Computational discovery of transcription factors associated with drug response.
This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
C
C
Hanson
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
J
J
Cairns
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
L
L
Wang
Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
S
S
Sinha
D000328:Adult; D018943:Anthracyclines / Q000009:adverse effects / Q000627:therapeutic use*; D000903:Antibiotics, Antineoplastic / Q000009:adverse effects / Q000627:therapeutic use*; D001665:Binding Sites; D045744:Cell Line, Tumor; D002470:Cell Survival / Q000187:drug effects; D019295:Computational Biology; D030541:Databases, Genetic; D004305:Dose-Response Relationship, Drug; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D020128:Inhibitory Concentration 50; D008297:Male; D018579:Patient Selection; D010597:Pharmacogenetics / Q000379:methods*; D000071184:Pharmacogenomic Variants; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D034622:RNA Interference; D043823:Taxoids / Q000009:adverse effects / Q000627:therapeutic use*; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D014162:Transfection; D064726:Triple Negative Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism / Q000473:pathology
D018943:Anthracyclines; D000903:Antibiotics, Antineoplastic; D043823:Taxoids; D014157:Transcription Factors