The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies
J. E. L. Diaz, M. E. Ahsen, T. Schaffter, X. Chen, R. B. Realubit, C. Karan, A. Califano, B. Losic, G. Stolovitzky
Abstract
Our ability to predict the effects of drug combinations is limited, in part by limited understanding of how the transcriptional response of two monotherapies results in that of their combination. We performed the first analysis of matched time course RNAseq profiling of cells treated with both single drugs and their combinations. The transcriptional signature of the synergistic combination we studied had unique gene expression not seen in either constituent monotherapy. This can be explained mechanistically by the sequential activation of transcription factors in time in the gene regulatory network. 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 0.84 in the prediction of synergistic drug combinations in an independent dataset.
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations
Jing Hu, Jie Gao, … Guodong Zhao·Briefings in BioinformaticsPeer-reviewed·Aug 1, 2022
- Multi-task learning predicts drug combination synergy in cells and in the clinic
C. M. Gilvary, J. R. Dry, O. Elemento·bioRxiv·Mar 13, 2019
- MARSY: A multitask deep learning framework for prediction of drug combination synergy scores
Mohamed Reda El Khili, Safyan Aman Memon, Amin Emad·BioinformaticsPeer-reviewed·Apr 1, 2023
- TranSynergy: Mechanism-Driven Interpretable Deep Neural Network for the Synergistic Prediction and Pathway Deconvolution of Drug Combinations
Qiao Liu, Lei Xie·PLoS Computational BiologyPeer-reviewed·Feb 12, 2021
- Encircling the regions of the pharmacogenomic landscape that determine drug response
Adrià Fernández‐Torras, Miquel Duran‐Frigola, Patrick Aloy·Genome MedicinePeer-reviewed·Mar 26, 2019
- Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures
Daniel Osorio, Parastoo Shahrouzi, … Marieke L. Kuijjer·bioRxiv·Mar 31, 2022