Characterization of Bypassing Kinases Inferred By Phosphoproteomics in a Midostaurin Resistant Myeloid Cell Line Model

Background: Chronic myeloid neoplasms are heterogeneous malignancies caused by sequential accumulation of genetic lesions in hematopoietic stem cells with a tendency to evolve towards acute myeloid leukemia. Genomic approaches can stratify patients according to their mutational landscape but are lim...

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Published in:Blood 2019-11, Vol.134 (Supplement_1), p.2079-2079
Main Authors: Hallal, Mahmoud, Braga-Lagache, Sophie, Jankovic, Jovana, Bruggmann, Rémy, Allam, Ramanjaneyulu, Tschan, Mario P., Simillion, Cédric, Heller, Manfred, Bonadies, Nicolas
Format: Article
Language:English
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Summary:Background: Chronic myeloid neoplasms are heterogeneous malignancies caused by sequential accumulation of genetic lesions in hematopoietic stem cells with a tendency to evolve towards acute myeloid leukemia. Genomic approaches can stratify patients according to their mutational landscape but are limited in predicting response to therapeutic agents. Reliable biomarkers to identify patients' chances for response to targeting compounds remain a crucial necessity in the current post-genomic era of precision medicine. We hypothesize that differential phosphoproteomic (PP) profiles might represent a suitable functional biological layer that allows to identify relevant determinants of oncogenic phenotypes. Aim: The aim of the project was to build a bioinformatics pipeline using PP data to i) identify differentially phosphorylated sites, ii) infer targetable kinases and iii) characterize involved oncogenic pathways. Here, we present results from further exploratory experiments of a previously established PP analysis pipeline that enables to infer sensitive/bypassing kinase activities in a Midostaurin/PKC412 (MIDO) resistant myeloid cell line model. Methods: For the validation of our analysis pipeline, we have previously used the human myeloid cell lines K562 and MOLM13, driven by the oncogenic BCR-ABL1 and FLT3 kinases, and exposed to the kinase inhibitors Nilotinib (NILO) and MIDO, respectively. For the current biological study, we applied our pipeline to explore MIDO resistance mechanisms in published MIDO-resistant (rMOLM13) and sensitive (sMOLM13) cell lines (kindly provided by E. Weisberg, Dana Farber Cancer Institute, USA). r/sMOLM13 were cultured in triplicates for 24hrs and subsequently exposed for 1h to 25nM MIDO or DMSO (CTRL). PPs were enriched with titanium-dioxide and analyzed by mass spectrometry (nanoLC-MS2). Our previously validated Kinase Activity Enrichment Analysis (KAEA) bioinformatics pipeline was further applied to infer kinase activities based on the SetRank enrichment algorithm. KAEA integrates substrate-kinase datasets from five experimentally validated databases complemented with NetworKIN in-silico predictions. The pipeline is supported by a Shiny web-app interface to allow interactive visualization and interrogation of the data. Results: In our previous validation experiments, K562 treated with NILO showed expected inhibition of ABL1 and KIT, whereas MOLM13 treated with MIDO showed expected inhibition of PRKC and downstream kinases of F
ISSN:0006-4971
1528-0020
DOI:10.1182/blood-2019-122337