Dataset Information

Integrative annotation of variants from 1092 humans: application to cancer genomics.

ABSTRACT: Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

SUBMITTER: Khurana E

PROVIDER: S-EPMC3947637 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Integrative annotation of variants from 1092 humans: application to cancer genomics.

Khurana Ekta E Fu Yao Y Colonna Vincenza V Mu Xinmeng Jasmine XJ Kang Hyun Min HM Lappalainen Tuuli T Sboner Andrea A Lochovsky Lucas L Chen Jieming J Harmanci Arif A Das Jishnu J Abyzov Alexej A Balasubramanian Suganthi S Beal Kathryn K Chakravarty Dimple D Challis Daniel D Chen Yuan Y Clarke Declan D Clarke Laura L Cunningham Fiona F Evani Uday S US Flicek Paul P Fragoza Robert R Garrison Erik E Gibbs Richard R Gümüş Zeynep H ZH Herrero Javier J Kitabayashi Naoki N Kong Yong Y Lage Kasper K Liluashvili Vaja V Lipkin Steven M SM MacArthur Daniel G DG Marth Gabor G Muzny Donna D Pers Tune H TH Ritchie Graham R S GRS Rosenfeld Jeffrey A JA Sisu Cristina C Wei Xiaomu X Wilson Michael M Xue Yali Y Yu Fuli F Dermitzakis Emmanouil T ET Yu Haiyuan H Rubin Mark A MA Tyler-Smith Chris C Gerstein Mark M

Science (New York, N.Y.) 20131001 6154

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on ...[more]

PMID: 24092746

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Project description:It is understood that cancer is a clonal disease initiated by a single cell, and that metastasis, which is the spread of cancer from the primary site, is also initiated by a single cell. The seemingly natural capability of cancer to adapt dynamically in a Darwinian manner is a primary reason for therapeutic failures. Survival advantages may be induced by cancer therapies and also occur as a result of inherent cell and microenvironmental factors. The selected "more fit" clones outmatch their competition and then become dominant in the tumor via propagation of progeny. This clonal expansion leads to relapse, therapeutic resistance and eventually death. The goal of this study is to develop and demonstrate a more detailed clonality approach by utilizing integrative genomics.Patient tumor samples were profiled by Whole Exome Sequencing (WES) and RNA-seq on an Illumina HiSeq 2500 and methylation profiling was performed on the Illumina Infinium 450K array. STAR and the Haplotype Caller were used for RNA-seq processing. Custom approaches were used for the integration of the multi-omic datasets.Reported are major enhancements to CloneViz, which now provides capabilities enabling a formal tumor multi-dimensional clonality analysis by integrating: i) DNA mutations, ii) RNA expressed mutations, and iii) DNA methylation data. RNA and DNA methylation integration were not previously possible, by CloneViz (previous version) or any other clonality method to date. This new approach, named iCloneViz (integrated CloneViz) employs visualization and quantitative methods, revealing an integrative genomic mutational dissection and traceability (DNA, RNA, epigenetics) thru the different layers of molecular structures.The iCloneViz approach can be used for analysis of clonal evolution and mutational dynamics of multi-omic data sets. Revealing tumor clonal complexity in an integrative and quantitative manner facilitates improved mutational characterization, understanding, and therapeutic assignments.

Dataset Information

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Publications

Integrative annotation of variants from 1092 humans: application to cancer genomics.

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