Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes

Abstract

Motivation: Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies.

Results: In this manuscript we consider the NCI-DREAM Drug Synergy Prediction Challenge dataset to identify features informative about combination effects. Through systematic exploration of differential expression profiles after single compound treatments and comparison of molecular structures of compounds, we found that synergistic levels of combinations are statistically significantly associated with compounds' dissimilarity in structure and similarity in induced gene expression changes. These two types of features offer complementary information in predicting experimentally measured combination effects of compound pairs. Our findings offer insights on the mechanisms underlying different combination effects and may help prioritize promising combinations in the very large search space.

Availability and implementation: The R code for the analysis is available on https://github.com/YiyiLiu1/DrugCombination CONTACT: hongyu.zhao@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

EOB against structural similarity of compounds. Activities of compound pairs are discretized into three states as defined in the original challenge: synergistic (circle), additive (triangle) and antagonistic (square)

Fig. 2.

(a) PC-indices of direction-based gene expression similarity scores. (b) Resampled Spearman correlations of direction-based gene expression similarity scores. When calculating the similarity scores, we considered all probes as well as probes with fold changes beyond certain ranges only. *P-value <0$.05$; **P-value <$0.01$

Fig. 3.

(a) PC-indices of GSEA-based gene expression similarity scores. (b) Resampled Spearman correlations of GSEA-based gene expression similarity scores. When calculating the similarity scores, we considered different numbers of top ($\mathrm{p}$) and bottom ($\mathrm{q}$) signatures; we included the results for a merged data set and six unmerged data sets separately. *P-value <$0.05$; **P-value <$0.01$

Fig. 4.

(a) PC-indices of Pearson correlation-based gene expression similarity scores. (b) Resampled Spearman correlations of Pearson correlation-based gene expression similarity scores. When calculating the similarity scores, we considered all probes as well as probes with largest expression changes only. *P-value <$0.05$; **P-value <0.01

Fig. 5.

(a) PC-indices of Spearman correlation-based gene expression similarity scores. (b) Resampled Spearman correlations of Spearman correlation-based gene expression similarity scores. When calculating the similarity scores, we considered all probes as well as probes with largest expression changes only. *P-value <$0.05$; **P-value <$0.01$