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• W2890301035 abstract "Cancer precision medicine largely relies on knowledge about genetic aberrations in tumors and next-generation-sequencing studies have shown a high mutational complexity in many cancers. Although a large number of the observed mutations is believed to be not causally linked with cancer, the functional effects of many rare mutations but also of combinations of driver mutations are often unknown. Here, we perform a systems analysis of a model of EGFR-mutated nonsmall cell lung cancer resistant to targeted therapy that integrates whole exome sequencing, global time-course discovery phosphoproteomics and computational modeling to identify functionally relevant molecular alterations. Our approach allows for a complexity reduction from over 2,000 genetic events potentially involved in mediating resistance to only 44 phosphoproteins and 35 topologically close genetic alterations. We perform single- and combination-drug testing against the predicted phosphoproteins and discovered that targeting of HSPB1, DBNL and AKT1 showed potent antiproliferative effects overcoming resistance against EGFR-inhibitory therapy. Our approach may therefore be used to complement mutational profiling to identify functionally relevant molecular aberrations and propose combination therapies across cancers." @default.
• W2890301035 created "2018-09-27" @default.
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• W2890301035 date "2018-11-08" @default.
• W2890301035 modified "2023-09-30" @default.
• W2890301035 title "Proteogenomic systems analysis identifies targeted therapy resistance mechanisms in EGFR‐mutated lung cancer" @default.
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• W2890301035 doi "https://doi.org/10.1002/ijc.31845" @default.
• W2890301035 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30183078" @default.
• W2890301035 hasPublicationYear "2018" @default.
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