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Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer

View ORCID ProfileIman Tavassoly, Yuan Hu, Shan Zhao, Chiara Mariottini, Aislyn Boran, Yibang Chen, Lisa Li, Rosa E. Tolentino, Gomathi Jayaraman, Joseph Goldfarb, James Gallo, View ORCID ProfileRavi Iyengar
doi: https://doi.org/10.1101/396697
Iman Tavassoly
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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  • ORCID record for Iman Tavassoly
Yuan Hu
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
2Clinical Pharmacology and Pharmacy, Chinese PLA General Hospital, Beijing, PRC
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Shan Zhao
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Chiara Mariottini
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Aislyn Boran
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Yibang Chen
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Lisa Li
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Rosa E. Tolentino
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Gomathi Jayaraman
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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Joseph Goldfarb
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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James Gallo
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
3Present address: Department of Pharmaceutical Sciences, Albany College of Pharmacy and Health Sciences, Albany NY
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Ravi Iyengar
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York NY 10029
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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 genomic signatures for sensitivity or resistance to specific drugs. We analyzed TCGA data for lung adenocarcinoma (LUAD) patients and identified a subset where xanthine dehydrogenase expression correlated with decreased survival. We tested allopurinol, a FDA approved drug that inhibits xanthine dehydrogenase on a library of human Non Small Cell Lung Cancer (NSCLC) cell lines from CCLE and identified sensitive and resistant cell lines. We utilized the gene expression profiles of these cell lines to identify six-gene signatures for allopurinol sensitive and resistant cell lines. Network building and analyses 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 were verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinol-sensitive NSCLC lines, and four allopurinol-resistant lines susceptible to combination therapy. We found that drug treatment of all cell lines yielded responses as predicted by the genomic signatures. We searched the library of patient derived NSCLC tumors from Jackson Laboratory to identify tumors that would be predicted to be sensitive or resistant to allopurinol treatment. Both patient derived tumors predicted to be allopurinol sensitive showed the predicted sensitivity, and the predicted resistant tumor was sensitive to combination therapy. 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.

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Posted August 21, 2018.
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Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer
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
bioRxiv 396697; doi: https://doi.org/10.1101/396697
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Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer
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
bioRxiv 396697; doi: https://doi.org/10.1101/396697

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