title: Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma.
abstract: Synthetic lethal interactions in cancer hold the potential for successful combined therapies, which would avoid the difficulties of single molecule-targeted treatment. Identification of interactions that are specific for human tumors is an open problem in cancer research. This work aims at deciphering synthetic sick or lethal interactions directly from somatic alteration, expression and survival data of cancer patients. To this end, we look for pairs of genes and their alterations or expression levels that are "avoided" by tumors and "beneficial" for patients. Thus, candidates for synthetic sickness or lethality (SSL) interaction are identified as such gene pairs whose combination of states is under-represented in the data. Our main methodological contribution is a quantitative score that allows ranking of the candidate SSL interactions according to evidence found in patient survival. Applying this analysis to glioblastoma data, we collect 1,956 synthetic sick or lethal partners for 85 abundantly altered genes, most of which show extensive copy number variation across the patient cohort. We rediscover and interpret known interaction between TP53 and PLK1, as well as provide insight into the mechanism behind EGFR interacting with AKT2, but not AKT1 nor AKT3. Cox model analysis determines 274 of identified interactions as having significant impact on overall survival in glioblastoma, which is more informative than a standard survival predictor based on patient's age.
authors: Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195, Berlin, Germany. ewa.szczurek@bsse.ethz.ch
authors: Ewa
authors: E
authors: Szczurek
authors: Navodit
authors: N
authors: Misra
authors: Martin
authors: M
authors: Vingron
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000494:pharmacology*; D014408:Biomarkers, Tumor / Q000037:antagonists & inhibitors / Q000235:genetics*; D018797:Cell Cycle Proteins / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology; D056915:DNA Copy Number Variations / Q000235:genetics*; D066246:ErbB Receptors / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D005804:Genes, Lethal; D005909:Glioblastoma / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality*; D006801:Humans; D008957:Models, Genetic; D017354:Point Mutation / Q000235:genetics*; D011379:Prognosis; D017346:Protein Serine-Threonine Kinases / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D011518:Proto-Oncogene Proteins / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D051057:Proto-Oncogene Proteins c-akt / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D015996:Survival Rate; D016159:Tumor Suppressor Protein p53 / Q000037:antagonists & inhibitors / Q000235:genetics / Q000378:metabolism; D000097869:Polo-Like Kinase 1
chemical_list: D014408:Biomarkers, Tumor; D018797:Cell Cycle Proteins; D066246:ErbB Receptors; D017346:Protein Serine-Threonine Kinases; D011518:Proto-Oncogene Proteins; D051057:Proto-Oncogene Proteins c-akt; D016159:Tumor Suppressor Protein p53; D000097869:Polo-Like Kinase 1; C495901:TP53 protein, human; C512478:EGFR protein, human; C494921:AKT2 protein, human
keywords: combination therapy; glioblastoma; synthetic sickness or lethality