scholarly journals Web-based platform to identify cancer driver mutations across tumor types based on new and existing sequencing data

2013 ◽  
Vol 6 (39) ◽  
pp. 1107-1107
2018 ◽  
Author(s):  
Matthew A. Reyna ◽  
David Haan ◽  
Marta Paczkowska ◽  
Lieven P.C. Verbeke ◽  
Miguel Vazquez ◽  
...  

AbstractThe catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notablyTERTpromoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression inTP53, TLE4, andTCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.


2020 ◽  
Vol 48 (W1) ◽  
pp. W185-W192 ◽  
Author(s):  
Jorge Oscanoa ◽  
Lavanya Sivapalan ◽  
Emanuela Gadaleta ◽  
Abu Z Dayem Ullah ◽  
Nicholas R Lemoine ◽  
...  

Abstract SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.


2014 ◽  
Author(s):  
Tenghui Chen ◽  
Hao Zhao ◽  
Yong Mao ◽  
Yuan Qi ◽  
Agda Karina Eterovic ◽  
...  

2020 ◽  
Author(s):  
Ferran Muiños ◽  
Francisco Martinez-Jimenez ◽  
Oriol Pich ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas

SummaryExtensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.


F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1028
Author(s):  
James A. Eddy ◽  
Vésteinn Thorsson ◽  
Andrew E. Lamb ◽  
David L. Gibbs ◽  
Carolina Heimann ◽  
...  

The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.


2015 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Jessica Xin Hu ◽  
Elizabeth Worstell ◽  
Nina Ilic ◽  
...  

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.


2020 ◽  
Vol 19 ◽  
pp. 117693512097237
Author(s):  
Brian O’Sullivan ◽  
Cathal Seoighe

Motivation: Somatic mutations can have critical prognostic and therapeutic implications for cancer patients. Although targeted methods are often used to assay specific cancer driver mutations, high throughput sequencing is frequently applied to discover novel driver mutations and to determine the status of less-frequent driver mutations. The task of recovering somatic mutations from these data is nontrivial as somatic mutations must be distinguished from germline variants, sequencing errors, and other artefacts. Consequently, bioinformatics pipelines for recovery of somatic mutations from high throughput sequencing typically involve a large number of analytical choices in the form of quality filters. Results: We present vcfView, an interactive tool designed to support the evaluation of somatic mutation calls from cancer sequencing data. The tool takes as input a single variant call format (VCF) file and enables researchers to explore the impacts of analytical choices on the mutant allele frequency spectrum, on mutational signatures and on annotated somatic variants in genes of interest. It allows variants that have failed variant caller filters to be re-examined to improve sensitivity or guide the design of future experiments. It is extensible, allowing other algorithms to be incorporated easily. Availability: The shiny application can be downloaded from GitHub ( https://github.com/BrianOSullivanGit/vcfView ). All data processing is performed within R to ensure platform independence. The app has been tested on RStudio, version 1.1.456, with base R 3.6.2 and Shiny 1.4.0. A vignette based on a publicly available data set is also available on GitHub.


2016 ◽  
Vol 24 (5) ◽  
pp. 313-319 ◽  
Author(s):  
Matthew D. Stachler ◽  
Elizabeth M. Rinehart ◽  
Elizabeth Garcia ◽  
Neal I. Lindeman

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