Identifying Network Biomarkers of Cancer By Sample-Specific Differential Network

Abstract Abundant datasets generated from various big science projects on diseases have presented great challenges and opportunities, which are contributed to unfold the complexity of diseases. The discovery of disease- associated molecular networks for each individual plays an important role in personalized therapy and precision treatment of cancer based on the reference networks. However, there are no effective ways to distinguish the consistency of different reference networks. In this study, we developed a statistical method, i.e. a sample-specific differential network (SSDN), to construct and analyze such networks based on gene expression of a single sample against a reference dataset. We proved that the SSDN is structurally consistent even with different reference datasets if the reference dataset can follow certain conditions. The SSDN also can be used to identify patient-specific disease modules or network biomarkers as well as predict the potential driver genes of a tumor sample.

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Ranking cancer drivers via betweenness-based outlier detection and random walks

BMC Bioinformatics ◽

10.1186/s12859-021-03989-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Cesim Erten ◽

Aissa Houdjedj ◽

Hilal Kazan

Keyword(s):

Cancer Genomics ◽

Interaction Network ◽

Molecular Data ◽

Alternative Methods ◽

Patient Specific ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Protein Protein Interaction ◽

Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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Integrated multi-omics analyses on patient-derived CRC organoids highlight altered molecular pathways in colorectal cancer progression involving PTEN

Journal of Experimental & Clinical Cancer Research ◽

10.1186/s13046-021-01986-8 ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Marta Codrich ◽

Emiliano Dalla ◽

Catia Mio ◽

Giulia Antoniali ◽

Matilde Clarissa Malfatti ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Progression ◽

Cell Model ◽

Patient Specific ◽

Driver Genes ◽

Proteomic Data ◽

Whole Exome ◽

Molecular Stability ◽

Culturing Conditions ◽

Colorectal Cancer Progression

Abstract Background Colorectal cancer (CRC) represents the fourth leading cause of cancer-related deaths. The heterogeneity of CRC identity limits the usage of cell lines to study this type of tumor because of the limited representation of multiple features of the original malignancy. Patient-derived colon organoids (PDCOs) are a promising 3D-cell model to study tumor identity for personalized medicine, although this approach still lacks detailed characterization regarding molecular stability during culturing conditions. Correlation analysis that considers genomic, transcriptomic, and proteomic data, as well as thawing, timing, and culturing conditions, is missing. Methods Through integrated multi–omics strategies, we characterized PDCOs under different growing and timing conditions, to define their ability to recapitulate the original tumor. Results Whole Exome Sequencing allowed detecting temporal acquisition of somatic variants, in a patient-specific manner, having deleterious effects on driver genes CRC-associated. Moreover, the targeted NGS approach confirmed that organoids faithfully recapitulated patients’ tumor tissue. Using RNA-seq experiments, we identified 5125 differentially expressed transcripts in tumor versus normal organoids at different time points, in which the PTEN pathway resulted of particular interest, as also confirmed by further phospho-proteomics analysis. Interestingly, we identified the PTEN c.806_817dup (NM_000314) mutation, which has never been reported previously and is predicted to be deleterious according to the American College of Medical Genetics and Genomics (ACMG) classification. Conclusion The crosstalk of genomic, transcriptomic and phosphoproteomic data allowed to observe that PDCOs recapitulate, at the molecular level, the tumor of origin, accumulating mutations over time that potentially mimic the evolution of the patient’s tumor, underlining relevant potentialities of this 3D model.

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Inferring perturbation profiles of cancer samples

Bioinformatics ◽

10.1093/bioinformatics/btab113 ◽

2021 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Indirect Evidence ◽

R Package ◽

The Cancer Genome Atlas ◽

Supplementary Information ◽

Patient Specific ◽

Driver Genes ◽

Cancer Driver ◽

Molecular Alterations ◽

Incomplete Coverage ◽

Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

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Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2016/2090286 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Xindong Zhang ◽

Lin Gao ◽

Zhi-Ping Liu ◽

Songwei Jia ◽

Luonan Chen

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Lung Adenocarcinoma ◽

Early Stage ◽

Methylation Data ◽

Driver Genes ◽

Differential Network Analysis ◽

Differential Network ◽

Aberrant Dna Methylation ◽

Never Smokers

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.

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Dissecting molecular network structures using a network subgraph approach

PeerJ ◽

10.7717/peerj.9556 ◽

2020 ◽

Vol 8 ◽

pp. e9556

Author(s):

Chien-Hung Huang ◽

Efendi Zaenudin ◽

Jeffrey J.P. Tsai ◽

Nilubon Kurubanjerdjit ◽

Eskezeia Y. Dessie ◽

...

Keyword(s):

Biological Networks ◽

Spectral Graph Theory ◽

Molecular Networks ◽

Driver Genes ◽

Complexity Measures ◽

Cellular Processes ◽

Graph Energy ◽

Energy Cutoff ◽

Cancer Networks ◽

Irreducible Graphs

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

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PRODIGY: personalized prioritization of driver genes

Bioinformatics ◽

10.1093/bioinformatics/btz815 ◽

2019 ◽

Author(s):

Gal Dinstag ◽

Ron Shamir

Keyword(s):

Drug Targets ◽

Somatic Mutations ◽

Supplementary Information ◽

Gene Identification ◽

Centrality Measures ◽

Patient Specific ◽

Driver Gene ◽

Tree Model ◽

Driver Genes ◽

The Impact

Abstract Motivation Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge. Methods We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. Results In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further towards personalized medicine and treatment. Availability The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. Supplementary information Supplementary data are available at Bioinformatics online.

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Challenges and opportunities in patient‐specific, motion‐managed and PET/CT‐guided radiation therapy of lung cancer: review and perspective

Clinical and Translational Medicine ◽

10.1186/2001-1326-1-18 ◽

2012 ◽

Vol 1 (1) ◽

Cited By ~ 14

Author(s):

Stephen R Bowen ◽

Matthew J Nyflot ◽

Michael Gensheimer ◽

Kristi R G Hendrickson ◽

Paul E Kinahan ◽

...

Keyword(s):

Lung Cancer ◽

Radiation Therapy ◽

Patient Specific ◽

Ct Guided ◽

Cancer Review ◽

Pet Ct ◽

Challenges And Opportunities

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Colorectal cancer driver genes identified by patient specific comparison of cytogenetic microarray

Genomics Data ◽

10.1016/j.gdata.2014.02.004 ◽

2014 ◽

Vol 2 ◽

pp. 29-31 ◽

Cited By ~ 6

Author(s):

Mohammad Azhar Aziz ◽

Sathish Periyasamy ◽

Zeyad Yousef ◽

Ahmad Deeb ◽

Majed AlOtaibi

Keyword(s):

Colorectal Cancer ◽

Patient Specific ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes ◽

Specific Comparison

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European Reference Networks: challenges and opportunities

Journal of Community Genetics ◽

10.1007/s12687-021-00521-8 ◽

2021 ◽

Author(s):

Birute Tumiene ◽

Holm Graessner ◽

Irene MJ Mathijssen ◽

Alberto M Pereira ◽

Franz Schaefer ◽

...

Keyword(s):

European Reference Networks ◽

Challenges And Opportunities ◽

Reference Networks

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Big data in IBD: big progress for clinical practice

Gut ◽

10.1136/gutjnl-2019-320065 ◽

2020 ◽

Vol 69 (8) ◽

pp. 1520-1532 ◽

Cited By ~ 5

Author(s):

Nasim Sadat Seyed Tabib ◽

Matthew Madgwick ◽

Padhmanand Sudhakar ◽

Bram Verstockt ◽

Tamas Korcsmaros ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Treatment Options ◽

Health And Safety ◽

Fine Tuning ◽

Molecular Networks ◽

Data Generation ◽

Intrinsic Factors ◽

Challenges And Opportunities ◽

Efficient Integration

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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