A Network Analysis of Multiple Myeloma Related Gene Signatures

• Published in: Cancers 2019-09, Vol.11 (10), p.1452
• Main Authors: Liu, Yu, Yu, Haocheng, Yoo, Seungyeul, Lee, Eunjee, Laganà, Alessandro, Parekh, Samir, Schadt, Eric E., Wang, Li, Zhu, Jun
• Format: Article
• Language: English
• Subjects: Annotations; Cell cycle; Chromosomes; Copy number; Datasets; Discriminant analysis; Gene expression; Genomics; Immunomodulation; Labeling; Medical prognosis; Metadata; Molecular modelling; Multiple myeloma; Patients; Principal components analysis; Proteasome inhibitors; Risk groups; Tumorigenesis
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers11101452

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.