SubpathwayMiner: a software package for flexible identification of pathways

With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates subpathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up to- date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (~100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/

TumorFusions: an integrative resource for cancer-associated transcript fusions.

Gene fusion represents a class of molecular aberrations in cancer and has been exploited for therapeutic purposes. In this paper we describe TumorFusions, a data portal that catalogues 20 731 gene fusions detected in 9966 well characterized cancer samples and 648 normal specimens from The Cancer Genome Atlas (TCGA). The portal spans 33 cancer types in TCGA. Fusion transcripts were identified via a uniform pipeline, including filtering against a list of 3838 transcript fusions detected in a panel of 648 non-neoplastic samples. Fusions were mapped to somatic DNA rearrangements identified using whole genome sequencing data from 561 cancer samples as a means of validation. We observed that 65% of transcript fusions were associated with a chromosomal alteration, which is annotated in the portal. Other features of the portal include links to SNP array-based copy number levels and mutational patterns, exon and transcript level expressions of the partner genes, and a network-based centrality score for prioritizing functional fusions. Our portal aims to be a broadly applicable and user friendly resource for cancer gene annotation and is publicly available at http://www.tumorfusions.org. Nucleic Acids Res 2018 Jan 4; 46(D1):D1144-D1149

Lineage-coupled clonal capture identifies clonal evolution mechanisms and vulnerabilities of BRAF

Targeted cancer therapies have revolutionized treatment but their efficacies are limited by the development of resistance driven by clonal evolution within tumors. We developed CAPTURE , a single-cell barcoding approach to comprehensively trace clonal dynamics and capture live lineage-coupled resistant cells for in-depth multi-omics analysis and functional exploration. We demonstrate that heterogeneous clones, either preexisting or emerging from drug-tolerant persister cells, dominated resistance to vemurafenib in BRA

Suppression of RAF/MEK or PI3K synergizes cytotoxicity of receptor tyrosine kinase inhibitors in glioma tumor-initiating cells

Additional file 5. Data of RPPA analysis

Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM\u27s natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711

Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma.

BACKGROUND: Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. RESULTS: We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCO CONCLUSIONS: Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy

Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers

The Implications of Relationships between Human Diseases and Metabolic Subpathways

One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the “k-clique” subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

SummaryWe describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers