Generation of an automated tool for querying myeloma transcriptomics for multiple gene expression signatures used in risk-stratification.

Abstract

8024 Background: Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. In MM, gene expression profiling (GEP) has a key role in establishing clinically relevant disease subgroups. Methods: To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells from 469 MM cases in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. Results: All signatures identified around a third of patients with high-risk disease, ranging from 26% by using high proliferation index to 28% with high centrosome signature and 31% with high 70-gene index. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02), D2 (p=0.05), and None (p=0.08) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations without known partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Conclusions: Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making.