Biology subjects

Naulaerts, S.

Publications and source records attributed to Naulaerts, S..

5 recordsLinked to original sources

bioRxiv2023

A lymph node-to-tumour PD-L1+macrophage circuit antagonizes dendritic cell immunotherapy

Immune-checkpoint blockers (ICB) provide limited benefit against T cell-depleted tumours, calling for therapeutic innovation. Here, we aimed at designing a new type of dendritic cell (DC) vaccine by unbiased computational integration of multi-omics data from cancer patients. In a first attempt, a DC vaccine designed to present tumor antigens from cancer cells succumbing to immunogenic cancer cell death (ICD) and to elicit high type I interferon (IFN) responses failed to induce the regression of mouse tumors lacking T cell infiltrates. In lymph nodes (LNs), instead of activating CD4+ and CD8+T cells, DCs stimulated immunosuppressive PD-L1+LN-associated macrophages (LAMs) via a type I IFN response. Moreover, DC vaccines of this type stimulated pre-existing, T cell-suppressive, PD-L1+tumour-associated macrophages (TAMs). This created a T cell-suppressive circuit of PD-L1+macrophages, spanning across LNs and tumours. Accordingly, DC vaccines synergised with PD-L1 blockade to deplete PD-L1+macrophages, suppress myeloid inflammation affecting the tumor bed and draining lymph nodes, and de-inhibit effector/stem-like memory T cells, eventually causing tumour regression. The synergistic interaction between the DC vaccine and PD-L1 blockade was lost when DCs were manipulated to lose Ifnar1or Ccr7 or when macrophages were depleted. Interestingly, clinical DC vaccines also potentiated lymphocyte-suppressive PD-L1+TAMs in patients bearing T cell-depleted tumours. Altogether, our results reveal the existence of a novel PD-L1+LAM/TAM-driven immunosuppressive pathway that can be elicited by DC vaccines, yet can be subverted for improving the outcome of immunotherapy.

immunology↗

bioRxiv2023

Tryptophan depletion sensitizes the AHR pathway by increasing AHR expression and GCN2/LAT1-mediated kynurenine uptake, and potentiates induction of regulatory T lymphocytes

BackgroundIndoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan-dioxygenase (TDO) are enzymes catabolizing the essential amino acid tryptophan into kynurenine. Expression of these enzymes is frequently observed in advanced-stage cancers and is associated with poor disease prognosis and immune suppression. Mechanistically, the respective roles of tryptophan shortage and kynurenine production in suppressing immunity remain unclear. Kynurenine was proposed as an endogenous ligand for the aryl hydrocarbon receptor (AHR), which can regulate inflammation and immunity. However, controversy remains regarding the role of AHR in IDO1/TDO-mediated immune suppression, as well as the involvement of kynurenine. In this study, we aimed to clarify the link between IDO1/TDO expression, AHR pathway activation and immune suppression. MethodsAHR expression and activation was analyzed by qRT-PCR and western blot analysis in cells engineered to express IDO1/TDO, or cultured in medium mimicking tryptophan catabolism by IDO1/TDO. In vitro differentiation of naive CD4+ T cells into regulatory T cells (Tregs) was compared in T cells isolated from mice bearing different Ahr alleles or a knockout of Ahr, and cultured in medium with or without tryptophan and kynurenine. ResultsWe confirmed that IDO1/TDO expression activated AHR in HEK-293-E cells, as measured by the induction of AHR target genes. Unexpectedly, AHR was also overexpressed upon IDO1/TDO expression. AHR overexpression did not depend on kynurenine but was triggered by tryptophan deprivation. Multiple human tumor cell lines overexpressed AHR upon tryptophan deprivation. AHR overexpression was not dependent on GCN2, and strongly sensitized the AHR pathway. As a result, kynurenine and other tryptophan catabolites, which are weak AHR agonists in normal conditions, strongly induced AHR target genes in tryptophan-depleted conditions. Tryptophan depletion also increased kynurenine uptake by increasing SLC7A5 (LAT1) expression in a GCN2-dependent manner. Tryptophan deprivation potentiated Treg differentiation from naive CD4+ T cells isolated from mice bearing an AHR allele of weak affinity similar to the human AHR. ConclusionsTryptophan deprivation sensitizes the AHR pathway by inducing AHR overexpression and increasing cellular kynurenine uptake. As a result, tryptophan catabolites such as kynurenine, more potently activate AHR, and Treg differentiation is promoted. Our results propose a molecular explanation for the combined roles of tryptophan deprivation and kynurenine production in mediating IDO1/TDO-induced immune suppression. SIGNIFICANCEIn preclinical models, tryptophan degradation by IDO1 or TDO was shown to induce tumoral resistance to immune rejection, by restricting inflammation and promoting T-cell tolerance to immunogenic tumor antigens. However, the mechanism that translates these metabolic changes into T-lymphocyte malfunction within the tumor microenvironment (TME) is still uncertain. It has been proposed that kynurenine, the main tryptophan catabolite, acts as an endogenous ligand for the aryl hydrocarbon receptor (AHR), leading to the suggestion that the IDO1/Kyn/AHR axis could play a key role in modulating inflammatory and immune responses. However, recent studies challenged the notion that kynurenine is a genuine and potent AHR agonistic ligand. Moreover, the relative role of tryptophan depletion versus kynurenine production in IDO1/TDO mediated immune suppression remains unknown. In this work, we further explored and clarified the association between IDO1/TDO activity and AHR activation. Unexpectedly, we observed that tryptophan depletion strongly increased AHR expression, thereby potentiating its activation by weak agonists such as kynurenine and derivatives. Tryptophan depletion thereby potentiated the induction of regulatory T cells. This was particularly true in mouse strains that express an Ahr allele of weak affinity, similar to the human AHR. Tryptophan depletion also increased cellular kynurenine uptake by increasing SLC7A5 (LAT1) expression in a GCN2-dependent manner, thereby also contributing to a better AHR activation by kynurenine upon tryptophan depletion. Altogether, our findings identify a new mechanism explaining IDO/TDO mediated AHR activation and immune suppression, based on the sensitization of the AHR pathway by tryptophan depletion, resulting in a higher AHR stimulation by weak agonists of the kynurenine pathway, and a better induction of regulatory T cells.

cell biology↗

bioRxiv2021

Immunogenomic, single-cell and spatial dissection of CD8+T cell exhaustion reveals critical determinants of cancer immunotherapy

Tumoural-CD8+T cells exhibit exhausted or dysfunctional states. Contrary to immunotherapy-responsive exhausted-CD8+T cells, the clinical features of dysfunctional-CD8+T cells are disputed. Hence, we conducted large-scale multi-omics and multi-dimensional mapping of CD8+T cell-states across multiple cancer patient-cohorts. This identified tumour-specific continuum of CD8+T cell-states across 6 human cancers, partly imprinted by organ-specific immuno-modulatory niches. Herein, melanoma and glioblastoma enriched prototypical exhausted (CD8+TEXT) and severely-dysfunctional (CD8+TSDF) states, respectively. Contrary to CD8+TEXT, CD8+TSDF displayed transcriptomic and epigenetic effector/cytolytic dysfunctions, and dysregulated effector/memory single-cell trajectories, culminating into maladaptive prodeath stress and cell-cycle defects. Suboptimal antigen-priming underscored CD8+TSDF, which was distinct from immune-checkpoints "rich" CD8+TEXT, reflecting chronic antigen-stimulation. Continuum variation also existed on tumour spatial-level, with convergent (CD8+TEXT-supportive vascular regions) and divergent features (dysfunctional CD4+T::CD8+TSDFcell-to-cell interactions) between melanoma and glioblastoma. Globally, IFN{gamma}-IL2 disparities, paucity of intra-tumoural CD4+/CD8+T cells, and myeloid TGF{beta}/wound healing responses, distinguished CD8+TSDF-landscape. Within immuno-oncology clinical-trials, anti-PD1 immunotherapy failed to "reinvigorate" CD8+TSDF-landscape, and instead facilitated effector-dysfunction and TGF{beta}/wound healing. However, cellular immunotherapies (dendritic cell-vaccines, adoptive T-cell therapy) ameliorated assorted CD8+TSDF-landscape disparities, highlighting a roadmap for anti-glioblastoma multimodal-immunotherapy. Collectively, our study comprehensively expands clinical-knowledge on CD8+T cell-exhaustion and suggests that tumour-specific, pre-existing CD8+TEXT/TSDF-states, determine immunotherapy-responses.

immunology↗

bioRxiv2018

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

BackgroundDrug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations).\n\nMethodsEach cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5000 pair-wise drug combinations, 60 cell lines, 4 types of models and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated.\n\nResultsThe evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least two-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models.\n\nConclusionsDespite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

bioinformatics↗

bioRxiv2018

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

Inter-tumour heterogeneity is one of cancers most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

bioinformatics↗