Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments.

Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summarization and visualization of the estimated networks and optional genetic testing of dependencies revealed by the network model are demonstrated. While developed for gene perturbation experiments, TopNet models data in which nodes are both perturbed and measured. For complete details on the use and execution of this protocol, please refer to McMurray et al. (2021).

Arid1a mutation suppresses TGF-ß signaling and induces cholangiocarcinoma.

Activating KRAS mutations and functional loss of members of the SWI/SNF complex, including ARID1A, are found together in the primary liver tumor cholangiocarcinoma (CC). How these mutations cooperate to promote CC has not been established. Using murine models of hepatocyte and biliary-specific lineage tracing, we show that Kras and Arid1a mutations drive the formation of CC and tumor precursors from the biliary compartment, which are accelerated by liver inflammation. Using cultured cells, we find that Arid1a loss causes cellular proliferation, escape from cell-cycle control, senescence, and widespread changes in chromatin structure. Notably, we show that the biliary proliferative response elicited by Kras/Arid1a cooperation and tissue injury in CC is caused by failed engagement of the TGF-ß-Smad4 tumor suppressor pathway. We thus identify an ARID1A-TGF-ß-Smad4 axis as essential in limiting the biliary epithelial response to oncogenic insults, while its loss leads to biliary pre-neoplasia and CC.

Gene network modeling via TopNet reveals functional dependencies between diverse tumor-critical mediator genes.

Malignant cell transformation and the underlying reprogramming of gene expression require the cooperation of multiple oncogenic mutations. This cooperation is reflected in the synergistic regulation of non-mutant downstream genes, so-called cooperation response genes (CRGs). CRGs affect diverse hallmark features of cancer cells and are not known to be functionally connected. However, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency >50%, as indicated by genetic perturbations. Here, we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the malignant state. Our network modeling methodology, TopNet, takes the approach of incorporating uncertainty in the underlying gene perturbation data and can identify non-linear gene interactions. In the dense space of gene connectivity, TopNet reveals a sparse topological gene network architecture, effectively pinpointing functionally relevant gene interactions. Thus, among diverse potential applications, TopNet has utility for identification of non-mutant targets for cancer intervention.

A four gene signature predictive of recurrent prostate cancer.

Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature (HBEGF, HOXC13, IGFBP2, and SATB1) capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38x10-6). Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.

Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells.

Metabolic reprogramming is critical to oncogenesis, but the emergence and function of this profound reorganization remain poorly understood. Here we find that cooperating oncogenic mutations drive large-scale metabolic reprogramming, which is both intrinsic to cancer cells and obligatory for the transition to malignancy. This involves synergistic regulation of several genes encoding metabolic enzymes, including the lactate dehydrogenases LDHA and LDHB and mitochondrial glutamic pyruvate transaminase 2 (GPT2). Notably, GPT2 engages activated glycolysis to drive the utilization of glutamine as a carbon source for TCA cycle anaplerosis in colon cancer cells. Our data indicate that the Warburg effect supports oncogenesis via GPT2-mediated coupling of pyruvate production to glutamine catabolism. Although critical to the cancer phenotype, GPT2 activity is dispensable in cells that are not fully transformed, thus pinpointing a metabolic vulnerability specifically associated with cancer cell progression to malignancy.

Inhibition of COP9-signalosome (CSN) deneddylating activity and tumor growth of diffuse large B-cell lymphomas by doxycycline.

In searching for small-molecule compounds that inhibit proliferation and survival of diffuse large B-cell lymphoma (DLBCL) cells and may, therefore, be exploited as potential therapeutic agents for this disease, we identified the commonly used and well-tolerated antibiotic doxycycline as a strong candidate. Here, we demonstrate that doxycycline inhibits the growth of DLBCL cells both in vitro and in mouse xenograft models. In addition, we show that doxycycline accumulates in DLBCL cells to high concentrations and affects multiple signaling pathways that are crucial for lymphomagenesis. Our data reveal the deneddylating activity of COP-9 signalosome (CSN) as a novel target of doxycycline and suggest that doxycycline may exert its effects in DLBCL cells in part through a CSN5-HSP90 pathway. Consistently, knockdown of CSN5 exhibited similar effects as doxycycline treatment on DLBCL cell survival and HSP90 chaperone function. In addition to DLBCL cells, doxycycline inhibited growth of several other types of non-Hodgkin lymphoma cells in vitro. Together, our results suggest that doxycycline may represent a promising therapeutic agent for DLBCL and other non-Hodgkin lymphomas subtypes.

Plac8 links oncogenic mutations to regulation of autophagy and is critical to pancreatic cancer progression.

Mutations in p53 and RAS potently cooperate in oncogenic transformation, and correspondingly, these genetic alterations frequently coexist in pancreatic ductal adenocarcinoma (PDA) and other human cancers. Previously, we identified a set of genes synergistically activated by combined RAS and p53 mutations as frequent downstream mediators of tumorigenesis. Here, we show that the synergistically activated gene Plac8 is critical for pancreatic cancer growth. Silencing of Plac8 in cell lines suppresses tumor formation by blocking autophagy, a process essential for maintaining metabolic homeostasis in PDA, and genetic inactivation in an engineered mouse model inhibits PDA progression. We show that Plac8 is a critical regulator of the autophagic machinery, localizing to the lysosomal compartment and facilitating lysosome-autophagosome fusion. Plac8 thus provides a mechanistic link between primary oncogenic mutations and the induction of autophagy, a central mechanism of metabolic reprogramming, during PDA progression.

On non-detects in qPCR data.

MOTIVATION: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects-those reactions failing to produce a minimum amount of signal. RESULTS: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project.

Unphosphorylated STAT5A stabilizes heterochromatin and suppresses tumor growth.

Tumor suppressors known to date impede cancer growth by arresting the cell cycle or promoting apoptosis. Here we show that unphosphorylated human STAT5A functions as a tumor suppressor capable of repressing multiple oncogenes via heterochromatin formation. Unphosphorylated STAT5A binds to heterochromatin protein 1α (HP1α) and stabilizes heterochromatin. Expressing unphosphorylated STAT5A or HP1α inhibits colon cancer growth in mouse xenograft models. Transcriptome profiling shows that expressing an unphosphorylatable STAT5A has similar effects to overexpressing HP1α in global gene expression. Notably, the majority of the genes commonly repressed by unphosphorylated STAT5A and HP1α have been implicated in cancer development. Finally, down-regulation, somatic mutations, and deletions of STAT5 genes are found in certain human cancers. These results suggest that unphosphorylated STAT5A may epigenetically suppress tumor growth by promoting heterochromatin formation.

The Birt-Hogg-Dubé tumor suppressor Folliculin negatively regulates ribosomal RNA synthesis.

Birt-Hogg-Dubé syndrome (BHD) is a human cancer disorder caused by mutations in the tumor suppressor gene Folliculin (FLCN) with unknown biological functions. Here, we show that the Drosophila homolog of FLCN, dFLCN (a.k.a. dBHD) localizes to the nucleolus and physically interacts with the 19S proteasomal ATPase, Rpt4, a nucleolar resident and known regulator of rRNA transcription. Downregulation of dFLCN resulted in an increase in nucleolar volume and upregulation of rRNA synthesis, whereas dFLCN overexpression reduced rRNA transcription and counteracted the effects of Rpt4 on rRNA production by preventing the association of Rpt4 with the rDNA locus. We further show that human FLCN exhibited evolutionarily conserved function and that Rpt4 knockdown inhibits the growth of FLCN-deficient human renal cancer cells in mouse xenografts. Our study suggests that FLCN functions as a tumor suppressor by negatively regulating rRNA synthesis.

Anticancer activity of the cholesterol exporter ABCA1 gene.

The ABCA1 protein mediates the transfer of cellular cholesterol across the plasma membrane to apolipoprotein A-I. Loss-of-function mutations in the ABCA1 gene induce Tangier disease and familial hypoalphalipoproteinemia, both cardiovascular conditions characterized by abnormally low levels of serum cholesterol, increased cholesterol in macrophages, and subsequent formation of vascular plaque. Increased intracellular cholesterol levels are also frequently found in cancer cells. Here, we demonstrate anticancer activity of ABCA1 efflux function, which is compromised following inhibition of ABCA1 gene expression by oncogenic mutations or cancer-specific ABCA1 loss-of-function mutations. In concert with elevated cholesterol synthesis found in cancer cells, ABCA1 deficiency allows for increased mitochondrial cholesterol, inhibits release of mitochondrial cell death-promoting molecules, and thus facilitates cancer cell survival, suggesting that elevated mitochondrial cholesterol is essential to the cancer phenotype.

Gene sets identified with oncogene cooperativity analysis regulate in vivo growth and survival of leukemia stem cells.

Leukemia stem cells (LSCs) represent a biologically distinct subpopulation of myeloid leukemias, with reduced cell cycle activity and increased resistance to therapeutic challenge. To better characterize key properties of LSCs, we employed a strategy based on identification of genes synergistically dysregulated by cooperating oncogenes. We hypothesized that such genes, termed "cooperation response genes" (CRGs), would represent regulators of LSC growth and survival. Using both a primary mouse model and human leukemia specimens, we show that CRGs comprise genes previously undescribed in leukemia pathogenesis in which multiple pathways modulate the biology of LSCs. In addition, our findings demonstrate that the CRG expression profile can be used as a drug discovery tool for identification of compounds that selectively target the LSC population. We conclude that CRG-based analyses provide a powerful means to characterize the basic biology of LSCs as well as to identify improved methods for therapeutic targeting.

Kras(G12D) and p53 mutation cause primary intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (IHCC) is a primary cancer of the liver with an increasing incidence and poor prognosis. Preclinical studies of the etiology and treatment of this disease are hampered by the relatively small number of available IHCC cell lines or genetically faithful animal models. Here we report the development of a genetically engineered mouse model of IHCC that incorporates two of the most common mutations in human IHCC, activating mutations of Kras (Kras(G12D)) and deletion of p53. Tissue-specific activation of Kras(G12D) alone resulted in the development of invasive IHCC with low penetrance and long latency. Latency was shortened by combining Kras(G12D) activation with heterozygous or homozygous deletion of p53 (mean survival of 56 weeks vs. 19 weeks, respectively), which also resulted in widespread local and distant metastasis. Serial analysis showed that the murine models closely recapitulated the multistage histopathologic progression of the human disease, including the development of stroma-rich tumors and the premalignant biliary lesions, intraductal papillary biliary neoplasms (IPBN), and Von Meyenburg complexes (VMC; also known as biliary hamartomas). These findings establish a new genetically and histopathologically faithful model of IHCC and lend experimental support to the hypothesis that IPBN and VMC are precursors to invasive cancers.

Fitting Boolean networks from steady state perturbation data.

Gene perturbation experiments are commonly used for the reconstruction of gene regulatory networks. Typical experimental methodology imposes persistent changes on the network. The resulting data must therefore be interpreted as a steady state from an altered gene regulatory network, rather than a direct observation of the original network. In this article an implicit modeling methodology is proposed in which the unperturbed network of interest is scored by first modeling the persistent perturbation, then predicting the steady state, which may then be compared to the observed data. This results in a many-to-one inverse problem, so a computational Bayesian approach is used to assess model uncertainty. The methodology is first demonstrated on a number of synthetic networks. It is shown that the Bayesian approach correctly assigns high posterior probability to the network structure and steady state behavior. Further, it is demonstrated that where uncertainty of model features is indicated, the uncertainty may be accurately resolved with further perturbation experiments. The methodology is then applied to the modeling of a gene regulatory network using perturbation data from nine genes which have been shown to respond synergistically to known oncogenic mutations. A hypothetical model emerges which conforms to reported regulatory properties of these genes. Furthermore, the Bayesian methodology is shown to be consistent in the sense that multiple randomized applications of the fitting algorithm converge to an approximately common posterior density on the space of models. Such consistency is generally not feasible for algorithms which report only single models. We conclude that fully Bayesian methods, coupled with models which accurately account for experimental constraints, are a suitable tool for the inference of gene regulatory networks, in terms of accuracy, estimation of model uncertainty, and experimental design.

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype.

Understanding the molecular underpinnings of cancer is of critical importance to the development of targeted intervention strategies. Identification of such targets, however, is notoriously difficult and unpredictable. Malignant cell transformation requires the cooperation of a few oncogenic mutations that cause substantial reorganization of many cell features and induce complex changes in gene expression patterns. Genes critical to this multifaceted cellular phenotype have therefore only been identified after signalling pathway analysis or on an ad hoc basis. Our observations that cell transformation by cooperating oncogenic lesions depends on synergistic modulation of downstream signalling circuitry suggest that malignant transformation is a highly cooperative process, involving synergy at multiple levels of regulation, including gene expression. Here we show that a large proportion of genes controlled synergistically by loss-of-function p53 and Ras activation are critical to the malignant state of murine and human colon cells. Notably, 14 out of 24 'cooperation response genes' were found to contribute to tumour formation in gene perturbation experiments. In contrast, only 1 in 14 perturbations of the genes responding in a non-synergistic manner had a similar effect. Synergistic control of gene expression by oncogenic mutations thus emerges as an underlying key to malignancy, and provides an attractive rationale for identifying intervention targets in gene networks downstream of oncogenic gain- and loss-of-function mutations.

Tumor suppressor p53 restricts Ras stimulation of RhoA and cancer cell motility.

Many features of the cancer cell phenotype emerge as a result of cooperation between multiple oncogenic mutations. Here we show that activated Ras(V12) and loss of p53 function can cooperate to promote cell motility, a feature closely associated with cancer progression to malignancy. Our analysis indicates that Ras(V12) and loss of p53 synergistically induce RhoA activity, revealing a previously unknown role for p53 in tumor suppression. p53 prevents activation of RhoA and thus induction of cell motility by Ras(V12) through a simple signaling circuit, which integrates multiple inputs that converge on RhoA. Our data suggest that p53 suppresses cancer progression to malignancy by modulating the quality of Ras signaling.