Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia

Abstract

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.

Fig. 1.

Results of gene selection. Representation of selected genes for a representative patient. Graphs A–D successively represent genes that have consistent up-regulation at a given time, noted in bold (t₁–t₄, respectively). Graph E shows genes that are highly expressed through all four time-points. Graph F shows all of the retained genes.

Fig. 2.

Visualization of inferred networks. The gene regulatory network of the most-aggressive leukemic B cells (A), the indolent leukemic B cells (B), and healthy B cells (C) are represented. Nodes represent genes, and edges statistical relationships between genes. For each network, hubs are highlighted in color. As the number of hubs decreases between aggressive, indolent, and healthy networks, the structure of the network is changed. Subnetworks for DUSP1 (D) and EGR1 (E) in the most-aggressive leukemic B-cell networks. The concerned gene is highlighted in red. Direct links are shown in navy blue, and indirect links are shown in pale blue. EGR1 is a gene whose influence is very large, because its subnetwork takes a large part of the complete network. In contrast, DUSP1 has a limited subnetwork. Visualization generated using R and R package igraph.