Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses. Issue 8 (10th July 2019)

• Record Type: Journal Article
• Authors: Tavassoly, Iman; Hu, Yuan; Zhao, Shan; Mariottini, Chiara; Boran, Aislyn; Chen, Yibang; Li, Lisa; Tolentino, Rosa E.; Jayaraman, Gomathi; Goldfarb, Joseph; Gallo, James; Iyengar, Ravi
• Is Part Of: Molecular oncology. Volume 13:Issue 8(2019)
• Page Start: 1725
• Page End: 1743
• Publication Date: 2019-07-10
• Subjects: cancer genomics -- CCLE -- gene signature -- lung cancer -- precision oncology -- TCGA; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1002/1878-0261.12521
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : 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. Abstract : Integrative analysis of The Cancer Genome Atlas data revealed that allopurinol is an effective drug for the treatment of non‐small‐cell lung cancer. In vitro and in vivo experiments, followed by analysis of Cancer Cell Line Encyclopedia data and network analysis, enabled identification of the genomic signatures of responsiveness to allopurinol and combination therapy with allopurinol and a JAK2 inhibitor. This framework enables precision oncology for lung cancer.