PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data

Bahceci, Istemi; Doğrusöz, Uğur; La, Konnor; Babur, Özgün; Gao, Jianjiong; Schultz, Nikolaus

doi:10.1093/bioinformatics/btx149

Cited by 50 publications

(42 citation statements)

References 16 publications

(6 reference statements)

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“…We performed all machine learning model training, testing, and evaluations using sci-kit learn (version 0.18.1) with python 3.5.2 ( Pedregosa et al, 2011 ). We processed data using a combination of pandas (version 0.20.3) and dplyr (version 0.7.1) and visualized results using a combination of seaborn (version 0.7.1), ggplot2 (version 2.2.1), and PathwayMapper ( Bahceci et al, 2017 ). R packages were run on R version 3.4.0.…”

Section: Star ★ Methodsmentioning

confidence: 99%

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Way¹,

et al. 2018

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SUMMARY Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.

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Section: Star ★ Methodsmentioning

confidence: 99%

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Way¹,

et al. 2018

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“…Data for the genomic alteration analysis was processed as follows: oncogenes (OGs) and tumour suppressor genes (TSGs) in the samples were annotated using information from multiple sources. These include the Sanger Consensus Cancer Gene Database (699 OGs and TSGs), the UniProt Knowledgebase (304 OGs and 741 TSGs), the TSGene database (1,219 TSGs) and the ONGene database (725 OGs) [ 34 , 86 – 88 ] [ 32 , 83 – 85 ]. Collation of data from these sources yielded a list of 3,688 OGs and TSGs, representing 2,773 unique genes (969 OGs and 1,804 TSGs).…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, plots of alteration patterns in genes among tumours were generated using the R package complex heatmaps [ 40 , 87 ]. Mapping of alterations onto genes in pathways shown in Supplementary Figure 8A–8C were done using the software PathwayMapper [ 88 ].…”

Section: Methodsmentioning

confidence: 99%

Integrative landscape of dysregulated signaling pathways of clinically distinct pancreatic cancer subtypes

2018

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Despite modern therapeutic advances, the survival prospects of pancreatic cancer patients have remained poor. Besides being highly metastatic, pancreatic cancer is challenging to treat because it is caused by a heterogeneous array of somatic mutations that impact a variety of signaling pathways and cellular regulatory systems. Here we use publicly available transcriptomic, copy number alteration and mutation profiling datasets from pancreatic cancer patients together with data on disease outcomes to show that the three major pancreatic cancer subtypes each display distinctive aberrations in cell signaling and metabolic pathways. Importantly, patients afflicted with these different pancreatic cancer subtypes also exhibit distinctive survival profiles. Within these patients, we find that dysregulation of the phosphoinositide 3-kinase and mitogen-activated protein kinase pathways, and p53 mediated disruptions of cell cycle processes are apparently drivers of disease. Further, we identify for the first time the molecular perturbations of signalling networks that are likely the primary causes of oncogenesis in each of the three pancreatic cancer subtypes.

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“…Also, we plotted the spectrum of genomic alterations for the fourteen most altered genes in the samples using a custom function (Figure 4a). To assess which signalling pathways, we used the PathwayMapper software [60].…”

Section: Analysis and Mutations And Copy Number Alterationsmentioning

confidence: 99%

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Kafita

Nkhoma

Zulu

et al. 2020

Preprint

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Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes.We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

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PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data

Cited by 50 publications

References 16 publications

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Integrative landscape of dysregulated signaling pathways of clinically distinct pancreatic cancer subtypes

Proteogenomic Analysis of Pancreatic Cancer Subtypes

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