pyNBS: a Python implementation for network-based stratification of tumor mutations

Huang, Justin K.; Jia, Tongqiu; Carlin, Daniel E.; Ideker, Trey

doi:10.1093/bioinformatics/bty186

Cited by 23 publications

(30 citation statements)

References 20 publications

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“…Identification of tumor subclasses based on somatic non-synonymous mutations was performed using oncosign (v1.0) (Ciriello et al, 2013) and Network-Based Stratification (pyNBS, downloaded on 4th June 2020) (Hofree et al, 2013; Huang et al, 2018). Significantly mutated genes identified using MutsigCV (Lawrence et al, 2013), as well as genes identified as significantly mutated in HCC in at least 2 of Martincorena et al(Martincorena et al, 2018), Schultz et al (Schulze et al, 2015), Fujimoto et al (Fujimoto et al, 2016), Bailey et al (Bailey et al, 2018) (excluding TERT ) and mutated (non-synonymous) in at least 3 tumor samples were included for the clustering.…”

Section: Star Methodsmentioning

confidence: 99%

Proteogenomic characterization of hepatocellular carcinoma

Dazert

Boldanova

et al. 2021

Preprint

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We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies. We identified pathways differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. These pathways are involved in the organization of cellular components, cell cycle control, signaling pathways, transcriptional and translational control and metabolism. Analyses of CNA-mRNA and mRNA-protein correlations identified candidate driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β-catenin pathway, transcriptional control, cholesterol biosynthesis and sphingolipid metabolism. The activity of targetable kinases aurora kinase A and CDKs was upregulated. We found that CTNNB1 mutations are associated with altered phosphorylation of proteins involved in actin filament organization, whereas TP53 mutations are associated with elevated CDK1/2/5 activity and altered phosphorylation of proteins involved in lipid and mRNA metabolism. Integrative clustering identified HCC subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our analysis provides insights into the molecular processes underlying HCCs.

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Section: Star Methodsmentioning

confidence: 99%

Proteogenomic characterization of hepatocellular carcinoma

Dazert

Boldanova

et al. 2021

Preprint

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“…If the pathway is not enriched, 0 is inserted into that entry. We used this matrix for implementing the non negative matrix factorization without a network regularizer and then consensus clustering from pyNBS package which is a Python implementation of NBS [65]. The identified groups were searched for if any identified spatial patch tends to represent a group using hypergeometric testing.…”

Section: Methodsmentioning

confidence: 99%

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Dinçer¹,

Kaya²,

Keskin

et al. 2019

PLoS Comput Biol

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Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in “patches” which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

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“…Smooth mutations over a gene interaction network. The gene-gene interaction network used in this example contains high-confidence cancer-specific interactions 18 . This specific network effectively clusters tumour samples of patients, distinguishing them by tumour type and time of survival.…”

Section: Use Casesmentioning

confidence: 99%

“…genoFile <-paste(system.file("extdata",package="netDx"), "TGCT_mutSmooth_geno.txt",sep=getFileSep()) geno <-read.delim(genoFile,sep="\t",header=TRUE,as.is=TRUE) phenoFile <-paste(system.file("extdata",package="netDx"), "TGCT_mutSmooth_pheno.txt",sep=getFileSep()) pheno <-read.delim(phenoFile,sep="\t",header=TRUE,as.is=TRUE) rownames(pheno) <-pheno$ID Smooth mutations over a gene interaction network. The gene-gene interaction network used in this example contains high-confidence cancer-specific interactions 18 . This specific network effectively clusters tumour samples of patients, distinguishing them by tumour type and time of survival.…”

Section: Path_grlistmentioning

confidence: 99%

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

et al. 2021

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Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

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pyNBS: a Python implementation for network-based stratification of tumor mutations

Abstract: The package, along with examples and data, can be downloaded and installed from the URL https://github.com/idekerlab/pyNBS.

Cited by 23 publications

References 20 publications

Proteogenomic characterization of hepatocellular carcinoma

Proteogenomic characterization of hepatocellular carcinoma

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

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