Laboratory diagnostics

Prognostic factors are indispensable in the clinic and this topic was therefore well covered during the EHA Congress this year. In this context, it was presented that measuring MRD showed its significance for risk prediction in multiple myeloma, as did chromosomal alterations. To detect these aberrations, it was presented that emerging next-generation sequencing techniques are superior to the conventional microscopic techniques.

MRD status predicts high-risk in myeloma

Increasing evidence show that deeper therapeutic responses correlate with greater efficacy of treatment in multiple myeloma (MM) and hence a better chance to survive. Nevertheless, most patients with deep response finally relapse, which urges for more sensitive methods to detect and quantify response. To this end, minimal residual disease (MRD) detection has shown to be a strong prognostic factor.

Therefore, Dr Stefania Oliva (University of Torino, Torino, Italy) assessed the utility of MRD, measured by next generation flow cytometry (NGF), to predict progression-free survival (PFS) of MM patients receiving autologous stem cell transplantation or intensification/consolidation with novel agents.1 These patients were <65 years, newly diagnosed and enrolled in the phase 2 RV-MM-COOP-0556 trial. Samples were collected pre-maintenance, and at 6, 12, 18 and 24 months after start of Lenalidomide maintenance of those with a very good partial response pre-maintenance (n=316).

In this trial, Lenalidomide maintenance improved depth of response, as 48% of MRD-positive patients at pre-maintenance stage turned MRD negative during 2 years of treatment. Moreover, three years after start of maintenance, MRD status independently predicted PFS (MRD- 77% vs MRD+ 52%), which was most apparent for cytogenetic high-risk patients (vs standard-risk HR 0.11, 95% CI 0.05-0.24, P=0.05), “confirming the importance of MRD as clinical endpoint in the context of intensive therapies”… “and the importance of achieving deeper responses in the high-risk MM types”, said Oliva.


NGS detects high-risk chromosomal aberrations in myeloma

Translocations t(4;14) and t(14;16) as well as 17p deletions are high-risk factors in multiple myeloma. Conventional methods to detect these prognostic aberrations comprise FISH and karyotyping. As these approaches have limitations, such as their restricted targeting and limited resolution, Dr Christopher Chiu (Janssen research & development, Spring House, USA) validated whether they could be replaced by the new sensitive next-generation sequencing (NGS) methods;2  whole exome sequencing to identify del17p and RNA-seq to detect t(4;14), known to overexpress MMSET, and t(14;16), which often results in aberrant MAF and CCND2 expression.

For this, Chiu collected bone marrow aspirated samples from patients enrolled in the phase 3 daratumumab POLUX and CASTOR trials. Apart from known, also novel abnormalities were explored with NGS.

First, it was confirmed that 17p deletions could be adequately identified based on two data points, as 17p-deleted patients clearly had less reads and also B allele frequency (BAF) evidently deviated from its standard 0.5 value on the 17p position. Chiu said “looking at these two data points allows us to have high confidence in the deletion calls.” Second, correlations of t(4;14) and MMSET RNA and of t(14;16) and MAF and CCND2 RNA were high, “once again, demonstrating that this methodology was highly accurate and sensitive to detect these translocations” mentioned Chiu. Importantly, concordance rates of FISH and NGS were high (range 88-98%) and both were similarly related to progression-free survival. Moreover, NGS was able to identify additional risk factors that were normally undetected by conventional methodologies, including TP53 mutations. Taken together, Chiu said that “these data truly support the use of these tools in additional clinical trials and allows us to identify novel and emerging biological and clinical correlates, that will help us to discover biomarkers.” 



  1. Oliva S, et al. EHA 2017: abstract S102.
  2. Chiu C, et al. EHA 2017: abstract S100.