Mark Wappett, Austin Dulak, Zheng Rong Yang, Abdullatif Al-Watban, James R. Bradford, Jonathan R. Dry
Abstract
Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp.
Applying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens.
This work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html .
Pharmacology, Toxicology and Pharmaceutical Science
3
5%
Engineering
3
5%
Other
3
5%
Unknown
12
20%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 21 July 2016.
All research outputs
#6,265,736
of 23,316,003 outputs
Outputs from BMC Genomics
#2,635
of 10,742 outputs
Outputs of similar age
#100,154
of 396,857 outputs
Outputs of similar age from BMC Genomics
#62
of 271 outputs
Altmetric has tracked 23,316,003 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 10,742 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 75% of its peers.
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We're also able to compare this research output to 271 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.
