Abstract
Synthetic lethality (SL) has emerged as a promising approach to cancer therapy. In contrast to the costly and labour-intensive genome-wide siRNA or CRISPR-based human cell line screening approaches, computational approaches to prioritize potential synthetic lethality pairs for further experimental validation represent an attractive alternative. In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene pairs were obtained from the top 10 results covering 11 cancers. Functional analysis of these genes indicated that several promising pathways were identified, including the DNA repair related Fanconi Anemia pathway and HIF-1 signaling pathway. In addition, 4 SL pairs, mTOR-TP53, VEGFR2-TP53, EGFR-TP53, ATM-PRKCA, were validated using drug sensitivity information in the cancer cell line databases CCLE or NCI60. Interestingly, significant differences in the cell growth of mTOR siRNA or EGFR siRNA knock-down were detected between cancer cells with wild type TP53 and mutant TP53. Our study indicates that the pre-screening of potential SL gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy.
Highlights
Synthetic lethality describes the genetic interaction by which the combination of two separately non-lethal mutations results in lethality
Our study indicates that the pre-screening of potential Synthetic lethality (SL) gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy
We evaluated the ranking performance in 11 cancers through 10 times 5-fold cross validation, the other cancers in The Cancer Genome Atlas (TCGA) failed due to the limited number of overlapping samples between mutation data and the expression data or limited coverage of positive SL pairs
Summary
Synthetic lethality describes the genetic interaction by which the combination of two separately non-lethal mutations results in lethality. Given that genetic mutations underpin differences www.impactjournals.com/oncotarget between cancer cells and healthy cells, Hartwell [3] was the first to suggest the use of chemical and genetic synthetic lethality screening for cancer therapy. Since this approach has attracted great attention from cancer biologists as it provides a promising perspective for oncology medicine discovery [4, 5]. Compared to model genetic systems (such as yeast or fruit flies), human cell systems hold greater challenges for genomewide siRNA or CRISPR screening For this reason, several computational approaches have been proposed to facilitate the systematic detection of SL gene pairs in cancer. In contrast to the lack of experimental validation in most previous methods, we implemented further siRNA knockdown experiments to evaluate our results
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