Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets.
Antineoplastic Agents
/ pharmacology
Bile Duct Neoplasms
/ drug therapy
Biomarkers, Tumor
/ antagonists & inhibitors
Cholangiocarcinoma
/ drug therapy
Computational Biology
/ methods
Drug Discovery
Humans
Phosphoproteins
/ analysis
Protein Kinase Inhibitors
/ pharmacology
Protein Kinases
/ chemistry
Proteome
/ analysis
Tumor Cells, Cultured
Journal
Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R
Informations de publication
Date de publication:
15 11 2021
15 11 2021
Historique:
received:
26
03
2021
revised:
11
08
2021
accepted:
20
09
2021
pubmed:
24
9
2021
medline:
11
1
2022
entrez:
23
9
2021
Statut:
ppublish
Résumé
Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry-based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies.
Identifiants
pubmed: 34551960
pii: 0008-5472.CAN-21-0955
doi: 10.1158/0008-5472.CAN-21-0955
pmc: PMC9397618
doi:
Substances chimiques
Antineoplastic Agents
0
Biomarkers, Tumor
0
Phosphoproteins
0
Protein Kinase Inhibitors
0
Proteome
0
Protein Kinases
EC 2.7.-
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5765-5776Informations de copyright
©2021 The Authors; Published by the American Association for Cancer Research.
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