PGE2 inhibits TIL expansion by disrupting IL-2 signalling and mitochondrial function.

Expansion of antigen-experienced CD8+ T cells is critical for the success of tumour-infiltrating lymphocyte (TIL)-adoptive cell therapy (ACT) in patients with cancer1. Interleukin-2 (IL-2) acts as a key regulator of CD8+ cytotoxic T lymphocyte functions by promoting expansion and cytotoxic capability2,3. Therefore, it is essential to comprehend mechanistic barriers to IL-2 sensing in the tumour microenvironment to implement strategies to reinvigorate IL-2 responsiveness and T cell antitumour responses. Here we report that prostaglandin E2 (PGE2), a known negative regulator of immune response in the tumour microenvironment4,5, is present at high concentrations in tumour tissue from patients and leads to impaired IL-2 sensing in human CD8+ TILs via the PGE2 receptors EP2 and EP4. Mechanistically, PGE2 inhibits IL-2 sensing in TILs by downregulating the IL-2Rγc chain, resulting in defective assembly of IL-2Rß-IL2Rγc membrane dimers. This results in impaired IL-2-mTOR adaptation and PGC1α transcriptional repression, causing oxidative stress and ferroptotic cell death in tumour-reactive TILs. Inhibition of PGE2 signalling to EP2 and EP4 during TIL expansion for ACT resulted in increased IL-2 sensing, leading to enhanced proliferation of tumour-reactive TILs and enhanced tumour control once the cells were transferred in vivo. Our study reveals fundamental features that underlie impairment of human TILs mediated by PGE2 in the tumour microenvironment. These findings have therapeutic implications for cancer immunotherapy and cell therapy, and enable the development of targeted strategies to enhance IL-2 sensing and amplify the IL-2 response in TILs, thereby promoting the expansion of effector T cells with enhanced therapeutic potential.

Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma.

Adoptive cell therapy (ACT) using ex vivo-expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell-intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor-reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

Activation of the transcription factor NFAT5 in the tumor microenvironment enforces CD8+ T cell exhaustion.

Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. Here we found that NFAT5, an NFAT family transcription factor that lacks an AP-1 docking site, was highly expressed in exhausted CD8+ T cells in the context of chronic infections and tumors but was selectively required in tumor-induced CD8+ T cell exhaustion. Overexpression of NFAT5 in CD8+ T cells reduced tumor control, while deletion of NFAT5 improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that had reduced expression of the exhaustion-associated proteins TOX and PD-1 and produced more cytokines, such as IFNÉ£ and TNF, than cells with wild-type levels of NFAT5, specifically in the precursor exhausted PD-1+TCF1+TIM-3-CD8+ T cell population. NFAT5 did not promote T cell exhaustion during chronic infection with clone 13 of lymphocytic choriomeningitis virus. Expression of NFAT5 was induced by TCR triggering, but its transcriptional activity was specific to the tumor microenvironment and required hyperosmolarity. Thus, NFAT5 promoted the exhaustion of CD8+ T cells in a tumor-selective fashion.

Orthogonal cytokine engineering enables novel synthetic effector states escaping canonical exhaustion in tumor-rejecting CD8+ T cells.

To date, no immunotherapy approaches have managed to fully overcome T-cell exhaustion, which remains a mandatory fate for chronically activated effector cells and a major therapeutic challenge. Understanding how to reprogram CD8+ tumor-infiltrating lymphocytes away from exhausted effector states remains an elusive goal. Our work provides evidence that orthogonal gene engineering of T cells to secrete an interleukin (IL)-2 variant binding the IL-2Rßγ receptor and the alarmin IL-33 reprogrammed adoptively transferred T cells to acquire a novel, synthetic effector state, which deviated from canonical exhaustion and displayed superior effector functions. These cells successfully overcame homeostatic barriers in the host and led-in the absence of lymphodepletion or exogenous cytokine support-to high levels of engraftment and tumor regression. Our work unlocks a new opportunity of rationally engineering synthetic CD8+ T-cell states endowed with the ability to avoid exhaustion and control advanced solid tumors.

Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy.

Developing strategies to inflame tumors is critical for increasing response to immunotherapy. Here, we report that low-dose radiotherapy (LDRT) of murine tumors promotes T-cell infiltration and enables responsiveness to combinatorial immunotherapy in an IFN-dependent manner. Treatment efficacy relied upon mobilizing both adaptive and innate immunity and depended on both cytotoxic CD4+ and CD8+ T cells. LDRT elicited predominantly CD4+ cells with features of exhausted effector cytotoxic cells, with a subset expressing NKG2D and exhibiting proliferative capacity, as well as a unique subset of activated dendritic cells expressing the NKG2D ligand RAE1. We translated these findings to a phase I clinical trial administering LDRT, low-dose cyclophosphamide, and immune checkpoint blockade to patients with immune-desert tumors. In responsive patients, the combinatorial treatment triggered T-cell infiltration, predominantly of CD4+ cells with Th1 signatures. Our data support the rational combination of LDRT with immunotherapy for effectively treating low T cell-infiltrated tumors. SIGNIFICANCE: Low-dose radiation reprogrammed the tumor microenvironment of tumors with scarce immune infiltration and together with immunotherapy induced simultaneous mobilization of innate and adaptive immunity, predominantly CD4+ effector T cells, to achieve tumor control dependent on NKG2D. The combination induced important responses in patients with metastatic immune-cold tumors.This article is highlighted in the In This Issue feature, p. 1.

Myeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation.

The mechanisms regulating exhaustion of tumor-infiltrating lymphocytes (TIL) and responsiveness to PD-1 blockade remain partly unknown. In human ovarian cancer, we show that tumor-specific CD8+ TIL accumulate in tumor islets, where they engage antigen and upregulate PD-1, which restrains their functions. Intraepithelial PD-1+CD8+ TIL can be, however, polyfunctional. PD-1+ TIL indeed exhibit a continuum of exhaustion states, with variable levels of CD28 costimulation, which is provided by antigen-presenting cells (APC) in intraepithelial tumor myeloid niches. CD28 costimulation is associated with improved effector fitness of exhausted CD8+ TIL and is required for their activation upon PD-1 blockade, which also requires tumor myeloid APC. Exhausted TIL lacking proper CD28 costimulation in situ fail to respond to PD-1 blockade, and their response may be rescued by local CTLA-4 blockade and tumor APC stimulation via CD40L.

Predicting combinations of immunomodulators to enhance dendritic cell-based vaccination based on a hybrid experimental and computational platform.

Dendritic cell (DC)-based vaccines have been largely used in the adjuvant setting for the treatment of cancer, however, despite their proven safety, clinical outcomes still remain modest. In order to improve their efficacy, DC-based vaccines are often combined with one or multiple immunomodulatory agents. However, the selection of the most promising combinations is hampered by the plethora of agents available and the unknown interplay between these different agents. To address this point, we developed a hybrid experimental and computational platform to predict the effects and immunogenicity of dual combinations of stimuli once combined with DC vaccination, based on the experimental data of a variety of assays to monitor different aspects of the immune response after a single stimulus. To assess the stimuli behavior when used as single agents, we first developed an in vitro co-culture system of T cell priming using monocyte-derived DCs loaded with whole tumor lysate to prime autologous peripheral blood mononuclear cells in the presence of the chosen stimuli, as single adjuvants, and characterized the elicited response assessing 18 different phenotypic and functional traits important for an efficient anti-cancer response. We then developed and applied a prediction algorithm, generating a ranking for all possible dual combinations of the different single stimuli considered here. The ranking generated by the prediction tool was then validated with experimental data showing a strong correlation with the predicted scores, confirming that the top ranked conditions globally significantly outperformed the worst conditions. Thus, the method developed here constitutes an innovative tool for the selection of the best immunomodulatory agents to implement in future DC-based vaccines.

Fisetin protects against cardiac cell death through reduction of ROS production and caspases activity.

Myocardial infarction (MI) is a leading cause of death worldwide. Reperfusion is considered as an optimal therapy following cardiac ischemia. However, the promotion of a rapid elevation of O2 levels in ischemic cells produces high amounts of reactive oxygen species (ROS) leading to myocardial tissue injury. This phenomenon is called ischemia reperfusion injury (IRI). We aimed at identifying new and effective compounds to treat MI and minimize IRI. We previously studied heart regeneration following myocardial injury in zebrafish and described each step of the regeneration process, from the day of injury until complete recovery, in terms of transcriptional responses. Here, we mined the data and performed a deep in silico analysis to identify drugs highly likely to induce cardiac regeneration. Fisetin was identified as the top candidate. We validated its effects in an in vitro model of MI/IRI in mammalian cardiac cells. Fisetin enhances viability of rat cardiomyocytes following hypoxia/starvation - reoxygenation. It inhibits apoptosis, decreases ROS generation and caspase activation and protects from DNA damage. Interestingly, fisetin also activates genes involved in cell proliferation. Fisetin is thus a highly promising candidate drug with clinical potential to protect from ischemic damage following MI and to overcome IRI.

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation.

Database URL: http://biomodels.caltech.edu.

Deciphering the Dynamic Transcriptional and Post-transcriptional Networks of Macrophages in the Healthy Heart and after Myocardial Injury.

Macrophage plasticity has been studied in vitro, but transcriptional regulation upon injury is poorly understood. We generated a valuable dataset that captures transcriptional changes in the healthy heart and after myocardial injury, revealing a dynamic transcriptional landscape of macrophage activation. Partial deconvolution suggested that post-injury macrophages exhibit overlapping activation of pro-inflammatory and anti-inflammatory programs rather than aligning to canonical M1/M2 programs. Furthermore, simulated dynamics and experimental validation of a regulatory core of the underlying gene-regulatory network revealed a negative-feedback loop that limits initial inflammation via hypoxia-mediated upregulation of Il10. Our results also highlight the prominence of post-transcriptional regulation (miRNAs, mRNA decay, and lincRNAs) in attenuating the myocardial injury-induced inflammatory response. We also identified a cardiac-macrophage-specific gene signature (e.g., Egfr and Lifr) and time-specific markers for macrophage populations (e.g., Lyve1, Cd40, and Mrc1). Altogether, these data provide a core resource for deciphering the transcriptional network in cardiac macrophages in vivo.

Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics.

BACKGROUND: Smith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from haploinsufficiency of RAI1. It is characterized by distinctive facial features, brachydactyly, sleep disturbances, and stereotypic behaviors. METHODS: We investigated a cohort of 15 individuals with a clinical suspicion of SMS who showed neither deletion in the SMS critical region nor damaging variants in RAI1 using whole exome sequencing. A combination of network analysis (co-expression and biomedical text mining), transcriptomics, and circularized chromatin conformation capture (4C-seq) was applied to verify whether modified genes are part of the same disease network as known SMS-causing genes. RESULTS: Potentially deleterious variants were identified in nine of these individuals using whole-exome sequencing. Eight of these changes affect KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C, and POGZ, known to be associated with Kabuki syndrome 1, Mowat-Wilson syndrome, cardiofaciocutaneous syndrome, glycine encephalopathy, mental retardation and microcephaly with pontine and cerebellar hypoplasia, X-linked mental retardation 13, X-linked mental retardation Claes-Jensen type, and White-Sutton syndrome, respectively. The ninth individual carries a de novo variant in JAKMIP1, a regulator of neuronal translation that was recently found deleted in a patient with autism spectrum disorder. Analyses of co-expression and biomedical text mining suggest that these pathologies and SMS are part of the same disease network. Further support for this hypothesis was obtained from transcriptome profiling that showed that the expression levels of both Zeb2 and Map2k2 are perturbed in Rai1 -/- mice. As an orthogonal approach to potentially contributory disease gene variants, we used chromatin conformation capture to reveal chromatin contacts between RAI1 and the loci flanking ZEB2 and GLDC, as well as between RAI1 and human orthologs of the genes that show perturbed expression in our Rai1 -/- mouse model. CONCLUSIONS: These holistic studies of RAI1 and its interactions allow insights into SMS and other disorders associated with intellectual disability and behavioral abnormalities. Our findings support a pan-genomic approach to the molecular diagnosis of a distinctive disorder.

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

BACKGROUND: Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs. RESULTS: We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses. CONCLUSIONS: Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic PKNs. These contextualized models have improved utility for hypothesis generation and experimental design. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research. Our method was implemented in the software optimusqual, available online at http://www.vital-it.ch/software/optimusqual/ .

Analysis of the dynamic co-expression network of heart regeneration in the zebrafish.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

Social networks help to infer causality in the tumor microenvironment.

BACKGROUND: Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems--such as the tumor microenvironment--where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. RESULTS: In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. CONCLUSIONS: This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis.

Survival analyses based on the Kaplan-Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a 'bottom-up' approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a 'top-down' approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer by a non-hypothesis-driven iterative survival analysis. We show that the application of our novel method to a population of ~2,000 breast cancer patients of the METABRIC consortium allows the identification of several well-known cancer mechanisms, such as ERBB4, HNF3A and TGFB pathways, and the investigation of their paradoxical dual effect. In addition, several novel biological mechanisms are proposed as potentially involved in cancer progression. The proposed exploratory methodology can be considered both alternative and complementary to classical 'bottom-up' approaches for validation of biological hypotheses. We propose that our method may be used to better characterize cancer, and may therefore impact the future design of therapies that are truly molecularly tailored to individual patients. The method, named SURCOMED, was implemented as a web-based tool, which is publicly available at http://surcomed.vital-it.ch. R scripts are also available at http://surcomed.sourceforge.net).

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.

Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT(+)) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types.

Integrating pathways of Parkinson's disease in a molecular interaction map.

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .