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

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

Research output: Contribution to journalArticlepeer-review


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 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.
Original languageUndefined/Unknown
Pages (from-to)e8250
JournalMolecular Systems Biology
Publication statusPublished - Apr 1 2019


  • cancer
  • computational tool
  • drug selection
  • kinase–substrate phosphorylation network
  • single‐sample analysis

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