A Network Analysis of Multiple Myeloma Related Gene Signatures

Abstract

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10−26). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures.

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

“…Four existing prognostic gene expression profiles of MM demonstrated significant overlap with the genes of Pr-68. We compared the gene sets of UAMS70 33 , EMC92 34 , M3CN 24 , and the Proliferation signature of Hose et al . 35 to the Pr-68 genes and computed hypergeometric p- values.…”

“…Although gene expression networks have previously been derived to study MM 22–24 , a CM TRN that elucidates causal flows from mutations to regulators to co-regulated genes across MM subtypes has not yet been established. In this work, we present a novel method called Mechanistic Inference of Node-Edge Relationships (MINER) to construct a CM TRN from multi-omics and clinical outcomes data, infer patient-specific network activity, and identify subtype-specific mechanisms that are likely to predispose resistance or susceptibility to a given therapy.…”

Actionable Strategies to Target Multiple Myeloma Plasma Cell Resistance/Resilience to Stress: Insights From “Omics” Research

“…Liu et al (51) analyzed the gene expression profile, copy number variation, and clinical features in a large data set from the Multiple Myeloma Research Consortium (MMRC) identifying eight prognostic signatures encompassing 178 genes related to cell cycle progression and a molecular gene signature involved in immunomodulatory drugs and proteasome inhibitors response. The authors were able to create a MM molecular causal network model, by integrating gene expression and copy number variation data, with supposed key regulators, such as genes involved in cell cycle and metabolic pathways.…”

Genome-Wide Identification of Potential Biomarkers in Multiple Myeloma Using Meta-Analysis of mRNA and miRNA Expression Data

“…Expression pro les of differentially expressed genes (DEGs) are of paramount importance and have provided critical prognostic insights in MM. Computational and functional analysis of hub genes, nodes, networks and pathways in MM have led to the development of risk scoring systems, relating to the 8 genetic subgroups 2 , 70 genes UAMS70 risk signatures 3 , IFM15 risk strati cation 4 , 5 gene stemness score 5 , UAMS 17 3 , CINGLEC 214 6 , EMC 92 7 , HZD 97 8 , Millenium 100 9 , M3CN 10 and others.…”