Molecular subtyping of cancer and nomination of kinase candidates for inhibition with phosphoproteomics: Reanalysis of CPTAC ovarian cancer.
Algorithms
Biomarkers, Tumor
Computational Biology
/ methods
Databases, Genetic
Enzyme Activation
Female
Humans
Ligands
Models, Biological
Molecular Targeted Therapy
Ovarian Neoplasms
/ diagnosis
Phosphoproteins
/ metabolism
Prognosis
Protein Binding
Protein Kinase Inhibitors
/ pharmacology
Protein Kinases
/ metabolism
Proteomics
/ methods
Signal Transduction
/ drug effects
Substrate Specificity
Survival Analysis
Druggable kinase
Ovarian cancer
Phosphoproteomics
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Feb 2019
Feb 2019
Historique:
received:
02
09
2018
revised:
18
12
2018
accepted:
18
12
2018
pubmed:
31
12
2018
medline:
27
6
2019
entrez:
31
12
2018
Statut:
ppublish
Résumé
Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates. In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a "patient-specific" hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity. Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer.
Sections du résumé
BACKGROUND
BACKGROUND
Molecular subtyping of cancer aimed to predict patient overall survival (OS) and nominate drug targets for patient treatments is central to precision oncology. Owing to the rapid development of phosphoproteomics, we can now measure thousands of phosphoproteins in human cancer tissues. However, limited studies report how to analyse the complex phosphoproteomic data for cancer subtyping and to nominate druggable kinase candidates.
FINDINGS
RESULTS
In this work, we reanalysed the phosphoproteomic data of high-grade serous ovarian cancer (HGSOC) from the Clinical Proteomic Tumour Analysis Consortium (CPTAC). Our analysis classified HGSOC into 5 major subtypes that were associated with different OS and appeared to be more accurate than that achieved with protein profiling. We provided a workflow to identify 29 kinases whose increased activities in tumours are associated with poor survival. The altered kinase signalling landscape of HGSOC included the PI3K/AKT/mTOR, cell cycle and MAP kinase signalling pathways. We also developed a "patient-specific" hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity.
INTERPRETATION
CONCLUSIONS
Our study offered a global phosphoproteomics data analysis workflow to aid in cancer molecular subtyping, determining phosphorylation-based cancer hallmarks and facilitating nomination of kinase inhibition in cancer.
Identifiants
pubmed: 30594550
pii: S2352-3964(18)30616-9
doi: 10.1016/j.ebiom.2018.12.039
pmc: PMC6412074
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
Ligands
0
Phosphoproteins
0
Protein Kinase Inhibitors
0
Protein Kinases
EC 2.7.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
305-317Informations de copyright
Copyright © 2018. Published by Elsevier B.V.
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