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Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer

Overview of attention for article published in Biology Direct, October 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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Title
Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer
Published in
Biology Direct, October 2015
DOI 10.1186/s13062-015-0086-1
Pubmed ID
Authors

Sriganesh Srihari, Jitin Singla, Limsoon Wong, Mark A. Ragan

Abstract

Synthetic lethality (SL) refers to the genetic interaction between two or more genes where only their co-alteration (e.g. by mutations, amplifications or deletions) results in cell death. In recent years, SL has emerged as an attractive therapeutic strategy against cancer: by targeting the SL partners of altered genes in cancer cells, these cells can be selectively killed while sparing the normal cells. Consequently, a number of studies have attempted prediction of SL interactions in human, a majority by extrapolating SL interactions inferred through large-scale screens in model organisms. However, these predicted SL interactions either do not hold in human cells or do not include genes that are (frequently) altered in human cancers, and are therefore not attractive in the context of cancer therapy. Here, we develop a computational approach to infer SL interactions directly from frequently altered genes in human cancers. It is based on the observation that pairs of genes that are altered in a (significantly) mutually exclusive manner in cancers are likely to constitute lethal combinations. Using genomic copy-number and gene-expression data from four cancers, breast, prostate, ovarian and uterine (total 3980 samples) from The Cancer Genome Atlas, we identify 718 genes that are frequently amplified or upregulated, and are likely to be synthetic lethal with six key DNA-damage response (DDR) genes in these cancers. By comparing with published data on gene essentiality (~16000 genes) from ten DDR-deficient cancer cell lines, we show that our identified genes are enriched among the top quartile of essential genes in these cell lines, implying that our inferred genes are highly likely to be (synthetic) lethal upon knockdown in these cell lines. Among the inferred targets are tousled-like kinase 2 (TLK2) and the deubiquitinating enzyme ubiquitin-specific-processing protease 7 (USP7) whose overexpression correlates with poor survival in cancers. Mutual exclusivity between frequently occurring genetic events identifies synthetic lethal combinations in cancers. These identified genes are essential in cell lines, and are potential candidates for targeted cancer therapy.    http://bioinformatics.org.au/tools-data/underMutExSL REVIEWERS: This article was reviewed by Dr Michael Galperin, Dr Sebastian Maurer-Stroh and Professor Sanghyuk Lee.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 1%
Germany 1 1%
Unknown 93 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 21%
Student > Ph. D. Student 18 19%
Student > Bachelor 13 14%
Student > Master 11 12%
Student > Postgraduate 8 8%
Other 12 13%
Unknown 13 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 35%
Agricultural and Biological Sciences 21 22%
Medicine and Dentistry 10 11%
Computer Science 7 7%
Engineering 4 4%
Other 4 4%
Unknown 16 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2019.
All research outputs
#4,700,806
of 25,200,621 outputs
Outputs from Biology Direct
#174
of 533 outputs
Outputs of similar age
#56,783
of 281,123 outputs
Outputs of similar age from Biology Direct
#8
of 20 outputs
Altmetric has tracked 25,200,621 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 533 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 281,123 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.