Integrating genomics and proteomics data to predict drug effects using binary linear programming

Abstract

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

Figure 1. The flow chart of the proposed approach to infer a cell-type specific pathway map and to identify a compound's effects.

Figure 2. Boolean network topologies of the generic and inferred cell-specific pathways for the MCF7 cell line.

(A) The MCF7 generic pathway map included some important classic pathways. The edges with green color were potential downstream pathways of some compounds. (B) After optimization via BLP, the red nodes and grey dash lines were removed from generic pathway map so that the cell-specific pathways were obtained.

Figure 3. Four types of linking patterns in the pathway topological structures.

Most of the actual pathways can be represented as Boolean networks using these linking patterns. (A) “and” gate for single activation; (B) “or” gate for multiple activations; (C) “or” gate for multiple inhibitions; (D) “or” gate for mixed reactions.

Figure 4. Change of states of some important proteins during two time points.

In this figure, the binary value “1” and “0” are represented as high level and low level signal, respectively. Sat means the moment that the signaling pathways come to saturation condition in the early stage of signal transduction after treatment with compound. In the BLP approach, Sat and 6 h are the time and , respectively.

Figure 5. The BLP approach revealed the compound-induced topological alterations in the MCF7-specific pathways.

The treatment effects of four compounds on MCF7 cell line were shown in the Figure. Red arrows denote these reactions were blocked after treatment with compounds.