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 Y. S. le Large, Richard R. de Haas, Sander R. Piersma, Valentina Vurchio, Andrea Bertotti, Livio Trusolino, Henk M. W. Verheul, Connie R. Jimenez

Research output: Contribution to journalArticleAcademicpeer-review

38 Citations (Scopus)


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.
Original languageEnglish
Article numbere8250
Pages (from-to)e8250
JournalMolecular systems biology
Issue number4
Publication statusPublished - 1 Apr 2019


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

Cite this