A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose–Response Curve

Abstract

Gene expression data before and after treatment with an individual drug and the IC20 of dose–response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.

A novel isoflavone, ME-344, targets the cytoskeleton in acute myeloid leukemia

“…Cell growth and viability was measured 72 hours after incubation by MTS assay. Synergy, additivism, and antagonism were assessed using Excess Over Bliss Additivism [21, 22]. ME-344 showed strong synergy with vinblastine to reduce the growth and viability of TEX cells and showed moderate synergy in OCI-AML2 cells (Figure 5A–5D) (* P < 0.0001).…”

Biomolecular Network-Based Synergistic Drug Combination Discovery

“…Drug-treated cellular response data can provide an insight of drug mechanism of action [55]. Transcriptional expression profile is one of the most common cellular response data used to study the mechanism underlying a biological pathway [56] and the biology response of a cell to a certain perturbation [51, 57].…”

“…Among all the 31 groups taking part in the project, three of them performed significantly better than random guess. All of the three groups developed their models based on their assumptions about drug synergy, such as assumption that changes in gene expression after drug perturbations could be used to predicted these drug interaction effect [55] or the correlation of differential expression genes (DEGs) after two drugs perturbations would reflect the possibility of drug synergistic effect [64]. Although the final result of this project was modest, the challenge gives us reasons to hope for powerful methods to identify effective drug combinations in the future [65].…”

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

“…One of the key outcomes of the Challenge and other studies was that synergistic drug combinations could be partially predicted from the transcriptomics of the monotherapies (Niepel et al, 2017;Sun et al, 2015) . The two best-performing teams based their algorithms on the assumption that a concordance of gene expression signatures in drugs with different mechanisms of action often yield synergistic interactions (Goswami, Cheng, Alexander, Singal, & Li, 2015;Yang et al, 2015) . This assumption, while plausible, has little experimental support beyond winning the Challenge.…”