Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA).

Hallal, Mahmoud Malek; Braga-Lagache, Sophie; Jankovic, Jovana; Simillion, Cedric; Bruggmann, Rémy; Uldry, Anne-Christine; Allam, Ramanjaneyulu; Heller, Manfred; Bonadies, Nicolas (2021). Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA). BMC cancer, 21(1), p. 789. BioMed Central 10.1186/s12885-021-08479-z

[img]
Preview
Text
Inference_of_kinase-signaling_networks_in_human.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

BACKGROUND

Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.

METHODS

We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm  with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4-11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.

RESULTS

Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4-11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4-11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.

CONCLUSIONS

We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR)
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Haematology and Central Haematological Laboratory
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Unit Childrens Hospital > Forschungsgruppe Hämatologie (Erwachsene)
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DCR Services > Core Facility Massenspektrometrie- und Proteomics-Labor
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Unit Childrens Hospital > Protein- und Zellbiologie

UniBE Contributor:

Hallal, Mahmoud Malek; Jankovic, Jovana; Simillion, Cedric André Marie; Bruggmann, Rémy; Uldry, Anne-Christine; Heller, Manfred and Bonadies, Nicolas

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1471-2407

Publisher:

BioMed Central

Language:

English

Submitter:

Pierrette Durand Lüthi

Date Deposited:

14 Jul 2021 15:42

Last Modified:

18 Jul 2021 03:07

Publisher DOI:

10.1186/s12885-021-08479-z

PubMed ID:

34238254

Uncontrolled Keywords:

Kinase activity Kinase-signaling network Myeloid malignancies Phosphoproteomics

BORIS DOI:

10.48350/157556

URI:

https://boris.unibe.ch/id/eprint/157556

Actions (login required)

Edit item Edit item
Provide Feedback