RRC ID 68962
Author Khorsandi SE, Dokal AD, Rajeeve V, Britton DJ, Illingworth MS, Heaton N, Cutillas PR.
Title Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets.
Journal Cancer Res
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.
Volume 81(22)
Pages 5765-5776
Published 2021-11-15
DOI 10.1158/0008-5472.CAN-21-0955
PII 0008-5472.CAN-21-0955
PMID 34551960
MeSH Antineoplastic Agents / pharmacology* Bile Duct Neoplasms / drug therapy Bile Duct Neoplasms / metabolism Bile Duct Neoplasms / pathology Biomarkers, Tumor / antagonists & inhibitors Biomarkers, Tumor / metabolism Cholangiocarcinoma / drug therapy Cholangiocarcinoma / metabolism* Cholangiocarcinoma / pathology Computational Biology / methods* Drug Discovery Humans Phosphoproteins / analysis Phosphoproteins / antagonists & inhibitors Phosphoproteins / metabolism* Protein Kinase Inhibitors / pharmacology* Protein Kinases / chemistry* Proteome / analysis Proteome / metabolism* Tumor Cells, Cultured
IF 9.727
Human and Animal Cells HuH-28(RCB1943) TGBC1TKB(RCB1129) TGBC24TKB(RCB1196)