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Human

T cells response to CTLA4 and PD-1 checkpoint inhibitors

Summary: 

This comprehensive model integrates the available data on T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. It encompasses 216 components and 451 regulatory arcs.

To ease the verification of the behaviour of this large logical model, we have designed a modular approach based on a unit testing framework used in software development. Furthermore, to compare the respective impact of the activation of the two checkpoints, we have designed a value propagation technique enabling the analytical computation of all the nodes frozen following the persisting activation or inhibition of any model component. The model verification approach greatly eased the delineation of logical rules complying with predefined dynamical specifications, while the use of the value propagation technique provided interesting insights into the differential potential of CTLA4 and PD-1 immunotherapies.

All our analyses have been implemented into two python notebooks, enabling their reproduction or extension with the most recent version of the CoLoMoTo Docker image (http://www.colomoto.org/notebook).

Preview the unit testing notebook

Preview the value propagation analysis notebook

Curation
Submitter: 
Aurelien Naldi

Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation

Summary: 

The low response to infection in neonatal T cells contributes to a high incidence of morbidity and mortality. Here we evaluated the effect of the cytoplasmic and mitochondrial levels of Reactive Oxygen Species (ROS) of neonatal CD8+T cells on their low activation. This model captures the interplay between antigen recognition with ROS and metabolic status in T cell responses. This model displays alternative stable states, which corresponds to different cell fates, i.e. quiescent, activated and anergic, depending on ROS status.

The associated notebook can be loaded using the CoLoMoTo notebook docker image (see http://www.colomoto.org/notebook).

Curation
Submitter: 
Aurelien Naldi

TCR and TLR5 merged Boolean model

Summary: 

CD4+ T cells recognize antigens through their T cell receptors TCR). However, additional signals involving co-stimulatory receptors, for example CD28, are required for proper T cell activation. Alternative co-stimulatory receptors have been proposed, including members of the Toll-like receptor family, such as TLR5 and TLR2.

We report here three Boolean models for:
- the T cell receptor (TCR) signalling pathway;
- the Toll-like receptor (TLR5) signalling pathway;
- the combination of TCR and TLR5 pathway, taking into accounting cross-interactions.

These models were validated by analysing the responses of T cells to the activation of these pathways alone or in combination, in terms of CREB, c-Jun and p65 activation.
The resulting merged model accurately reproduces the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28, regarding AP-1, CREB and NF-кB activation, thereby, providing insights regarding the cross-regulation of these pathways in CD4+ T cells.

Curation
Submitter: 
Pedro Monteiro

Molecular Pathways Enabling Tumour Cell Invasion and Migration

Summary: 

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.

Curation
Submitter: 
L. Calzone / C. Chaouiya

Mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis

Summary: 

Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.

Curation
Submitter: 
Claudine Chaouiya

ERBB receptor-regulated G1/S transition

Summary: 

Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance

Background

In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.

Results

First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.

Conclusion

In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.

Curation
Submitter: 
Claudine Chaouiya
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