Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States; Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, United States; IBM Computational Biology Center, IBM Research, Yorktown Heights, United States
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States; IBM Computational Biology Center, IBM Research, Yorktown Heights, United States; Department of Business Administration, University of Illinois at Urbana-Champaign, Champaign, United States
Thomas Schaffter
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States; IBM Computational Biology Center, IBM Research, Yorktown Heights, United States
Xintong Chen
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States
Ronald B Realubit
Department of Systems Biology, Columbia University, New York, United States; Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia University Medical Center, New York, United States
Charles Karan
Department of Systems Biology, Columbia University, New York, United States; Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia University Medical Center, New York, United States
Andrea Califano
Department of Systems Biology, Columbia University, New York, United States; Department of Biomedical Informatics, Columbia University, New York, United States; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States; Department of Medicine, Columbia University, New York, United States
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States; Tisch Cancer Institute, Cancer Immunology, Icahn School of Medicine at Mount Sinai, New York, United States; Diabetes, Obesity and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, United States; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, United States
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, United States; IBM Computational Biology Center, IBM Research, Yorktown Heights, United States; Department of Systems Biology, Columbia University, New York, United States; Department of Biomedical Informatics, Columbia University, New York, United States
Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.
