Mammal

Idiopathic primary myelofibrosis is an age-related clonal neoplastic disorder of
haematopoiesis characterised by a myeloproliferation and myelofibrosis. Recent
evidence suggests that disease onset results from an altered bone marrow
microenvironment, leading to disrupted crosstalk between progenitor haematopoietic
and mesenchymal stem cells populations. 90% of myelofibrosis cases exhibit ectopic
mutations of JAK2, CALR and, or MPL genes which all converge on the activation of
JAK and STAT signaling pathways. Treatments aiming to target STAT overactivity
have been developed; however, disease management is conducted at advanced
stages of the disease and treatments are not effective. A computational description
of how altered microenvironments can lead to dysregulated crosstalk between
haematopoietic and mesenchymal stem cells populations following STAT activation
would increase our knowledge of disease pathology and influence future treatment
protocols. To meet this aim, we have constructed a logical model that accounts for
the myelofibrotic microenvironment following TPO and lTLR signalling, integrated
with JAK-STAT signalling. The model primarily aims to provide a mechanistic
understanding of the dysregulated crosstalk between progenitor HSC’s, MSC’s and
the microenvironment to predict the onset of PMF with, and without the JAK
activation. Wildtype simulations result in 4 cyclic attractors being obtained, all
depending on combination of inputs being modelled. The model predicted that
presence of TPO and lTLR signalling are both required to facilitate disease onset for
wildtype simulations. For simulations involving JAK knock-in mutated scenarios, the
model resulted in 4 fixed point attractors, with the presence of lTLR alone being
sufficient to drive disease progression.

This logical model accounts for the differentiation of monocytes into monocyte-derived dendritic cells (moDCs) and macrophages. It recapitulates the main established facts regarding wild-type differentiation of monocytes and macrophages in the presence of CSF2 and/or IL4, as well as the impact of various documented mutations. This model integrates documented regulatory interactions, together with novel interactions predicted from public transcriptomic and epigenomic data, enabling to validate in silico various novel transcriptional regulatory links presumably involved in this differentiation process.

After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.

This Boolean model covers the major cell types that intervene in immunogenic cell death (ICD), namely cancer cells, DCs, CD8+ and CD4+ T cells. This model integrates intracellular mechanisms within each individual cell entity, and further incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell populations.

Model dynamics has been simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type.

With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response.

All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and calculations of parameter sensitivity analyses.

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