Fig 1.
Drug combination cross design.
(A) The cross design to determine the drug combination sensitivity score. Compared to the full matrix design (left panel), only a single row and a single column from the matrix that correspond to the IC50 concentrations of the two drugs are utilized for the calculation of CSS (middle panel). One drug is utilized as the background drug fixed at its IC50 concentration while the other drug becomes the foreground drug with multiple doses being titrated. The resulting two dose-response curves will be summarized as the drug combination sensitivity score (CSS), from which the S synergy score can be determined as the deviation from a reference model which predicts the expected percentage inhibition effect from monotherapy dose responses. (B) Comparing the cross design with the full matrix design in terms of data size. Less materials are needed for the cross design when the size of the full matrix is two or above.
Fig 2.
Robustness and replicability of CSS.
(A) The Pearson correlation of CSS1 and CSS2 over all the drug combinations colored according to tissue type; (B) Density plot of the CSS1 and CSS2 distributions; (C) The Pearson correlation per cell line colored according to tissue type; and (D) The Pearson correlation per drug colored according to drug target class.
Table 1.
The prediction performance for Elastic Net, Random Forests and Support Vector Machines, as compared to the upper limit when randomly selecting one technical replicate as the prediction.
RMSE: root mean square error, R2: coefficient of determination, COR: Pearson correlation, MAE: mean absolute error.
Table 2.
The prediction performances for drug-target features and chemical fingerprint features using Elastic Net.
RMSE: root mean square error, R2: coefficient of determination, COR: Pearson correlation, MAE: mean absolute error.
Fig 3.
The top important features by Elastic Net for each cell line.
Cell-line independent as well as cancer subtype-specific features can be identified by evaluating the regression coefficients of the Elastic Net model. Features such as TOP1MT, TOP2A/B has shown consistently positive coefficients as compared to features such as AKT1/2/3 which showed cancer subtype specificity in breast cancer (indicated as arrows).
Table 3.
Pearson correlations of the S synergy scores with those derived using four reference models that were calculated using the full dose-response matrices.
Fig 4.
Identification of true synergistic and true antagonistic drug combinations.
(A) The ROC curves for the S synergy scores to detect true synergistic and antagonistic drug combinations. (B) The S-S plot for all the drug combinations. The drug combinations with the 75th percentile and above for both the CSS and the S scores were highlighted in red to be considered as the prioritized hits for further experimental validation in a confirmatory screen using a full dose-response matrix design.