late disruption t(4;14) was 75.1, 59.4 and 28.6 months, respectively.¹ 

The discovery and independent replication cohorts included patients receiving numerous induction regimens and transplant-based and nontransplant approaches, as well as maintenance of fixed vs continuous duration. Thus, although targeted therapies are eagerly awaited for this and other genetic subgroups, future analyses should address whether the dismal survival of patients with late disruption t(4;14) can be improved with the nuances of current treatment approaches, including the use of anti-CD38 monoclonal antibodies upfront. In such analyses, that would probably require an even larger series of patients with t(4;14), it will be interesting to investigate whether the multiparameter definition of cytogenetic risk proposed by the same authors⁹ is able to improve outcome predictions in each of the newly defined t(4;14) molecular subgroups.

Both the late and early disruption NSD2 breakpoint, as well as del(17p), del(1p), and 1qAmp, were significantly associated with inferior overall survival in a multivariate analysis, although age or the International Staging System (ISS) evaluation were not.¹ These data, along with prior publications by some of the same authors that identified del(17p) with cancer clonal fraction of 0.55 or higher and 1qAmp as high-risk features, “makes a scientific case to discuss modifications of the revised ISS (R-ISS) criteria to define high-risk MM.”¹ Interestingly, a second revision of the R-ISS (R2-ISS) was recently proposed, which includes chromosome 1q gain/amplification, that outperforms the R-ISS.¹⁰ The authors might be correct in their claim, but new staging systems must prove superiority to the R2-ISS, and should be easily performed worldwide. Unfortunately, next-generation sequencing or polymerase chain reaction-based approaches to characterize high-risk t(4;14) are not performed routinely. Many groups support a progressive replacement of karyotyping and fluorescence in situ hybridization by targeted sequencing; the study from Stong et al nicely shows that such a replacement is not only about studying more genetic alterations using a single assay, but also detailed characterization of selected abnormalities for improved risk stratification in MM. A targeted sequencing panel should therefore analyze the coordinates of the translocation breakpoints in the NSD2 gene. The authors should be commended for showing why and how this should be done.

Conflict-of-interest disclosure: B.P. reports honoraria for lectures from and membership on advisory boards with Adaptive, Amgen, Becton Dickinson, Bristol-Myers Squibb-Celgene, Creative BioLabs, GSK, Janssen, Kite Pharma, Roche, Sanofi, and Takeda; unrestricted grants from Celgene, EngMab, Roche, Sanofi, and Takeda; and consultancy for Bristol-Myers Squibb-Celgene, Janssen, Sanofi, and Takeda. M.-J.C. declares honoraria for lectures from and membership on advisory boards with Janssen, Jazz Pharmaceuticals, Astellas, Novartis, Amgen, and Bristol-Myers Squibb-Celgene.