Biology subjects

Soumelis, V.

Publications and source records attributed to Soumelis, V..

12 recordsLinked to original sources

bioRxiv2026

OKN4395, a first-in-class EP2/EP4/DP1 triple antagonist reprograms prostanoid-driven immunosuppression to restore antitumor immunity

Immune checkpoint inhibitors, particularly T cell targeting anti-PD(L)1 therapies, have revolutionized the treatment landscape for solid malignancies, but challenges related to non-responsiveness and the development of treatment resistance continue to be observed. An additional immunosuppressive axis relates to prostaglandin signaling downstream of cyclooxygenase-2 (COX2), where COX2 inhibitors have shown clinical promise in re-engaging both T and non-T cell immune compartments, yet have suffered from toxicity concerns. We report here the preclinical characterization of OKN4395, a highly potent and specific first-in-class triple antagonist of EP2, EP4, and DP1, major tumor immunosuppressive receptors downstream of COX2. OKN4395 restores immune function on both T cells and NK cells in vitro. Additionally, OKN4395 acts synergistically with anti-PD1 to increase speed and depth of antitumor activity. Overall, these findings robustly support the clinical investigation of OKN4395 in an ongoing Phase 1 trial (NCT06789172) as an innovative cancer immunotherapy for solid tumors, as a single agent and in combination with anti-PD1 therapy. Statement of significanceOKN4395, a first-in-class oral EP2/EP4/DP1 antagonist, reverses prostanoid-driven immunosuppression to restore antitumor immunity. Integrated pharmacology defines mechanism, translational biomarkers as well as both monotherapy and anti-PD1 combination strategies. These data position prostanoid tri-receptor antagonism as a translatable strategy in solid tumors. A global Phase 1 study is underway (NCT06789172).

pharmacology and toxicology↗

bioRxiv2024

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities for studying decades-old archival samples in biobanks, facilitating the generation of data from extensive cohorts. However, the experimental protocols can be labor-intensive and costly; therefore, it is thus essential for researchers to carefully evaluate the strengths and limitations of each technology in relation to their specific research objectives. Here, we report the results of a comparative analysis of the three methods mentioned above on FFPE archival tumor samples from four non-small cell lung cancer, four breast cancer and six diffuse large B-cell lymphoma. We highlight some relative advantages and disadvantages of each method in the context of operational challenges, bioinformatic analysis and biological discovery. Our results show that: 1) all three methods yielded good-quality, highly reproducible transcriptomic data from serial sections of the same FFPE block; 2) GeoMx data contained mixtures of cell types, even when pre-selecting areas with cell type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) data enabled the identification of tumor heterogeneity within and between patients, which could be used to identify targeted therapies. Our data support the use of Visium and Chromium for high-throughput and discovery-driven projects, while the GeoMx platform could be suited for addressing specialized questions on targeted regions. All data generated from this study, including GeoMx, Visium, Chromium, H&E, and expert annotations are publicly available.

bioinformatics↗

bioRxiv2024

Spatial transcriptomics unveils immune cellular ecosystems associated with patient survival in diffuse large B-cell lymphoma

Diffuse Large B-cell Lymphoma (DLBCL) is the most prevalent subtype of non-Hodgkins lymphoma for which current therapeutic strategies remain insufficient. The diffuse nature of DLBCL, lacking distinct tissue structures, represents a challenge to elucidate the cellular organization and interactions within the tumor microenvironment (TME). In this study, we applied spatial transcriptomics to identify spatially-resolved gene expression profiles in 10 DLBCL tissue samples, identifying distinct immune cell infiltration and colocalization patterns. These profiles were classified into six cellular ecosystems (Cell-Eco) that differ in cellular composition, functional patterns, and neighborhood characteristics. The spatially-resolved Cell-Eco signatures provided prognostic scores that stratified patients with different overall survival rates. We also found that C1q+ tumor-associated macrophages are the primary cells interacting with malignant B cells and influencing the spatial architecture of the TME. This study provides novel biological insights into the complexity of the TME in DLBCL and highlights the potential prognostic value of its spatial organization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/613252v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@147a46eorg.highwire.dtl.DTLVardef@7c5236org.highwire.dtl.DTLVardef@1f99506org.highwire.dtl.DTLVardef@a6d271_HPS_FORMAT_FIGEXP M_FIG C_FIG Key findingsO_LISpatial transcriptomics classifies DLBCL tissues based on immune cell infiltration and colocalization patterns. C_LIO_LIDLBCL tumor microenvironment consists of cellular ecosystems (Cell-Eco) that differ in cellular composition, transcriptomic profiles and neighborhood characteristics. C_LIO_LISpatially-resolved Cell-Eco signatures stratify patients with different overall survival. C_LIO_LIC1q+ tumor-associated macrophages primarily interact with malignant B cells and contribute to the spatial organization of the tumor microenvironment. C_LI

immunology↗

bioRxiv2022

Context-dependent effects explain divergent prognostic roles of Tregs in cancer

Assessing cancer prognosis is a challenging task, given the heterogeneity of the disease. Multiple features (clinical, environmental, genetic) have been used for such assessments. The tumor immune microenvironment (TIME) is a key feature, and describing the impact of its many components on cancer prognosis is an active field of research. The complexity of the tumor microenvironment context makes it difficult to use the human TIME to assess prognosis, as demonstrated by the example of regulatory T cells (Tregs). The effect of Tregs on prognosis is ambiguous, with different studies considering them to be negative, positive or neutral. We focused on five different cancer types (breast, colorectal, gastric, lung and ovarian). We clarified the definition of Tregs and their utility for assessing cancer prognosis by taking the context into account via the following parameters: the Treg subset, the anatomical location of these cells, and the neighboring cells. With a meta-analysis on these three parameters, we were able to clarify the prognostic role of Tregs. We found that CD45RO+ Tregs had a reproducible negative effect on prognosis across cancer types, and we gained insight into the contributions of the anatomical location of Tregs and of their neighboring cells on their prognostic value. Our results suggest that Tregs play a similar prognostic role in all cancer types. We also established guidelines for improving the design of future studies addressing the pathophysiological role of Tregs in cancer.

cancer biology↗

bioRxiv2022

GM-CSF-activated human dendritic cells promote type1 T follicular helper cells (Tfh1) polarization in a CD40-dependent manner

T follicular helper (Tfh) cells are specialized CD4+ T cells that regulate humoral immunity by providing B cell help. Tfh1 sub-population was recently identified and associated with severity in infection and autoimmune diseases. The cellular and molecular requirements to induce human Tfh1 differentiation are unknown. Our work investigated the role of human dendritic cells (DC) in promoting Tfh1 differentiation and their physiopathological implication in mycobacterium tuberculosis and mild COVID-19 infection. Activated human blood CD1c+ DC were cocultured with allogeneic naive CD4+ T cells. Single-cell RNA sequencing was then used alongside protein validation to define the induced Tfh lineage. DC signature and correlation with Tfh1 cells in infected patients was established through bioinformatic analysis. Our results show that GM-CSF-activated DC drove the differentiation of Tfh1 cells, displaying typical Tfh molecular features, including 1) high levels of PD-1, CXCR5, and ICOS expression; 2) BCL6 and TBET co-expression; 3) IL-21 and IFN-{gamma} secretion. Mechanistically, GM-CSF triggered the emergence of two distinct DC sub-populations defined by their differential expression of CD40 and ICOS-ligand (ICOS-L), and distinct phenotype, morphology, transcriptomic signature, and function. We showed that Tfh1 differentiation was efficiently and specifically induced by CD40highICOS-Llow DC in a CD40-dependent manner. Tfh1 cells were positively associated with a CD40highICOS-LLow DC signature in patients with latent mycobacterium tuberculosis and mild COVID-19 infection. Our study uncovers a novel CD40-dependent human Tfh1 axis. Immunotherapy modulation of Tfh1 activity might contribute to control diseases where Tfh1 are known to play a key role, such as infections. Significance StatementDendritic cells (DC) play a central role in triggering the adaptive immune response due to their T cell priming functions. Among different T cell subsets, it is still not clear how human type1 T follicular helper cells (Tfh1) differentiate. Tfh1 cells are implicated in several physiopathological conditions, including infections. Here we show that GM-CSF induces diversification of human DC. Only CD40highICOS-LLow DC were able to drive Tfh1 cell differentiation. We found that CD40highICOS-LLow DC signature was associated to Tfh1 cells in mycobacterium tuberculosis and COVID-19 patients. Our data reveal a previously undescribed pathway leading to human Tfh1 cell differentiation and highlight the importance of GM-CSF and CD40 as potential targets for the design of anti-infective therapies.

immunology↗

bioRxiv2021

Characterization and functional interrogation of SARS-CoV-2 RNA interactome

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19 pandemic, which has caused a devastating global health crisis. The emergence of highly transmissible novel viral strains that escape neutralizing responses emphasizes the urgent need to deepen our understanding of SARS-CoV-2 biology and to develop additional therapeutic strategies. Using a comprehensive identification of RNA binding proteins (RBP) by mass spectrometry (ChIRP-M/S) approach, we identified 142 high-confidence cellular factors that bind the SARS-CoV-2 viral genome during infection. By systematically knocking down their expression in a human lung epithelial cell line, we found that the majority of the RBPs identified in our study are proviral factors that regulate SARS-CoV-2 genome replication. We showed that some of these proteins represented drug targets of interest for inhibiting SARS-CoV-2 infection. In conclusion, this study provides a comprehensive view of the SARS-CoV-2 RNA interactome during infection and highlights candidates for host-centered antiviral therapies.

microbiology↗

bioRxiv2021

Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data

BackgroundSingle-cell RNA-seq datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness. ResultsWe investigate the phenomenon of hubness in scRNA-seq data in spaces of increasing dimensionality. We also link increased hubness to increased levels of dropout in sequencing data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the visualization, clustering and trajectory inference tasks in scRNA-seq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualisation perform better, especially for datasets characterized by large intrinsic dimensionality. ConclusionHubness is an important phenomenon in sequencing data. Reducing hubness can be beneficial for the analysis of scRNA-seq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction.

bioinformatics↗

bioRxiv2020

Single cell RNA sequencing of blood antigen-presenting cells in severe Covid-19 reveals multi-process defects in antiviral immunity

COVID-19 can lead to life-threatening acute respiratory failure, characterized by simultaneous increase in inflammatory mediators and viral load. The underlying cellular and molecular mechanisms remain unclear. We performed single-cell RNA-sequencing to establish an exhaustive high-resolution map of blood antigen-presenting cells (APC) in 7 COVID-19 patients with moderate or severe pneumonia, at day-1 and day-4 post-admission, and two healthy donors. We generated a unique dataset of 31,513 high quality APC, including monocytes and rare dendritic cell (DC) subsets. We uncovered multiprocess and previously unrecognized defects in anti-viral immune defense in specific APC compartments from severe patients: i) increase of pro-apoptotic genes exclusively in pDC, which are key effectors of antiviral immunity, ii) sharp decrease of innate sensing receptors, TLR7 and DHX9, in pDC and cDC1, respectively, iii) down-regulation of antiviral effector molecules, including Interferon stimulated genes (ISG) in all monocyte subsets, and iv) decrease of MHC class II-related genes, and MHC class II transactivator (CIITA) activity in cDC2, suggesting a viral inhibition of antigen presentation. These novel mechanisms may explain patient aggravation and suggest strategies to restore defective immune defense.

immunology↗

bioRxiv2020

ICELLNET: a transcriptome-based framework to dissect intercellular communication

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).

systems biology↗

bioRxiv2019

Deciphering the combinatorial interaction landscape

From cellular activation to drug combinations, the control of biological systems involves multiple stimuli that can elicit complex nonlinear interactions. To elucidate the functions and logic of stimulus interactions, we developed SAIL (Synergistic/Antagonistic Interaction Learner). SAIL uses a machine learning classifier trained to categorize interactions across a complete taxonomy of possible combinatorial effects. The strategy resolves the most informative interactions, and helps infer their functions and regulatory mechanisms. SAIL-predicted interaction mechanisms controlling key immune functions were experimentally validated. SAIL can integrate results from multiple datasets to derive general properties of how cells respond to multiple stimuli. Using public immunological datasets, we assembled a fine-grained landscape of [~]30000 interactions. Analysis of the landscape shows the context-dependent functions of individual modulators, and reveals a probabilistic algebra that links the separate and combined stimulus effects. SAIL is available through a user friendly interface to resolve the effect of stimulus and drug combinations.

bioinformatics↗

bioRxiv2019

MMP2 As An Independent Prognostic Stratifier In Oral Cavity Cancers

BackgroundAround 25% of oral cavity squamous cell carcinoma (OCSCC) are not controlled by standard of care. Identifying those patients could offer them possibilities for intensified and personalized regimen. However, there is currently no validated biomarker for OCSCC patient selection in a pre-treatment setting.\n\nPatients and methodsOur objectives were to determine a robust and independent predictive biomarker for disease related death in OCSCC treated with standard of care. Tumor and juxtatumor secretome were analyzed in a prospective discovery cohort of 37 OCSCC treated by primary surgery. Independent biomarker validation was performed by RTqPCR in a retrospective cohort of 145 patients with similar clinical features. An 18-gene signature (18G) predictive of the response to PD-1 blockade was evaluated in the same cohort..\n\nResultsAmong 29 deregulated molecules in a secretome analysis, we identified soluble MMP2 as a prognostic biomarker. In our validation cohort (n=145), high levels of MMP2 and CD276, and low levels of CXCL10 and STAT1 mRNA were associated with poor prognosis in univariate analysis (Kaplan-Meier). MMP2 (p = 0.001) and extra-nodal extension (ENE) (p = 0.006) were independent biomarkers of disease-specific survival (DSS) in multivariate analysis, and defined prognostic groups with 5-year DSS ranging from 36% (MMP2highENE+) to 88% (MMP2lowENE-). The expression of 18G was similar in the different prognostic groups, suggesting comparable responsiveness to anti-PD-1.\n\nConclusionHigh levels of MMP2 was an independent and validated prognostic biomarker, which may be used to select poor prognosis patients for intensified neoadjuvant or adjuvant regimens.

cancer biology↗

bioRxiv2019

PDL1 AND ICOSL DISCRIMINATE HUMAN SECRETORY AND HELPER DENDRITIC CELLS

Dendritic cells (DC) are described as immature at the steady state, with a high antigen capture capacity, turning into a mature state with a strong T cell stimulatory capacity upon activation. Using 16 different stimuli in vitro (130 observations), we describe two states of human activated dendritic cells. PDL1highICOSLlow \"secretory DC\" produced large amounts of inflammatory cytokines and chemokines but induced very low levels of T helper (Th) cytokines following DC-T co-culture; conversely PDL1lowICOSLhigh \"helper DC\" produced low levels of secreted factors but induced high levels of Th cytokines characteristic of a broad range of Th subsets. Secretory DC were phenotypically identified in T cell inflamed primary head and neck squamous cell carcinoma. RNAseq analysis showed that they expressed a typical secretory DC signature, including CD40, PVR, IL1B, TNF, and CCL19. This novel and universal functional dichotomy of human DC opens broad perspectives for the characterization of inflammatory diseases, and for immunotherapy.

immunology↗