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Sinha, S; Thomas, D; Chan, S; Gao, Y; Brunen, D; Torabi, D; Reinisch, A; Hernandez, D; Chan, A; Rankin, EB; Bernards, R; Majeti, R; Dill, DL.
Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data.
Nat Commun. 2017; 8(2):15580-15580
Doi: 10.1038/ncomms15580
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- Co-Autor*innen der Med Uni Graz
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Reinisch Andreas
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- Abstract:
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Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. SL partners of cancer mutations are of great interest as pharmacological targets; however, identifying them by cell line-based methods is challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data to identify mutation-specific SL partners for specific cancers. We apply MiSL to 12 different cancers and predict 145,891 SL partners for 3,120 mutations, including known mutation-specific SL partners. Comparisons with functional screens show that MiSL predictions are enriched for SLs in multiple cancers. We extensively validate a SL interaction identified by MiSL between the IDH1 mutation and ACACA in leukaemia using gene targeting and patient-derived xenografts. Furthermore, we apply MiSL to pinpoint genetic biomarkers for drug sensitivity. These results demonstrate that MiSL can accelerate precision oncology by identifying mutation-specific targets and biomarkers.
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Algorithms -
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Animals -
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Cell Line, Tumor -
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Computational Biology - methods
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Female -
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Humans -
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Leukemia, Myeloid, Acute - genetics
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MCF-7 Cells -
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Male -
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Mice -
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Neoplasm Transplantation -
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Precision Medicine - methods
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RNA Interference -
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RNA, Small Interfering - genetics
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Synthetic Lethal Mutations - genetics
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Transplantation, Heterologous -