Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach

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dc.contributor.author Ali, Mehreen
dc.contributor.author Khan, Suleiman A.
dc.contributor.author Wennerberg, Krister
dc.contributor.author Aittokallio, Tero
dc.date.accessioned 2018-05-21T08:07:01Z
dc.date.available 2018-05-21T08:07:01Z
dc.date.issued 2018-04-15
dc.identifier.citation Ali , M , Khan , S A , Wennerberg , K & Aittokallio , T 2018 , ' Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach ' , Bioinformatics , vol. 34 , no. 8 , pp. 1353-1362 . https://doi.org/10.1093/bioinformatics/btx766
dc.identifier.other PURE: 106996314
dc.identifier.other PURE UUID: 5583de9b-6f44-423e-b8a9-55e19e29a9f4
dc.identifier.other WOS: 000430175000012
dc.identifier.other Scopus: 85046722206
dc.identifier.other ORCID: /0000-0002-0886-9769/work/44999683
dc.identifier.uri http://hdl.handle.net/10138/235251
dc.description.abstract Motivation: Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results: Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. en
dc.format.extent 10
dc.language.iso eng
dc.relation.ispartof Bioinformatics
dc.rights cc_by_nc
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject BREAST-CANCER
dc.subject MYELOID-LEUKEMIA
dc.subject LUNG-CANCER
dc.subject LARGE-SCALE
dc.subject CHEMOTHERAPY
dc.subject INHIBITION
dc.subject ALGORITHMS
dc.subject IMPUTATION
dc.subject DRAFT
dc.subject 3111 Biomedicine
dc.subject 1182 Biochemistry, cell and molecular biology
dc.subject 1183 Plant biology, microbiology, virology
dc.title Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach en
dc.type Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization University of Helsinki
dc.contributor.organization Krister Wennerberg / Principal Investigator
dc.contributor.organization Tero Aittokallio / Principal Investigator
dc.contributor.organization Bioinformatics
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1093/bioinformatics/btx766
dc.relation.issn 1367-4803
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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