A neurodevelopmental epigenetic programme mediated by SMARCD3-DAB1-Reelin signalling is hijacked to promote medulloblastoma metastasis.

How abnormal neurodevelopment relates to the tumour aggressiveness of medulloblastoma (MB), the most common type of embryonal tumour, remains elusive. Here we uncover a neurodevelopmental epigenomic programme that is hijacked to induce MB metastatic dissemination. Unsupervised analyses of integrated publicly available datasets with our newly generated data reveal that SMARCD3 (also known as BAF60C) regulates Disabled 1 (DAB1)-mediated Reelin signalling in Purkinje cell migration and MB metastasis by orchestrating cis-regulatory elements at the DAB1 locus. We further identify that a core set of transcription factors, enhancer of zeste homologue 2 (EZH2) and nuclear factor I X (NFIX), coordinates with the cis-regulatory elements at the SMARCD3 locus to form a chromatin hub to control SMARCD3 expression in the developing cerebellum and in metastatic MB. Increased SMARCD3 expression activates Reelin-DAB1-mediated Src kinase signalling, which results in a MB response to Src inhibition. These data deepen our understanding of how neurodevelopmental programming influences disease progression and provide a potential therapeutic option for patients with MB.

GRWD1-WDR5-MLL2 Epigenetic Complex Mediates H3K4me3 Mark and Is Essential for Kaposi's Sarcoma-Associated Herpesvirus-Induced Cellular Transformation.

Infection by Kaposi's sarcoma-associated herpesvirus (KSHV) is causally associated with numerous cancers. The mechanism of KSHV-induced oncogenesis remains unclear. By performing a CRISPR-Cas9 screening in a model of KSHV-induced cellular transformation of primary cells, we identified epigenetic regulators that were essential for KSHV-induced cellular transformation. Examination of TCGA data sets of the top 9 genes, including glutamate-rich WD repeat containing 1 (GRWD1), a WD40 family protein upregulated by KSHV, that had positive effects on cell proliferation and survival of KSHV-transformed cells (KMM) but not the matched primary cells (MM), uncovered the predictive values of their expressions for patient survival in numerous types of cancer. We revealed global epigenetic remodeling including H3K4me3 epigenetic active mark in KMM cells compared to MM cells. Knockdown of GRWD1 inhibited cell proliferation, cellular transformation, and tumor formation and caused downregulation of global H3K4me3 mark in KMM cells. GRWD1 interacted with WD repeat domain 5 (WDR5), the core protein of H3K4 methyltransferase complex, and several H3K4me3 methyltransferases, including myeloid leukemia 2 (MLL2). Knockdown of WDR5 and MLL2 phenocopied GRWD1 knockdown, caused global reduction of H3K4me3 mark, and altered the expression of similar sets of genes. Transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses further identified common and distinct cellular genes and pathways that were regulated by GRWD1, WDR5, and MLL2. These results indicate that KSHV hijacks the GRWD1-WDR5-MLL2 epigenetic complex to regulate H3K4me3 methylation of specific genes, which is essential for KSHV-induced cellular transformation. Our work has identified an epigenetic complex as a novel therapeutic target for KSHV-induced cancers. IMPORTANCE By performing a genome-wide CRISPR-Cas9 screening, we have identified cellular epigenetic regulators that are essential for KSHV-induced cellular transformation. Among them, GRWD1 regulates epigenetic active mark H3K4me3 by interacting with WDR5 and MLL2 and recruiting them to chromatin loci of specific genes in KSHV-transformed cells. Hence, KSHV hijacks the GRWD1-WDR5-MLL2 complex to remodel cellular epigenome and induce cellular transformation. Since the dysregulation of GRWD1 is associated with poor prognosis in several types of cancer, GRWD1 might also be a critical driver in other viral or nonviral cancers.

The Role of Fibroblast Growth Factor 19 in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the fifth most common type of cancer and the third leading cause of cancer-related deaths worldwide. Liver resection or liver transplantation is the most effective therapy for HCC because drugs approved by the US Food and Drug Administration to treat patients with unresectable HCC have an unfavorable overall survival rate. Therefore, the development of biomarkers for early diagnosis and effective therapy strategies are still necessary to improve patient outcomes. Fibroblast growth factor (FGF) 19 was amplified in patients with HCC from various studies, including patients from The Cancer Genome Atlas. FGF19 plays a syngeneic function with other signaling pathways in primary liver cancer development, such as epidermal growth factor receptor, Wnt/ß-catenin, the endoplasmic reticulum-related signaling pathway, STAT3/IL-6, RAS, and extracellular signal-regulated protein kinase, among others. The current review presents a comprehensive description of the FGF19 signaling pathway involved in liver cancer development. The use of big data and bioinformatic analysis can provide useful clues for further studies of the FGF19 pathway in HCC, including its application as a biomarker, targeted therapy, and combination therapy strategies.

ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network.

Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.

BEM: Mining Coregulation Patterns in Transcriptomics via Boolean Matrix Factorization.

MOTIVATION: The matrix factorization is an important way to analyze coregulation patterns in transcriptomic data, which can reveal the tumor signal perturbation status and subtype classification. However, current matrix factorization methods do not provide clear bicluster structure. Furthermore, these algorithms are based on the assumption of linear combination, which may not be sufficient to capture the coregulation patterns. RESULTS: We presented a new algorithm for Boolean matrix factorization (BMF) via expectation maximization (BEM). BEM is more aligned with the molecular mechanism of transcriptomic coregulation and can scale to matrix with over 100 million data points. Synthetic experiments showed that BEM outperformed other BMF methods in terms of reconstruction error. Real-world application demonstrated that BEM is applicable to all kinds of transcriptomic data, including bulk RNA-seq, single-cell RNA-seq and spatial transcriptomic datasets. Given appropriate binarization, BEM was able to extract coregulation patterns consistent with disease subtypes, cell types or spatial anatomy. AVAILABILITY AND IMPLEMENTATION: Python source code of BEM is available on https://github.com/LifanLiang/EM_BMF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes.

Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

Integrating data and knowledge to identify functional modules of genes: a multilayer approach.

BACKGROUND: Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification. RESULTS: Our new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery. CONCLUSION: Higher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.

Phenotypic Plasticity of Invasive Edge Glioma Stem-like Cells in Response to Ionizing Radiation.

Unresectable glioblastoma (GBM) cells in the invading tumor edge can act as seeds for recurrence. The molecular and phenotypic properties of these cells remain elusive. Here, we report that the invading edge and tumor core have two distinct types of glioma stem-like cells (GSCs) that resemble proneural (PN) and mesenchymal (MES) subtypes, respectively. Upon exposure to ionizing radiation (IR), GSCs, initially enriched for a CD133+ PN signature, transition to a CD109+ MES subtype in a C/EBP-ß-dependent manner. Our gene expression analysis of paired cohorts of patients with primary and recurrent GBMs identified a CD133-to-CD109 shift in tumors with an MES recurrence. Patient-derived CD133-/CD109+ cells are highly enriched with clonogenic, tumor-initiating, and radiation-resistant properties, and silencing CD109 significantly inhibits these phenotypes. We also report a conserved regulation of YAP/TAZ pathways by CD109 that could be a therapeutic target in GBM.

Prolonged Cold Ischemia Induces Necroptotic Cell Death in Ischemia-Reperfusion Injury and Contributes to Primary Graft Dysfunction after Lung Transplantation.

Primary graft dysfunction (PGD) is a major cause of morbidity and mortality after lung transplantation. Ischemia-reperfusion injury (IRI) is a key event that contributes to PGD, though complex interactions affect donor lungs status, such as preceding brain death (BD), hemorrhagic shock (HS), and pre-engraftment lung management, the latter recognized as important risk factors for PGD. We hypothesized that a multi-hit isogenic mouse model of lung transplantation is more closely linked to PGD than IRI alone. Left lung transplants were performed between inbred C57BL/6 mice. A one-hit model of IRI was established by inducing cold ischemia (CI) of the donor lungs at 0°C for 1, 72, or 96 hours before engraftment. Multi-hit models were established by inducing 24 hours of HS and/or 3 hours of BD before 24 hours of CI. The recipients were killed at 24 hours after transplant and lung graft samples were analyzed. In the one-hit model of IRI, up to 72-hour CI time resulted in minimal cellular infiltration near small arteries after 24-hour reperfusion. Extension of CI time to 96 hours led to increased cellular infiltration and necroptotic pathway activation, without evidence of apoptosis, after 24-hour reperfusion. In a multi-hit model of PGD, "HS + BD + IRI" demonstrated increased lung injury, cellular infiltration, and activation of necroptotic and apoptotic pathways compared with IRI alone. Treatment with an inhibitor of receptor-interacting protein kinase 1 kinase, necrostatin-1, resulted in a significant decrease of downstream necroptotic pathway activation in both single- and multi-hit models of IRI. Thus, activation of necroptosis is a central event in IRI after prolonged CI, though it may not be sufficient to cause PGD alone. Pathological evaluation of donor lungs after CI-induced IRI, in conjunction with pre-engraftment donor lung factors in our multi-hit model, demonstrated early evidence of lung injury consistent with PGD. Our findings support the premise that pre-existing donor lung status is more important than CI time alone for inflammatory pathway activation in PGD, which may have important clinical implications for donor lung retrieval.

Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.

BACKGROUND: Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming. RESULTS: In this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations. CONCLUSIONS: Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .

A novel method of using Deep Belief Networks and genetic perturbation data to search for yeast signaling pathways.

Perturbing a signaling system with a serial of single gene deletions and then observing corresponding expression changes in model organisms, such as yeast, is an important and widely used experimental technique for studying signaling pathways. People have developed different computational methods to analyze the perturbation data from gene deletion experiments for exploring the signaling pathways. The most popular methods/techniques include K-means clustering and hierarchical clustering techniques, or combining the expression data with knowledge, such as protein-protein interactions (PPIs) or gene ontology (GO), to search for new pathways. However, these methods neither consider nor fully utilize the intrinsic relation between the perturbation of a pathway and expression changes of genes regulated by the pathway, which served as the main motivation for developing a new computational method in this study. In our new model, we first find gene transcriptomic modules such that genes in each module are highly likely to be regulated by a common signal. We then use the expression status of those modules as readouts of pathway perturbations to search for up-stream pathways. Systematic evaluation, such as through gene ontology enrichment analysis, has provided evidence that genes in each transcriptomic module are highly likely to be regulated by a common signal. The PPI density analysis and literature search revealed that our new perturbation modules are functionally coherent. For example, the literature search revealed that 9 genes in one of our perturbation module are related to cell cycle and all 10 genes in another perturbation module are related by DNA damage, with much evidence from the literature coming from in vitro or/and in vivo verifications. Hence, utilizing the intrinsic relation between the perturbation of a pathway and the expression changes of genes regulated by the pathway is a useful method of searching for signaling pathways using genetic perturbation data. This model would also be suitable for analyzing drug experiment data, such as the CMap data, for finding drugs that perturb the same pathways.

Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma.

Glioma stem cells (GSCs), a subpopulation of tumor cells, contribute to tumor heterogeneity and therapy resistance. Gene expression profiling classified glioblastoma (GBM) and GSCs into four transcriptomically-defined subtypes. Here, we determined the DNA methylation signatures in transcriptomically pre-classified GSC and GBM bulk tumors subtypes. We hypothesized that these DNA methylation signatures correlate with gene expression and are uniquely associated either with only GSCs or only GBM bulk tumors. Additional methylation signatures may be commonly associated with both GSCs and GBM bulk tumors, i.e., common to non-stem-like and stem-like tumor cell populations and correlating with the clinical prognosis of glioma patients. We analyzed Illumina 450K methylation array and expression data from a panel of 23 patient-derived GSCs. We referenced these results with The Cancer Genome Atlas (TCGA) GBM datasets to generate methylomic and transcriptomic signatures for GSCs and GBM bulk tumors of each transcriptomically pre-defined tumor subtype. Survival analyses were carried out for these signature genes using publicly available datasets, including from TCGA. We report that DNA methylation signatures in proneural and mesenchymal tumor subtypes are either unique to GSCs, unique to GBM bulk tumors, or common to both. Further, dysregulated DNA methylation correlates with gene expression and clinical prognoses. Additionally, many previously identified transcriptionally-regulated markers are also dysregulated due to DNA methylation. The subtype-specific DNA methylation signatures described in this study could be useful for refining GBM sub-classification, improving prognostic accuracy, and making therapeutic decisions.

Activation of the Receptor Tyrosine Kinase AXL Regulates the Immune Microenvironment in Glioblastoma.

Glioblastoma (GBM) is a lethal disease with no effective therapies available. We previously observed upregulation of the TAM (Tyro-3, Axl, and Mer) receptor tyrosine kinase family member AXL in mesenchymal GBM and showed that knockdown of AXL induced apoptosis of mesenchymal, but not proneural, glioma sphere cultures (GSC). In this study, we report that BGB324, a novel small molecule inhibitor of AXL, prolongs the survival of immunocompromised mice bearing GSC-derived mesenchymal GBM-like tumors. We show that protein S (PROS1), a known ligand of other TAM receptors, was secreted by tumor-associated macrophages/microglia and subsequently physically associated with and activated AXL in mesenchymal GSC. PROS1-driven phosphorylation of AXL (pAXL) induced NFκB activation in mesenchymal GSC, which was inhibited by BGB324 treatment. We also found that treatment of GSC-derived mouse GBM tumors with nivolumab, a blocking antibody against the immune checkpoint protein PD-1, increased intratumoral macrophages/microglia and activation of AXL. Combinatorial therapy with nivolumab plus BGB324 effectively prolonged the survival of mice bearing GBM tumors. Clinically, expression of AXL or PROS1 was associated with poor prognosis for patients with GBM. Our results suggest that the PROS1-AXL pathway regulates intrinsic mesenchymal signaling and the extrinsic immune microenvironment, contributing to the growth of aggressive GBM tumors.Significance: These findings suggest that development of combination treatments of AXL and immune checkpoint inhibitors may provide benefit to patients with GBM. Cancer Res; 78(11); 3002-13. ©2018 AACR.

MST4 Phosphorylation of ATG4B Regulates Autophagic Activity, Tumorigenicity, and Radioresistance in Glioblastoma.

ATG4B stimulates autophagy by promoting autophagosome formation through reversible modification of ATG8. We identify ATG4B as a substrate of mammalian sterile20-like kinase (STK) 26/MST4. MST4 phosphorylates ATG4B at serine residue 383, which stimulates ATG4B activity and increases autophagic flux. Inhibition of MST4 or ATG4B activities using genetic approaches or an inhibitor of ATG4B suppresses autophagy and the tumorigenicity of glioblastoma (GBM) cells. Furthermore, radiation induces MST4 expression, ATG4B phosphorylation, and autophagy. Inhibiting ATG4B in combination with radiotherapy in treating mice with intracranial GBM xenograft markedly slows tumor growth and provides a significant survival benefit. Our work describes an MST4-ATG4B signaling axis that influences GBM autophagy and malignancy, and whose therapeutic targeting enhances the anti-tumor effects of radiotherapy.

A signal-based method for finding driver modules of breast cancer metastasis to the lung.

Tumor metastasis is mainly caused by somatic genomic alterations (SGAs) that perturb pathways regulating metastasis-relevant activities and thus help the primary tumor to adapt to the new microenvironment. Identifying drivers of metastasis, i.e. SGAs, sheds light on the metastasis mechanism and provides guidance for targeted therapy. In this paper, we introduce a novel method to search for SGAs driving breast cancer metastasis to the lung. First, we search for transcriptomic modules with genes that are differentially expressed in breast cell lines with strong metastatic activities to the lung and co-expressed in a large number of breast tumors. Then, for each transcriptomic module, we search for a set of SGA genes (driver modules) such that genes in each driver module carry a common signal regulating the transcriptomic module. Evaluations indicate that many genes in driver modules are indeed related to metastasis, and our methods have identified many new driver candidates. We further choose two novel metastatic driver genes, BCL2L11 and CDH9, for in vitro verification. The wound healing assay reveals that inhibiting either BCL2L11 or CDH9 will enhance the migration of cell lines, which provides evidence that these two genes are suppressors of tumor metastasis.

Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.

Defining processes that are synthetic lethal with p53 mutations in cancer cells may reveal possible therapeutic strategies. In this study, we report the development of a signal-oriented computational framework for cancer pathway discovery in this context. We applied our bipartite graph-based functional module discovery algorithm to identify transcriptomic modules abnormally expressed in multiple tumors, such that the genes in a module were likely regulated by a common, perturbed signal. For each transcriptomic module, we applied our weighted k-path merge algorithm to search for a set of somatic genome alterations (SGA) that likely perturbed the signal, that is, the candidate members of the pathway that regulate the transcriptomic module. Computational evaluations indicated that our methods-identified pathways were perturbed by SGA. In particular, our analyses revealed that SGA affecting TP53, PTK2, YWHAZ, and MED1 perturbed a set of signals that promote cell proliferation, anchor-free colony formation, and epithelial-mesenchymal transition (EMT). These proteins formed a signaling complex that mediates these oncogenic processes in a coordinated fashion. Disruption of this signaling complex by knocking down PTK2, YWHAZ, or MED1 attenuated and reversed oncogenic phenotypes caused by mutant p53 in a synthetic lethal manner. This signal-oriented framework for searching pathways and therapeutic targets is applicable to all cancer types, thus potentially impacting precision medicine in cancer. Cancer Res; 76(23); 6785-94. ©2016 AACR.

A regulatory circuit of miR-125b/miR-20b and Wnt signalling controls glioblastoma phenotypes through FZD6-modulated pathways.

Molecularly defined subclassification is associated with phenotypic malignancy of glioblastoma (GBM). However, current understanding of the molecular basis of subclass conversion that is often involved in GBM recurrence remain rudimentary at best. Here we report that canonical Wnt signalling that is active in proneural (PN) but inactive in mesenchymal (MES) GBM, along with miR-125b and miR-20b that are expressed at high levels in PN compared with MES GBM, comprise a regulatory circuit involving TCF4-miR-125b/miR-20b-FZD6. FZD6 acts as a negative regulator of this circuit by activating CaMKII-TAK1-NLK signalling, which, in turn, attenuates Wnt pathway activity while promoting STAT3 and NF-κB signalling that are important regulators of the MES-associated phenotype. These findings are confirmed by targeting differentially enriched pathways in PN versus MES GBM that results in inhibition of distinct GBM subtypes. Correlative expressions of the components of this circuit are prognostic relevant for clinical GBM. Our findings provide insights for understanding GBM pathogenesis and for improving treatment of GBM.

An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem.

BACKGROUND: The mutual exclusivity of somatic genome alterations (SGAs), such as somatic mutations and copy number alterations, is an important observation of tumors and is widely used to search for cancer signaling pathways or SGAs related to tumor development. However, one problem with current methods that use mutual exclusivity is that they are not signal-based; another problem is that they use heuristic algorithms to handle the NP-hard problems, which cannot guarantee to find the optimal solutions of their models. METHOD: In this study, we propose a novel signal-based method that utilizes the intrinsic relationship between SGAs on signaling pathways and expression changes of downstream genes regulated by pathways to identify cancer signaling pathways using the mutually exclusive property. We also present a relatively efficient exact algorithm that can guarantee to obtain the optimal solution of the new computational model. RESULTS: We have applied our new model and exact algorithm to the breast cancer data. The results reveal that our new approach increases the capability of finding better solutions in the application of cancer research. Our new exact algorithm has a time complexity of [Formula: see text](Note: Following the recent convention, we use a star * to represent that the polynomial part of the time complexity is neglected), which has solved the NP-hard problem of our model efficiently. CONCLUSION: Our new method and algorithm can discover the true causes behind the phenotypes, such as what SGA events lead to abnormality of the cell cycle or make the cell metastasis lose control in tumors; thus, it identifies the target candidates for precision (or target) therapeutics.