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 reverseengineer 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.temporal gene network | lasso penalty | lymphoproliferative disorder | B-cell antigen receptor | predicted intervention C ellular behavior is conditioned mostly by functional genetic programs in response to various environmental signals, as initially shown in simple organisms (1, 2). External stimuli activate cellular surface receptors that trigger multiple signaling cascades in cells. The ultimate targets of these cascades are transcription factors that initiate sequential transcriptional activations with high temporal coordination. The first activated genes, at early time-points, after cell stimulation, essentially have a fast and transient expression; their gene products activate expression of various target genes downstream of transcriptional regulation cascades. These latter genes have longer-lasting expression, and their products sustain the adapted cellular response to initial environmental stimulation (3). These functional molecular networks are disrupted in various pathologies (e.g., cancer) where genetic aberrations lead to tumoral cellular programs. Since the first application of high-throughput technologies for measuring gene expression, a number of methods have been proposed to reverse-engineer gene regulatory networks; considered to be the underlying structure of these genetic programs (4). These different methods were developed to infer gene potential interactions and to describe these networks at the system level (5). The next important goal was to develop statistical tools to control these systems (6). One of the key challenges is to determine which critical genes whose perturbed expression drive these pathological genetic programs toward targeted states. We propose here a predictive met...

Acquired Initiating Mutations in Early Hematopoietic Cells of CLL Patients

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“…BM or spleen origin is indicated (****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, P > 0.05). we defined a set of genes upregulated upon BCR stimulation of normal B cells, using available data (29,30), and used Gene Set Enrichment Analyses (GSEA) to show that this signature is enriched in EGR2-mutated samples, with respect to nonmutated samples (Fig. 5C and D).…”

Identifying causal networks linking cancer processes and anti‐tumor immunity using Bayesian network inference and metagene constructs

“…Identifying oncogenic changes in networks can then be used to inform therapy . In cancer biology, the focus heretofore has been on identifying oncogenic changes associated with intracellular networks, which can be performed with (e.g.,) or without (e.g.,) prior information about the network. In contrast, identifying cellular changes in extracellular networks is aided by prior information about different cell subsets, as the presence or absence of a particular cell type within a heterogenous sample induces a distinct gene expression signature.…”

Systems Analysis of High-Throughput Data

“…Recently, these network inference techniques have played a role in reverse engineering the regulatory networks of healthy human B cells [127] and chronic lymphocytic leukemia cancer cells [128], providing a richer description of the systems biology of blood. Because network inference approaches do not rely on assumptions about the pathway architecture, they are exceptionally well–suited to be applied to integrated data sets (e.g., combining both mRNA and microRNA expression data) to identify complex regulatory relationships.…”