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

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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

Title: Global proteomics profiling improves drug sensitivity prediction : results from a multi-omics, pan-cancer modeling approach
Author: Ali, Mehreen; Khan, Suleiman A.; Wennerberg, Krister; Aittokallio, Tero
Other contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Tero Aittokallio / Principal Investigator




Date: 2018-04-15
Language: eng
Number of pages: 10
Belongs to series: Bioinformatics
ISSN: 1367-4803
DOI: https://doi.org/10.1093/bioinformatics/btx766
URI: http://hdl.handle.net/10138/235251
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.
Subject: BREAST-CANCER
MYELOID-LEUKEMIA
LUNG-CANCER
LARGE-SCALE
CHEMOTHERAPY
INHIBITION
ALGORITHMS
IMPUTATION
DRAFT
3111 Biomedicine
1182 Biochemistry, cell and molecular biology
1183 Plant biology, microbiology, virology
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