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.
Original languageEnglish
Article numbere8250
Pages (from-to)e8250
JournalMolecular Systems Biology
Volume15
Issue number4
DOIs
Publication statusPublished - 1 Apr 2019

Cite this

@article{2358a980b83e45649b8d8a9dca3f9426,
title = "INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases",
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.",
keywords = "cancer, computational tool, drug selection, kinase–substrate phosphorylation network, single-sample analysis",
author = "Robin Beekhof and {van Alphen}, Carolien and Henneman, {Alex A.} and Knol, {Jaco C.} and Pham, {Thang V.} and Frank Rolfs and Mariette Labots and Evan Henneberry and {le Large}, {Tessa Ys} and {de Haas}, {Richard R.} and Piersma, {Sander R.} and Valentina Vurchio and Andrea Bertotti and Livio Trusolino and Verheul, {Henk Mw} and Jimenez, {Connie R.}",
year = "2019",
month = "4",
day = "1",
doi = "10.15252/msb.20188250",
language = "English",
volume = "15",
pages = "e8250",
journal = "Molecular Systems Biology",
issn = "1744-4292",
publisher = "Wiley-Blackwell",
number = "4",

}

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases. / Beekhof, Robin; van Alphen, Carolien; Henneman, Alex A.; Knol, Jaco C.; Pham, Thang V.; Rolfs, Frank; Labots, Mariette; Henneberry, Evan; le Large, Tessa Ys; de Haas, Richard R.; Piersma, Sander R.; Vurchio, Valentina; Bertotti, Andrea; Trusolino, Livio; Verheul, Henk Mw; Jimenez, Connie R.

In: Molecular Systems Biology, Vol. 15, No. 4, e8250, 01.04.2019, p. e8250.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

AU - Beekhof, Robin

AU - van Alphen, Carolien

AU - Henneman, Alex A.

AU - Knol, Jaco C.

AU - Pham, Thang V.

AU - Rolfs, Frank

AU - Labots, Mariette

AU - Henneberry, Evan

AU - le Large, Tessa Ys

AU - de Haas, Richard R.

AU - Piersma, Sander R.

AU - Vurchio, Valentina

AU - Bertotti, Andrea

AU - Trusolino, Livio

AU - Verheul, Henk Mw

AU - Jimenez, Connie R.

PY - 2019/4/1

Y1 - 2019/4/1

N2 - 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.

AB - 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.

KW - cancer

KW - computational tool

KW - drug selection

KW - kinase–substrate phosphorylation network

KW - single-sample analysis

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064831556&origin=inward

UR - https://www.ncbi.nlm.nih.gov/pubmed/30979792

U2 - 10.15252/msb.20188250

DO - 10.15252/msb.20188250

M3 - Article

VL - 15

SP - e8250

JO - Molecular Systems Biology

JF - Molecular Systems Biology

SN - 1744-4292

IS - 4

M1 - e8250

ER -