Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

The use of biological networks such as protein–protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes.

The use of biological networks such as protein-protein interaction and transcriptional regulatory networks is becoming an integral part of biological research in the genomics era. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new "communities" of genes that carry out key cellular processes. Changes in community structure can be detected by maximizing a modularity-based score, but because biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a computational method that samples networks with fixed node strengths to identify a null distribution and assess the robustness of observed changes in network structure. In contrast with other approaches, such as consensus clustering or established network generative models, CRANE produces more biologically realistic results and performs better in simulations. When applied to breast and ovarian cancer networks, CRANE improves the recovery of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes. CRANE is a general tool that can be applied in tandem with a variety of stochastic community detection methods to evaluate the veracity of their results.

The tumor suppressor gene adenomatous polyposis coli (APC) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative (APCmut–) CRCs. We analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APCmut– and APC mutation-positive (APCmut+) microsatellite stable CRCs. Transcriptionally, APCmut– CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43, increased expression of the WNT agonist RSPO3, activating mutation of BRAF, or increased methylation and decreased expression of AXIN2. APCmut– CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. APCmut– CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.

Despite significant progress in understanding the genetic landscape of T-cell acute lymphoblastic leukemia (T-ALL), the discovery of novel therapeutic targets has been difficult. Our results demonstrate that the levels of PIM1 protein kinase is elevated in early T-cell precursor ALL (ETP-ALL) but not in mature T-ALL primary samples. Small-molecule PIM inhibitor (PIMi) treatment decreases leukemia burden in ETP-ALL. However, treatment of animals carrying ETP-ALL with PIMi was not curative. To model other pathways that could be targeted to complement PIMi activity, HSB-2 cells, previously characterized as a PIMi-sensitive T-ALL cell line, were grown in increasing doses of PIMi. Gene set enrichment analysis of RNA sequencing data and functional enrichment of network modules demonstrated that the HOXA9, mTOR, MYC, NFkB, and PI3K-AKT pathways were activated in HSB-2 cells after long-term PIM inhibition. Reverse phase protein array-based pathway activation mapping demonstrated alterations in the mTOR, PI3K-AKT, and NFkB pathways, as well. PIMi-tolerant HSB-2 cells contained phosphorylated RelA-S536 consistent with activation of the NFkB pathway. The combination of NFkB and PIMis markedly reduced the proliferation in PIMiresistant leukemic cells showing that this pathway plays an important role in driving the growth of T-ALL. Together these results demonstrate key pathways that are activated when HSB-2 cell line develop resistance to PIMi and suggest pathways that can be rationally targeted in combination with PIM kinases to inhibit T-ALL growth.