Protein Expression for Novel Prognostic Markers (Cyclins D1, D2, D3, B1, B2, ITGβ7, FGFR3, PAX5) Correlate With Previously Reported Gene Expression Profile Patterns in Plasma Cell Myeloma

Quantification of cyclin D1 and D2 proteins in multiple myeloma identifies different expression patterns from those revealed by gene expression profiling

Upregulation of a cyclin D gene determined by expression microarrays is an almost universal event in multiple myeloma (MM), but this finding has not been properly confirmed at the protein level. For this reason, we carried out a quantitative analysis of cyclin D proteins using a capillary electrophoresis nanoimmunoassay in newly diagnosed MM patients. Exclusive expression of cyclin D1 and D2 proteins was detected in 54 of 165 (33%) and 30 of 165 (18%) of the MM patients, respectively. Of note, cyclin D1 or D2 proteins were undetectable in 41% of the samples. High levels of cyclin D1 protein were strongly associated with the presence of t(11;14) or 11q gains. Cyclin D2 protein was detected in all the cases bearing t(14;16), but in only 24% of patients with t(4;14). The presence of cyclin D2 was associated with shorter overall survival (hazard ratio =2.14; P=0.017), although patients expressing cyclin D2 protein, but without 1q gains, had a favorable prognosis. In conclusion, although one of the cyclins D is overexpressed at the mRNA level in almost all MM patients, in approximately half of the patients this does not translate into detectable protein. This suggests that cyclins D could not play an oncogenic role in a proportion of patients with MM (clinicaltrials gov. identifier: NCT01916252).

Mutational landscape reflects the biological continuum of plasma cell dyscrasias

We subjected 90 patients covering a biological spectrum of plasma cell dyscrasias (monoclonal gammopathy of undetermined significance (MGUS), amyloid light-chain (AL) amyloidosis and multiple myeloma) to next-generation sequencing (NGS) gene panel analysis on unsorted bone marrow. A total of 64 different mutations in 8 genes were identified in this cohort. NRAS (28.1%), KRAS (21.3%), TP53 (19.5%), BRAF (19.1%) and CCND1 (8.9%) were the most commonly mutated genes in all patients. Patients with non-myeloma plasma cell dyscrasias showed a significantly lower mutational load than myeloma patients (0.91±0.30 vs 2.07±0.29 mutations per case, P=0.008). KRAS and NRAS exon 3 mutations were significantly associated with the myeloma cohort compared with non-myeloma plasma cell dyscrasias (odds ratio (OR) 9.87, 95% confidence interval (CI) 1.07–90.72, P=0.043 and OR 7.03, 95% CI 1.49–33.26, P=0.014). NRAS exon 3 and TP53 exon 6 mutations were significantly associated with del17p cytogenetics (OR 0.12, 95% CI 0.02–0.87, P=0.036 and OR 0.05, 95% CI 0.01–0.54, P=0.013). Our data show that the mutational landscape reflects the biological continuum of plasma cell dyscrasias from a low-complexity mutational pattern in MGUS and AL amyloidosis to a high-complexity pattern in multiple myeloma. Our targeted NGS approach allows resource-efficient, sensitive and scalable mutation analysis for prognostic, predictive or therapeutic purposes.