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Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets.
- Source :
-
Cancer research [Cancer Res] 2021 Nov 15; Vol. 81 (22), pp. 5765-5776. Date of Electronic Publication: 2021 Sep 22. - Publication Year :
- 2021
-
Abstract
- 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.<br /> (©2021 The Authors; Published by the American Association for Cancer Research.)
- Subjects :
- Bile Duct Neoplasms drug therapy
Bile Duct Neoplasms metabolism
Bile Duct Neoplasms pathology
Biomarkers, Tumor antagonists & inhibitors
Biomarkers, Tumor metabolism
Cholangiocarcinoma drug therapy
Cholangiocarcinoma pathology
Drug Discovery
Humans
Phosphoproteins analysis
Phosphoproteins antagonists & inhibitors
Proteome analysis
Tumor Cells, Cultured
Antineoplastic Agents pharmacology
Cholangiocarcinoma metabolism
Computational Biology methods
Phosphoproteins metabolism
Protein Kinase Inhibitors pharmacology
Protein Kinases chemistry
Proteome metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 1538-7445
- Volume :
- 81
- Issue :
- 22
- Database :
- MEDLINE
- Journal :
- Cancer research
- Publication Type :
- Academic Journal
- Accession number :
- 34551960
- Full Text :
- https://doi.org/10.1158/0008-5472.CAN-21-0955