RT Journal Article
SR Electronic
T1 Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 396697
DO 10.1101/396697
A1 Tavassoly, Iman
A1 Hu, Yuan
A1 Zhao, Shan
A1 Mariottini, Chiara
A1 Boran, Aislyn
A1 Chen, Yibang
A1 Li, Lisa
A1 Tolentino, Rosa E.
A1 Jayaraman, Gomathi
A1 Goldfarb, Joseph
A1 Gallo, James
A1 Iyengar, Ravi
YR 2018
UL http://biorxiv.org/content/early/2018/08/21/396697.abstract
AB 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.