Drug sensitivity prediction with high-dimensional mixture regression

Abstract

This paper proposes a mixture regression model-based method for drug sensitivity prediction. The proposed method explicitly addresses two fundamental issues in drug sensitivity prediction, namely, population heterogeneity and feature selection pertaining to each of the subpopulations. The mixture regression model is estimated using the imputation-conditional consistency algorithm, and the resulting estimator is consistent. This paper also proposes an average-BIC criterion for determining the number of components for the mixture regression model. The proposed method is applied to the CCLE dataset, and the numerical results indicate that the proposed method can make a drastic improvement over the existing ones, such as random forest, support vector regression, and regularized linear regression, in both drug sensitivity prediction and feature selection. The p-values for the comparisons in drug sensitivity prediction can reach the order O(10-8) or lower for the drugs with heterogeneous populations.

Fig 1. BIC paths and cluster dendrograms produced by ICC.

The BIC paths and cluster dendrograms produced by ICC with K = 2, 3, 4, where each cluster dendrogram was produced using a hierarchical clustering procedure (with the average link) based on the dissimilarity matrix calculated along the corresponding BIC path after discarding the first 100 iterations as the burn-in process.

Fig 2. Comparison of the single component and mixture regression models for training data fitting.

The left column is for the single regression model, the middle column is for the mixture regression model, and the right column is the cluster dendrogram produced by the mixture regression model; the top, middle and lower panels are for the drugs AZD0530, L-685458 and Lapatinib, respectively.

Fig 3. Comparison of the single component and mixture regression models for test data prediction.

The left column is for the single component regression model, and the right column is for the mixture regression model; the top, middle and lower panels are for the drugs AZD0530, L-685458 and Lapatinib, respectively.

Fig 4. Prediction performance comparison.

Comparison of the prediction performance (measured by corr(Y_test, ${\widehat{Y}}_{\text{test}}$)) of the mixture regression model with support vector regression, random forest, ridge regression, elastic net, and SIS-MCP regression (i.e., single model regression).