CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer

Lever, Jake; Zhao, Eric Y.; Grewal, Jasleen K.; Jones, Steven J.M.; Jones, Steven J.M.

doi:10.1038/s41592-019-0422-y

Cited by 159 publications

(147 citation statements)

References 22 publications

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“…We employed the COSMIC database [75] and the text mining tool CancerMine [76] to obtain information about differentially co-expressed genes from networks (downloaded: 05-01-2019). Information from COSMIC included (i) single nucleotide polymorphisms (SNPs) within the coding region of genes, annotated as pathogenic by fathmm (Functional Analysis through Hidden Markov Models, v2.3) [77]; (ii) the COSMIC set of quantified copy number variations encompassing whole genes (loss or gain) in breast cancer; and (iii) the COSMIC census set of oncogenes, tumor suppressors genes (TSGs), and dual role genes (DRGs).…”

Section: Cosmic and Cancermine-oncogenes Tumor Suppressors Mutationmentioning

confidence: 99%

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

et al. 2020

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Background: Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis. Methods: Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

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Section: Cosmic and Cancermine-oncogenes Tumor Suppressors Mutationmentioning

confidence: 99%

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

et al. 2020

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“…However the CGC list lacks a complete annotation of cancer type information. As such, for the breast cancer evaluations, we employ the CancerMine database to construct the corresponding set [28].…”

Section: Input Datamentioning

confidence: 99%

DriveWays: A Method for Identifying Possibly Overlapping Driver Pathways in Cancer

Baali

Erten

Kazan

2020

Preprint

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Motivation: The majority of the previous methods for identifying cancer driver modules output non-overlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. Results: We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWays's output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways. Availability: The data, the source code, and useful scripts are available at: https://github.com/ abu-compbio/DriveWays Supplementary information: Supplementary data are available at Biorxiv.Recent advances in high-throughput DNA sequencing technology have allowed several projects such as The Cancer Genome Atlas (TCGA) to systematically generate genomic data for thousands of tumors across many cancer types [1].A key fundamental challenge in cancer genomics is to distinguish functional mutations that drive tumorigenesis, or drivers, from the numerous non-functional passenger mutations that occur randomly but that are not important for cancer development. Such a challenge is further complicated by the highly interactive nature of genes/proteins, thus necessitating the identification not only of such drivers but also of modules consisting of webs of drivers culpable in

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“…We compile known cancer genes from the databases Cancer Gene Census (CGC) [35] and CancerMine [36]. From the former, we obtain the list of 728 genes that are found to be associated with cancer.…”

Section: Compiling Reference Gene Setsmentioning

confidence: 99%

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

Erten

Houdjedj

Kazan

2020

Preprint

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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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CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer

Cited by 159 publications

References 22 publications

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

DriveWays: A Method for Identifying Possibly Overlapping Driver Pathways in Cancer

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

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