Abstract
Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.
Highlights
Multiple myeloma (MM) is a cancer of malignant plasma cells in the bone marrow (BM) that has a prevalence of ~86,000 new cases per year1
We developed the mechanistic inference of node-edge relationships (MINER) pipeline to infer transcriptional regulatory network (TRN) from gene expression data and apply them to the characterization and prediction of phenotypes
MINER builds upon our previous work with the SYstems Genetics Network AnaLysis (SYGNAL) pipeline insofar as it enables the same core functionalities of mechanistic and causal inference, but does so with a new suite of algorithms that enable new applications in the network-based prediction of clinical outcomes (Fig. 1)7
Summary
Multiple myeloma (MM) is a cancer of malignant plasma cells in the bone marrow (BM) that has a prevalence of ~86,000 new cases per year. The myriad combinations of chromosomal aberrations and somatic mutations, coupled with the complex dependence of MM progression on the BM microenvironment, have precluded a mechanistic understanding of the disease on a patient-specific level. A detailed map of the underlying biology of MM is necessary to translate the data collected from a patient into personalized recommendations for therapy The development of such a map is complicated by the great degree of heterogeneity MM exhibits, including subtypes at the levels of gene expression, gene mutations, chromosomal abnormalities, and clinical outcomes. Before we can establish an era of personalized medicine for all MM patients, we must understand how the subtypes at these different levels relate to one another mechanistically, and which of these features are most important for determining the risk of disease progression. Once we understand the subtypespecific drivers of disease progression and biology of relapse, we can rationalize and test which therapies are most appropriate for which patient subtypes
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