Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma

Matthew A. Wall, Serdar Turkarslan, Wei-Ju Wu, Samuel A. Danziger, David J. Reiss, Mike J. Mason, Andrew P. Dervan, Matthew W. B. Trotter, Douglas Bassett, Robert M. Hershberg, Adrián López García de Lomana, Alexander V. Ratushny & Nitin S. Baliga


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.

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

Christopher L. Plaisier, Sofie O’Brien, Brady Bernard, Sheila Reynolds, Zac Simon, Chad  M. Toledo, Yu Ding, David J. Reiss, Patrick J. Paddison, Nitin S. Baliga


We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.

A Single-Cell Based Precision Medicine Approach Using Glioblastoma Patient-Specific Models.

James H. Park, Abdullah H. Feroze, Samuel N. Emerson, Anca B. Mihalas, C. Dirk Keene, Patrick J. Cimino, Adrian Lopez Garcia de Lomana, Kavya Kannan, Wei-Ju Wu, Serdar Turkarslan, Nitin S. Baliga & Anoop P. Patel


Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

Understanding the brain tumor microenvironment: Considerations to applying systems biology and immunotherapy.

Juarez TM, Carrillo JA, Achrol AA, Salomon MP, Marzese DM, Park JH, Baliga NS, Kesari S.


Patients with malignant brain cancers such as glioblastoma and brain metastases (BM) represent a population with a large unmet medical need, and a multitude of drugs have failed over decades. The current treatment modalities include surgery, radiation, and chemotherapy; yet, the median survival of patients with gliomas and BM remains abysmally low at 15 months and 2–14 months, respectively. In addition, standard treatments cause debilitating motor and neurological deficits. The paucity of effective therapies, despite intense investigation over the past several decades, represents inherent challenges to treating brain cancer and the critical knowledge gap in understanding tumor sensitivity, drug delivery, and microenvironmental shifts. Recently, immunotherapy has shown tremendous efficacy in melanoma and other cancers but has yet to revolutionize the treatment of brain cancers. However, as immunotherapy holds the promise of specifically targeting and eliminating tumor cells while sparing normal brain cells, innovative methods for investigating immunotherapy for brain cancer are essential for optimizing patient response. In this review, we will summarize the key issues and how a systems biology approach can help decipher this complexity and lead to better understanding and therapeutic targeting of the brain cancers.

 miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling.

Plaisier CL, Bare JC, Baliga NS.


Transcriptome profiling studies have produced staggering numbers of gene co-expression signatures for a variety of biological systems. A significant fraction of these signatures will be partially or fully explained by miRNA-mediated targeted transcript degradation. miRvestigator takes as input lists of co-expressed genes from Caenorhabditis elegans, Drosophila melanogaster, G. gallus, Homo sapiens, Mus musculus or Rattus norvegicus and identifies the specific miRNAs that are likely to bind to 3′ un-translated region (UTR) sequences to mediate the observed co-regulation. The novelty of our approach is the miRvestigator hidden Markov model (HMM) algorithm which systematically computes a similarity P-value for each unique miRNA seed sequence from the miRNA database miRBase to an overrepresented sequence motif identified within the 3′-UTR of the query genes. We have made this miRNA discovery tool accessible to the community by integrating our HMM algorithm with a proven algorithm for de novo discovery of miRNA seed sequences and wrapping these algorithms into a user-friendly interface. Additionally, the miRvestigator web server also produces a list of putative miRNA binding sites within 3′-UTRs of the query transcripts to facilitate the design of validation experiments. The miRvestigator is freely available at

A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers.

Christopher L. Plaisier, Min Pan, Nitin S. Baliga.


Genes regulated by the same miRNA can be discovered by virtue of their co-expression
at the transcriptional level and the presence of a conserved miRNA-binding site in their 3’ UTRs.
Using this principle we have integrated the three best performing and complementary algorithms
into a framework for inference of regulation by miRNAs (FIRM) from sets of co-expressed
genes. We demonstrate the utility of FIRM by inferring a cancer-miRNA regulatory network
through the analysis of 2,240 gene co-expression signatures from 46 cancers. By analyzing this
network for functional enrichment of known hallmarks of cancer we have discovered a subset of
13 miRNAs that regulate oncogenic processes across diverse cancers. We have performed
experiments to test predictions from this miRNA-regulatory network to demonstrate that
miRNAs of the miR-29 family (miR-29a, miR-29b and miR-29c) regulate specific genes
associated with tissue invasion and metastasis in lung adenocarcinoma. Further, we highlight the
specificity of using FIRM inferences to identify miRNA regulated genes by experimentally
validating that miR-767-5p, which partially shares the miR-29 seed sequence, regulates only a
subset of miR-29 targets. By providing mechanistic linkage between miRNA dysregulation in
cancer, their binding sites in the 3’UTRs of specific sets of co-expressed genes, and their
associations with known hallmarks of cancer, FIRM and the inferred cancer miRNA-regulatory
network will serve as a powerful public resource for discovery of potential cancer biomarkers