Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

Iman Tavassoly, Yuan Hu, Shan Zhao, Chiara Mariottini, Aislyn Boran, Yibang Chen, Lisa Li, Rosa E. Tolentino, Gomathi Jayaraman, Joseph Goldfarb, James Gallo, Ravi Iyengar

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.

Original languageEnglish
Pages (from-to)1725-1743
Number of pages19
JournalMolecular Oncology
Volume13
Issue number8
DOIs
StatePublished - Aug 2019

Keywords

  • CCLE
  • TCGA
  • cancer genomics
  • gene signature
  • lung cancer
  • precision oncology

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