scholarly journals Functional network analysis reveals an immune tolerance mechanism in cancer

2020 ◽  
Vol 117 (28) ◽  
pp. 16339-16345 ◽  
Author(s):  
James C. Mathews ◽  
Saad Nadeem ◽  
Maryam Pouryahya ◽  
Zehor Belkhatir ◽  
Joseph O. Deasy ◽  
...  
Keyword(s):  
Immune Tolerance ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
The Cancer Genome Atlas ◽  
Computationally Efficient ◽  
Tolerance Mechanism ◽  
Poor Group ◽  
Cancer Genome Atlas ◽  
Pathway Analyses ◽  
Local Connection

We present a technique to construct a simplification of a feature network which can be used for interactive data exploration, biological hypothesis generation, and the detection of communities or modules of cofunctional features. These are modules of features that are not necessarily correlated, but nevertheless exhibit common function in their network context as measured by similarity of relationships with neighboring features. In the case of genetic networks, traditional pathway analyses tend to assume that, ideally, all genes in a module exhibit very similar function, independent of relationships with other genes. The proposed technique explicitly relaxes this assumption by employing the comparison of relational profiles. For example, two genes which always activate a third gene are grouped together even if they never do so concurrently. They have common, but not identical, function. The comparison is driven by an average of a certain computationally efficient comparison metric between Gaussian mixture models. The method has its basis in the local connection structure of the network and the collection of joint distributions of the data associated with nodal neighborhoods. It is benchmarked on networks with known community structures. As the main application, we analyzed the gene regulatory network in lung adenocarcinoma, finding a cofunctional module of genes including the pregnancy-specific glycoproteins (PSGs). About 20% of patients with lung, breast, uterus, and colon cancer in The Cancer Genome Atlas (TCGA) have an elevated PSG+ signature, with associated poor group prognosis. In conjunction with previous results relating PSGs to tolerance in the immune system, these findings implicate the PSGs in a potential immune tolerance mechanism of cancers.

scholarly journals Hierarchical Network Exploration using Gaussian Mixture Models

2019 ◽  
Author(s):  
James Mathews ◽  
Saad Nadeem ◽  
Maryam Pouryahya ◽  
Zehor Belkhatir ◽  
Joseph O. Deasy ◽  
...  
Keyword(s):  
Mixture Models ◽  
Regulatory Networks ◽  
Single Gene ◽  
Gaussian Mixture Models ◽  
Numerical Data ◽  
Gaussian Mixture ◽  
Computationally Efficient ◽  
Transport Distance ◽  
Gene Modules ◽  
Local Connection

AbstractWe present a framework based on optimal mass transport to construct, for a given network, a reduction hierarchy which can be used for interactive data exploration and community detection. Given a network and a set of numerical data samples for each node, we calculate a new computationally-efficient comparison metric between Gaussian Mixture Models, the Gaussian Mixture Transport distance, to determine a series of merge simplifications of the network. If only a network is given, numerical samples are synthesized from the network topology. The method has its basis in the local connection structure of the network, as well as the joint distribution of the data associated with neighboring nodes.The analysis is benchmarked on networks with known community structures. We also analyze gene regulatory networks, including the PANTHER curated database and networks inferred from the GTEx lung and breast tissue RNA profiles. Gene Ontology annotations from the EBI GOA database are ranked and superimposed to explain the salient gene modules. We find that several gene modules related to highly specific biological processes are well-coordinated in such tissues. We also find that 18 of the 50 genes of the PAM50 breast-tumor prognostic signature appear among the highly coordinated genes in a single gene module, in both the breast and lung samples. Moreover these 18 are precisely the subset of the PAM50 recently identified as the basal-like markers.

scholarly journals A new genome-wide method to identify genes with bimodal gene expression

2020 ◽  
Author(s):  
Josivan Ribeiro Justino ◽  
Clovis F. Reis ◽  
Andre Faustino Fonseca ◽  
Sandro Jose de Souza ◽  
Beatriz Stransky
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Bimodal Gene ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
The Impact

AbstractA new method is presented to detect bimodality in gene expression data using the Gaussian Mixture Models to cluster samples in each mode. We have used the method to search for bimodal genes in data from 25 tumor types available from The Cancer Genome Atlas. The method identified 554 genes with bimodal gene expression, of which 46 were identified in more than one cancer type. To further illustrate the impact of the method, we show that 96 out of the 554 genes with bimodal expression patterns presented different prognosis when patients belonging to the two expression peaks are compared. The software to execute the method and the corresponding documentation are available at https://github.com/LabBiosystemUFRN/Bimodality_Genes.

scholarly journals An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT

2019 ◽  
Author(s):  
Shaolong Cao ◽  
Zeya Wang ◽  
Fan Gao ◽  
Jingxiao Chen ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Expression Profiles ◽  
Progression Free Survival ◽  
Tumor Stroma ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Biological Information ◽  
Computationally Efficient ◽  
Multiple Cancer ◽  
Cancer Genome Atlas

AbstractThe deconvolution of transcriptomic data from heterogeneous tissues in cancer studies remains challenging. Available software faces difficulties for accurately estimating both component-specific proportions and expression profiles for individual samples. To address these challenges, we present a new R-implementation pipeline for the more accurate and efficient transcriptome deconvolution of high dimensional data from mixtures of more than two components. The pipeline utilizes the computationally efficient DeMixT R-package with OpenMP and additional cancer-specific biological information to perform three-component deconvolution without requiring data from the immune profiles. It enables a wide application of DeMixT to gene expression datasets available from cancer consortium such as the Cancer Genome Atlas (TCGA) projects, where, other than the mixed tumor samples, a handful of normal samples are profiled in multiple cancer types. We have applied this pipeline to two TCGA datasets in colorectal adenocarcinoma (COAD) and prostate adenocarcinoma (PRAD). In COAD, we found varying distributions of immune proportions across the Consensus Molecular Subtypes, from the highest to the lowest being CMS1, CMS3, CMS4 and CMS2. In PRAD, we found the immune proportions are associated with progression-free survival (p<0.01) and negatively correlated with Gleason scores (p<0.001). Our DeMixT-centered analysis protocol opens up new opportunities to investigate the tumor-stroma-immune microenvironment, by providing both proportions and component-specific expressions, and thus better define the underlying biology of cancer progression.Availability and implementation: An R package, scripts and data are available: https://github.com/wwylab/DeMixTallmaterials.

Identification of unique long non-coding RNAs as putative biomarkers for chromophobe renal cell carcinoma

2020 ◽  
Author(s):  
Guanlin Wu ◽  
Pengfei Xia ◽  
Shixian Yan ◽  
Dongming Chen ◽  
Lei Xie ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Chromophobe Renal Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas ◽  
Protein Protein Interaction Networks ◽  
Pathway Analyses

Aim: To investigate whether long non-coding RNAs (lncRNAs) can be utilized as molecular biomarkers in predicting the occurrence and progression of chromophobe renal cell carcinoma. Methods & results: Genetic and related clinical traits of chromophobe renal cell carcinoma were downloaded from the Cancer Genome Atlas and used to construct modules using weighted gene coexpression network analysis. In total, 44,889 genes were allocated into 21 coexpression modules depending on intergenic correlation. Among them, the green module was the most significant key module identified by module–trait correlation calculations ( R 2 = 0.43 and p = 4e-04). Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that genes in the green module were enriched in many pathways. Coexpression, protein–protein interaction networks, screening for differentially expressed genes, and survival analysis were used to select hub lncRNAs. Five hub lncRNAs ( TTK, CENPE, KIF2C, BUB1, and RAD51AP1) were selected out. Conclusion: Our findings suggest that the five lncRNAs may act as potential biomarkers for chromophobe renal cell carcinoma progression and prognosis.

scholarly journals Gaussian mixture models for blended photometric redshifts

2019 ◽  
Vol 490 (3) ◽  
pp. 3966-3986 ◽  
Author(s):  
Daniel M Jones ◽  
Alan F Heavens
Keyword(s):  
Mixture Models ◽  
Model Comparison ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Computationally Efficient ◽  
Redshift Distribution ◽  
Photometric Redshifts ◽  
Galaxy Surveys ◽  
Photometric Redshift ◽  
Bayesian Model Comparison

ABSTRACT Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux–redshift distribution from a set of unblended training galaxies, and Bayesian model comparison to infer the number of galaxies comprising a blended source. The use of Gaussian mixture models renders both of these applications computationally efficient and therefore suitable for upcoming galaxy surveys.

scholarly journals ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

2020 ◽  
Vol 19 (12) ◽  
pp. 2115-2124 ◽  
Author(s):  
Johannes Griss ◽  
Guilherme Viteri ◽  
Konstantinos Sidiropoulos ◽  
Vy Nguyen ◽  
Antonio Fabregat ◽  
...  
Keyword(s):  
B Cell ◽  
Plasma Cells ◽  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
The Novel ◽  
Specific Subset ◽  
Public Data ◽  
Cancer Genome Atlas ◽  
Pathway Analyses

Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

Comprehensive assessment of PD-L1 and PD-L2 dysregulation in gastrointestinal cancers

Epigenomics ◽  
2020 ◽  
Author(s):  
Qijie Zhao ◽  
Jinan Guo ◽  
Yueshui Zhao ◽  
Jing Shen ◽  
Parham Jabbarzadeh Kaboli ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Signaling Pathways ◽  
Underlying Mechanism ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Gastrointestinal Cancers ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background: PD-L1 and PD-L2 are ligands of PD-1. Their overexpression has been reported in different cancers. However, the underlying mechanism of PD-L1 and PD-L2 dysregulation and their related signaling pathways are still unclear in gastrointestinal cancers. Materials & methods: The expression of PD-L1 and PD-L2 were studied in The Cancer Genome Atlas and Genotype-Tissue Expression databases. The gene and protein alteration of PD-L1 and PD-L2 were analyzed in cBioportal. The direct transcription factor regulating PD-L1/ PD-L2 was determined with ChIP-seq data. The association of PD-L1/PD-L2 expression with clinicopathological parameters, survival, immune infiltration and tumor mutation burden were investigated with data from The Cancer Genome Atlas. Potential targets and pathways of PD-L1 and PD-L2 were determined by protein enrichment, WebGestalt and gene ontology. Results: Comprehensive analysis revealed that PD-L1 and PD-L2 were significantly upregulated in most types of gastrointestinal cancers and their expressions were positively correlated. SP1 was a key transcription factor regulating the expression of PD-L1. Conclusion: Higher PD-L1 or PD-L2 expression was significantly associated with poor overall survival, higher tumor mutation burden and more immune and stromal cell populations. Finally, HIF-1, ERBB and mTOR signaling pathways were most significantly affected by PD-L1 and PD-L2 dysregulation. Altogether, this study provided comprehensive analysis of the dysregulation of PD-L1 and PD-L2, its underlying mechanism and downstream pathways, which add to the knowledge of manipulating PD-L1/PD-L2 for cancer immunotherapy.

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