Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method. Multiple myeloma (MM) is a neoplasm of plasma cells with an incidence rate of approximatively 5/100,000 in Europe. The median survival of MM patients has improved substantially over the past decade. Owing to the establishment of high-dose therapy followed by autologous stem cell transplantation as a routine procedure, significant improvements in supportive care strategies, and the introduction and widespread use of the immunomodulatory drugs thalidomide and lenalidomide, and the proteasome inhibitor bortezomib. Nevertheless, almost all MM patients ultimately relapse, and new drugs and new combinations for the treatment of MM are warranted. MM is a heterogeneous disease at both the clinical and molecular levels. Recent large scale genomics analysis based on the landscape of copy-number alterations and on whole exome sequencing have revealed the hallmarks of genetic changes in MM such as hyperdiploidy, translocations involving the IgH locus, and mutations in the RAS/MAP and NF-kB pathways and in TP53 1. These genetic changes as well as gene-expression profiling (GEP) have been widely used in the molecular classification of newly diagnosed patients to define diagnostic entities and identify promising new therapeutic targets 2–7. However, at present a standard of classification based on subgroups that could be targeted therapeutically is still being debated. Clearly, there is a need for innovative tools to improve the identification of the prognostically relevant entities, clinically and biologically, in newly diagnosed MM patients. It is tempting to use the mutational spectrum based on whole-exome sequencing as a gold standard; however we have previously shown that a large number of exome mutant alleles are not expressed clinically or biologically 8. In addition, exome sequencing may miss potential driver mutations in the non coding regulatory elements known to affect enhancer activity, which thereby affect the transcriptional program 9 ; therefore GEP remains a tool of choice.