INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Autor: Robin Beekhof, Carolien van Alphen, Alex A Henneman, Jaco C Knol, Thang V Pham, Frank Rolfs, Mariette Labots, Evan Henneberry, Tessa YS Le Large, Richard R de Haas, Sander R Piersma, Valentina Vurchio, Andrea Bertotti, Livio Trusolino, Henk MW Verheul, Connie R Jimenez
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Molecular Systems Biology, Vol 15, Iss 4, Pp 1-22 (2019)
Druh dokumentu: article
ISSN: 20188250
1744-4292
DOI: 10.15252/msb.20188250
Popis: Abstract Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry‐based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label‐free kinase‐centric and substrate‐centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild‐type versus mutant, +/− drug), (iii) pre‐ and on‐treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient‐derived tumor xenografts with INKA‐guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP, and underscore target potential of high‐ranking kinases, encouraging further exploration of INKA's functional and clinical value.
Databáze: Directory of Open Access Journals
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