Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses.
Allopurinol
/ pharmacology
Animals
Antineoplastic Combined Chemotherapy Protocols
/ pharmacology
Carcinoma, Non-Small-Cell Lung
/ drug therapy
Cell Line, Tumor
Cell Survival
/ drug effects
Drug Resistance, Neoplasm
/ drug effects
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
/ drug effects
Genomics
Humans
Janus Kinase 2
/ antagonists & inhibitors
Lung Neoplasms
/ drug therapy
Mice, Nude
Phenotype
Protein Kinase Inhibitors
/ pharmacology
Pyridines
/ pharmacology
Systems Analysis
Triazoles
/ pharmacology
Xenograft Model Antitumor Assays
CCLE
TCGA
cancer genomics
gene signature
lung cancer
precision oncology
Journal
Molecular oncology
ISSN: 1878-0261
Titre abrégé: Mol Oncol
Pays: United States
ID NLM: 101308230
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
pubmed:
23
5
2019
medline:
6
5
2020
entrez:
23
5
2019
Statut:
ppublish
Résumé
The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA-approved drug that inhibits XDH, on human non-small-cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six-gene signatures for allopurinol-sensitive and allopurinol-resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 was verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinol-sensitive NSCLC cell lines and four allopurinol-resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient-derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP-33779 treatment. Patient-derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.
Identifiants
pubmed: 31116490
doi: 10.1002/1878-0261.12521
pmc: PMC6670022
doi:
Substances chimiques
CEP 33779
0
Protein Kinase Inhibitors
0
Pyridines
0
Triazoles
0
Allopurinol
63CZ7GJN5I
Janus Kinase 2
EC 2.7.10.2
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1725-1743Subventions
Organisme : NIGMS NIH HHS
ID : T32 GM062754
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM054508
Pays : United States
Informations de copyright
© 2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.
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