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Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles. Bulk and single-cell transcriptomic data can be a source of novel insights into how cells interact with each other. Here the authors develop ICELLNET, a global, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.

Dendritic cells (DC) are traditionally classified according to their ontogeny and their ability to induce T cell response to antigens, however, the phenotypic and functional state of these cells in cancer does not necessarily align to the conventional categories. Here we show, by using 16 different stimuli in vitro that activated DCs in human blood are phenotypically and functionally dichotomous, and pure cultures of type 2 conventional dendritic cells acquire these states (termed Secretory and Helper) upon appropriate stimuli. PD-L1highICOSLlow Secretory DCs produce large amounts of inflammatory cytokines and chemokines but induce very low levels of T helper (Th) cytokines following co-culturing with T cells. Conversely, PD-L1lowICOSLhigh Helper DCs produce low levels of secreted factors but induce high levels and a broad range of Th cytokines. Secretory DCs bear a single-cell transcriptomic signature indicative of mature migratory LAMP3+ DCs associated with cancer and inflammation. Secretory DCs are linked to good prognosis in head and neck squamous cell carcinoma, and to response to checkpoint blockade in Melanoma. Hence, the functional dichotomy of DCs we describe has both fundamental and translational implications in inflammation and immunotherapy. Phenotypic and functional states of dendritic cells critically influence the outcome of cancer and inflammation. Authors here show by single cell transcriptomics and in vitro validation assays that dichotomous PD-L1 and ICOSL expression assign dendritic cells to secretory and helper functions, with respective predominance of inflammatory cytokine expression or T helper cytokine induction.

The RNF43 _p.G659fs mutation occurs frequently in colorectal cancer, but its function remains poorly understood and there are no specific therapies directed against this alteration. In this study, we find that RNF43 _p.G659fs promotes cell growth independent of Wnt signaling. We perform a drug repurposing library screen and discover that cells with RNF43 _p.G659 mutations are selectively killed by inhibition of PI3K signaling. PI3K/mTOR inhibitors yield promising antitumor activity in RNF43 659mut isogenic cell lines and xenograft models, as well as in patient-derived organoids harboring RNF43 _p.G659fs mutations. We find that RNF43 659mut binds p85 leading to increased PI3K signaling through p85 ubiquitination and degradation. Additionally, RNA-sequencing of RNF43 659mut isogenic cells reveals decreased interferon response gene expression, that is reversed by PI3K/mTOR inhibition, suggesting that RNF43 659mut may alter tumor immunity. Our findings suggest a therapeutic application for PI3K/mTOR inhibitors in treating RNF43 _p.G659fs mutant cancers. The RNF43 G659fs mutation occurs frequently in colorectal cancer, but its function remains poorly understood. In this study, the authors show that RNF43 G659fs is an oncogenic colorectal cancer mutation and sensitizes tumor cells to PI3K/mTOR inhibition.

Background During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored. Methods Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines. Results When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-β1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%. Conclusions Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer.

Cells receive, and adjust to, various stimuli, which function as part of complex microenvironments forming their “context”. The possibility that a given context impacts the response to a given stimulus defines “context-dependency” and it explains large parts of the functional variability of physiopathological and pharmacological stimuli. Currently, there is no framework to analyze and quantify context-dependency over multiple contexts and cellular response outputs. We established an experimental system including a stimulus of interest, applied to an immune cell type in several contexts. We studied the function of OX40 ligand (OX40L) on T helper (Th) cell differentiation, in 4 molecular (Th0, Th1, Th2, and Th17) and 11 dendritic cell (DC) contexts (monocyte-derived DC and cDC2 conditions). We measured 17 Th output cytokines in 302 observations, and developed a statistical modeling strategy to quantify OX40L context-dependency. This revealed highly variable context-dependency, depending on the output cytokine and context type itself. Among molecular contexts, Th2 was the most influential on OX40L function. Among DC contexts, the DC type rather than the activating stimuli was dominant in controlling OX40L context-dependency. This work mathematically formalizes the complex determinants of OX40L functionality, and provides a unique framework to decipher and quantify the context-dependent variability of any biomolecule or drug function.

Interleukins (IL)-17A and F are critical cytokines in anti-microbial immunity but also contribute to auto-immune pathologies. Recent evidence suggests that they may be differentially produced by T-helper (Th) cells, but the underlying mechanisms remain unknown. To address this question, we built a regulatory graph integrating all reported upstream regulators of IL-17A and F, completed by ChIP-seq data analyses. The resulting regulatory graph encompasses 82 components and 136 regulatory links. The graph was then supplemented by logical rules calibrated with original flow cytometry data using naive CD4 + T cells, in conditions inducing IL-17A or IL-17F. The model displays specific stable states corresponding to virtual phenotypes explaining IL-17A and IL-17F differential regulation across eight cytokine stimulatory conditions. Our model analysis points to the transcription factors NFAT2A, STAT5A and SMAD2 as key regulators of the differential expression of IL-17A and IL-17F, with STAT5A controlling IL-17F expression, and an interplay of NFAT2A, STAT5A and SMAD2 controlling IL-17A expression. We experimentally observed that the production of IL-17A was correlated with an increase of SMAD2 transcription, and the expression of IL-17F correlated with an increase of BLIMP-1 transcription, together with an increase of STAT5A expression (mRNA), as predicted by our model. Interestingly, RORγt presumably plays a more determinant role in IL-17A expression as compared to IL-17F expression. In conclusion, we propose the first mechanistic model accounting for the differential expression of IL-17A and F in Th cells, providing a basis to design novel therapeutic interventions in auto-immune and inflammatory diseases.

In an inflammatory microenvironment, multiple cytokines may act on the same target cell, creating the possibility for combinatorial interactions. How these may influence the system-level function of a given cytokine is unknown. Here we show that a single cytokine, interferon (IFN)-alpha, can generate multiple transcriptional signatures, including distinct functional modules of variable flexibility, when acting in four cytokine environments driving distinct T helper cell differentiation programs (Th0, Th1, Th2 and Th17). We provide experimental validation of a chemokine, cytokine and antiviral modules differentially induced by IFN-α in Th1, Th2 and Th17 environments. Functional impact is demonstrated for the antiviral response, with a lesser IFN-α-induced protection to HIV-1 and HIV-2 infection in a Th17 context. Our results reveal that a single cytokine can induce multiple transcriptional and functional programs in different microenvironments. This combinatorial flexibility creates a previously unrecognized diversity of responses, with potential impact on disease physiopathology and cytokine therapy. Type I interferons (IFNs) have pleiotropic functions in the immune system. Here, the authors evaluate transcriptional signatures generated by type I IFN under distinct T helper cell differentiation programmes and show that, depending on the cytokine context, IFN-α differentially modulates the global cytokine profile of each T helper subset.

BackgroundAlthough NSAIDs and COX2 inhibitors show clinical promise, their toxicities limit their cancer therapeutic use. PGE2 is a metabolite from the COX2 pathway, well known to mediate immunosuppressive functions through EP2 and EP4 receptors and downstream cAMP signaling in immune cells, which suppresses their anti-tumoral activities. Similarly, PGD2, a metabolite of HPGDS, known to bind DP1, has recently been proposed to play pro-tumoral functions. OKN4395 is a novel small molecule, highly selective and potent against EP2, EP4, and DP1, intended to block the pro-tumor activities of PGE2 and PGD2, while avoiding other prostanoid receptors to reduce side effects.MethodsOKN4395’s efficacy and selectivity were rigorously characterized in a series of vitro and vivo preclinical assays. Given that the biology of DP1 is not fully understood, we confirmed using primary immune cell that its signaling was similar to the one of EP2/EP4 and further evaluated the redundancy of PGD2 with PGE2 in vitro. In addition, combination potential with anti-PD1 was evaluated.ResultsA comprehensive selectivity screen confirmed that OKN4395 selectively inhibits EP2, EP4, and DP1 receptors, with no off-target effects on other prostanoid receptors. In vitro, OKN4395 exhibited robust potency in counteracting the immunosuppressive effects of PGE2 and PGD2 in human T cells and NK cells. Our data demonstrated that PGE2 and PGD2 can fully compensate for each other’s effects; notably, the blockade of EP2, EP4, and DP1 receptors was able to effectively restore NK and CD8+ T cell anti-tumor functions when both PGE2 and PGD2 were present at high concentrations. In a mixed lymphocyte reaction assay, we showed that the well-characterized activity of anti-PD1 on IFN-γ secretion was fully blocked by prostaglandins and could only be rescued by the activity of OKN4395. Furthermore, the potent anti-tumor effect of OKN4395 in combination with anti-PD1 was demonstrated using an in vitro killing assay. In vivo, OKN4395 significantly reduced tumor growth as a monotherapy and demonstrated instances of complete tumor regression when combined with anti-PD1. Finally, pharmacokinetic and tolerability profiles from non-clinical studies supported further clinical development.ConclusionsOur studies demonstrate that triple inhibition of EP2, EP4, and DP1 with OKN4395 is more effective than dual EP2/EP4 inhibition in restoring anti-tumor immune responses in presence of both PGE2 and PGD2. These compelling results strongly endorse the clinical development of OKN4395, currently in a Phase 1 trial (NCT06789172), as a novel immunotherapy for solid tumors, both as a standalone treatment and in combination with anti-PD1.Ethics ApprovalAll human derived material used in the study were obtained under ethics and regulatory approvals.

Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles. We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets. ICELLNET also revealed autocrine IL-10 as a switch to control human dendritic cell communication with up to 12 other cell types, four of which were experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from single or multiple cell-based transcriptomic profile(s). Footnotes * https://github.com/soumelis-lab/