Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Abstract

Background: Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.

Methods: Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a "healthy-like" status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.

Results: Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.

Conclusions: Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Keywords: Combination therapy; Immunotherapy; Kinases; Logic modeling; Multiple sclerosis; Network modeling; Pathways; Personalized medicine; Phosphoproteomics; Signaling networks; Treatment; xMAP assay.

Fig. 1

Topological modeling approach of signaling pathways for prediction of combination therapy. a Identification of subgroup networks. A model characterizing signaling activity (upstream kinase (circle) that regulates a response, e.g., innate immunity, survival (diamond)) in response to a stimulus (oval) was calculated for each donor based on the experimentally acquired dataset. Next, the donor-specific models are merged for all donors belonging to the same subgroup (left panel blue: healthy controls; middle panel orange: untreated MS; right panel green: treated MS). b Scoring subgroup interactions to find co-druggable network interactions. The score is calculated to identify interactions that differ from healthy-like signaling activity in spite of drug treatment (see the “Methods” section). c Topological prediction of drug combination. A topology-based graph search allows identifying secondary treatments that could target and revert signaling of co-druggable interactions to a healthy-like activity state

Fig. 2

Phosphoproteomic measurement and normalization pipeline. a xMAP mean fluorescence intensity (MFI) log values of the 17 analyzed phosphoproteins, b fold change distribution, c non-linearly normalized values (see the “Methods” section). Orange measurements a–c: values of the same patient to allow visualization of the changes across data transformation. d Percentage of patients, for which each phosphoprotein was classified as phosphorylated, dephosphorylated, or non-significant after statistical testing

Fig. 3

Logic modeling identifies donor-specific signaling networks and reveals MS-specific signaling pathways. a Signaling network found by modeling for each donor, visualized as a heatmap. Rows: Single donor network. Columns: Signaling activity determined for each interaction by calibrating the PKN shown in Additional file 3: Figure S4 after removing the unidentifiable interactions using the phosphoproteomics dataset of each donor. b After networks were merged by subgroup, the Jaccard distance was used to assess similarity from all donors within each group (selected donors in group legend) to their mean subgroup network (network in X axis) and compare it to the similarity from MS patients to the same group network. Healthy donors (blue) were more similar to the mean healthy network than untreated MS patients (orange). In turn, the distance from both groups of donors to that of the combined signaling activity in all donors (grey) was statistically significant. Distance from treated donors (green) to their mean subgroup network was largely reduced when compared to distance from untreated donors to the treatment’s network, suggesting a strong effect of treatment homogenizing within group signaling. c Differentially activated pathways (see Additional file 1: Supplementary methods) between healthy controls (HC) and untreated MS patients (MS). The models previously calculated for each donor were merged to reveal the common active pathways for controls (blue), untreated MS patients (orange), and both (brown). Gray: Inactive interactions from the MS, immune- and treatment-related network (Additional file 3: Figure S4)

Fig. 4

Combination therapies predicted and in vivo validation. a All predicted co-druggable interactions of the MS drugs models. Based on the subgroup models, the co-druggability of all 168 network interactions (X axis) was assessed using the co-druggability score, and those identified as co-druggable (see Fig. 1, Table 1 and main text) are shown. For each interaction (X-axis) the number of drugs (Y-axis) is shown, in which it was found to be co-druggable using the co-druggability criteria. b FTY network co-druggability: the case of FTY network co-druggability is shown as an example (red line: interactions predicted to be co-druggable). c In vivo validation of the combination FTY+TAK1-inhibitor in the EAE model. The graph shows the mean and the standard error of the clinical score for each group (n=7). Animals started treatment after disease onset (clinical score >1.0) and were randomized to each treatment and rated in a blinded manner. Stars show days significantly different from the same day with placebo