Quinoline-3-carboxamides such as tasquinimod are not specific inhibitors of S100A9

Blocking S100A9-signaling is detrimental to the initiation of anti-tumor immunity

S100A9, a multifunctional protein mainly expressed by neutrophils and monocytes, poses an immunological paradox. In virus infections or sterile inflammation, it functions as an alarmin attracting innate immune cells, as well as mediating proinflammatory effects through TLR4 signaling. However, in cancer, S100A9 levels have been shown to associate with poor prognosis and lack of response to immunotherapy. Its expression by myeloid cells has been related to an immune suppressive phenotype, the so-called myeloid derived suppressor cells (MDSCs). Targeting S100A9 in cancer has therefore been proposed as a potential way to relieve myeloid-mediated immune suppression. Surprisingly, we found that blocking the extracellular TLR4 signaling from S100A9 using the inhibitor Paquinimod, resulted in increased tumor growth and a detrimental effect on anti-PD-L1 efficacy in the CT26 tumor model. This effect was caused by a reduction in the tumor immune infiltration to about half of untreated controls, and the reduction was made up of a 5-fold decrease in Ly6Chigh monocytic cells. The suppressive Ly6G+ myeloid cells compartment was not reduced by Paquinimod treatment, suggesting alternative mechanisms by which S100A9 contributes to myeloid-mediated suppression. Intratumoral injection of recombinant S100A9 early after mice inoculation with CT26 cells had an anti-tumor effect. These findings indicate an important yet understudied role of S100A9 as an alarmin and immune stimulatory signal in cancer settings, and highlight the potential to exploit such signals to promote beneficial anti-tumor responses.

S100A9 interacts with a dynamic region on CD14 to activate Toll-like receptor 4

S100A9 is a Damage Associated Molecular Pattern (DAMP) that activates inflammatory pathways via Toll-like receptor 4 (TLR4). This activity plays important homeostatic roles in tissue repair, but can also contribute to inflammatory diseases. The mechanism of activation is unknown. Here, we follow up on a previous observation that the protein CD14 is an important co-receptor that enables S100A9 to activate TLR4. Using cell-based functional assays and a combination of mutations and pharmocological perturbations, we found that CD14 must be membrane bound to potentiate TLR4 activation by S100A9. Additionally, S100A9 is sensitive to inhibitors of pathways downstream of TLR4 internalization. Together, this suggests that S100A9 induces activity via CD14-dependent internalization of TLR4. We then used mutagenesis, structural modeling, and in vitro binding experiments to establish that S100A9 binds to CD14's N-terminus in a region that overlaps with, but is not identical to, the region where CD14 binds its canonical ligand, lipopolysaccharide (LPS). In molecular dynamics simulations, this region of the protein is dynamic, allowing it to reorganize to recognize both S100A9 (a soluble protein) and LPS (a small hydrophobic molecule). Our work is the first attempt at a molecular characterization of the S100A9/CD14 interaction, bringing us one step closer to unraveling the full mechanism by which S100A9 activates TLR4/MD-2.

S100A9: The Unusual Suspect Connecting Viral Infection and Inflammation

The study of S100A9 in viral infections has seen increased interest since the COVID-19 pandemic. S100A8/A9 levels were found to be correlated with the severity of COVID-19 disease, cytokine storm, and changes in myeloid cell subsets. These data led to the hypothesis that S100A8/A9 proteins might play an active role in COVID-19 pathogenesis. This review explores the structures and functions of S100A8/9 and the current knowledge on the involvement of S100A8/A9 and its constituents in viral infections. The potential roles of S100A9 in SARS-CoV-2 infections are also discussed.

Endogenous Ligands of TLR4 in Microglia: Potential Targets for Related Neurological Diseases

Chronic inflammation mediated by microglia is a cause of some neuroinflammatory diseases. TLR4, a natural immune receptor on microglia, plays an important role in the occurrence of inflammation and the process of diseases. TLR4 can be activated by a variety of ligands to trigger inflammatory responses, including endogenous ligands HMGB1, S100A8/9, Heme, and Fetuin-A. As ligands derived from the body itself, they have the ability to bind directly to TLR4 and can be used as inducers of aseptic inflammation. In the past 20 years, targeting ligands rather than receptors has become an emerging therapeutic strategy for the treatment of diseases, so understanding the relationship between microglia, TLR4, TLR4 ligands, and corresponding diseases may have new implications for the treatment of diseases. In the article, we will discuss the TLR4 and the endogenous substances that can activate the TLR4 signaling pathway and present literature support for their role in neuroinflammatory diseases.

Tasquinimod suppresses tumor cell growth and bone resorption by targeting immunosuppressive myeloid cells and inhibiting c-MYC expression in multiple myeloma

BACKGROUND

Immunotherapy emerged as a promising treatment option for multiple myeloma (MM) patients. However, therapeutic efficacy can be hampered by the presence of an immunosuppressive bone marrow microenvironment including myeloid cells. S100A9 was previously identified as a key regulator of myeloid cell accumulation and suppressive activity. Tasquinimod, a small molecule inhibitor of S100A9, is currently in a phase Ib/IIa clinical trial in MM patients (NCT04405167). We aimed to gain more insights into its mechanisms of action both on the myeloma cells and the immune microenvironment.

METHODS

We analyzed the effects of tasquinimod on MM cell viability, cell proliferation and downstream signaling pathways in vitro using RNA sequencing, real-time PCR, western blot analysis and multiparameter flow cytometry. Myeloid cells and T cells were cocultured at different ratios to assess tasquinimod-mediated immunomodulatory effects. The in vivo impact on immune cells (myeloid cell subsets, macrophages, dendritic cells), tumor load, survival and bone disease were elucidated using immunocompetent 5TMM models.

RESULTS

Tasquinimod treatment significantly decreased myeloma cell proliferation and colony formation in vitro, associated with an inhibition of c-MYC and increased p27 expression. Tasquinimod-mediated targeting of the myeloid cell population resulted in increased T cell proliferation and functionality in vitro. Notably, short-term tasquinimod therapy of 5TMM mice significantly increased the total CD11b⁺ cells and shifted this population toward a more immunostimulatory state, which resulted in less myeloid-mediated immunosuppression and increased T cell activation ex vivo. Tasquinimod significantly reduced the tumor load and increased the trabecular bone volume, which resulted in prolonged overall survival of MM-bearing mice in vivo.

CONCLUSION

Our study provides novel insights in the dual therapeutic effects of the immunomodulator tasquinimod and fosters its evaluation in combination therapy trials for MM patients.

From cell to cell: Identification of actionable targets in bone marrow fibrosis using single-cell technologies

Single-cell technologies have rapidly developed in recent years and have already had a significant impact on the research of myeloproliferative neoplasms. The increasing number of publicly available data sets allows characterization of the bone marrow niche in patients and mouse models at unprecedented resolution. Single-cell RNA sequencing has successfully been used to identify and characterize disease-driving cell populations and to identify the alarmin S100A8/A9 as an important mediator of myelofibrosis and potent therapeutic target. It is now possible to execute a streamlined set of experiments to specifically identify and validate actionable target genes functionally with the advance of reliable in vivo models and the possibility of conducting single-cell analyses with a minimal amount of patient material. The advent of large-scale analyses of both hematopoietic and non-hematopoietic bone marrow cells will allow comprehensive network analyses guiding an increasingly detailed mapping of the MPN interactome.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such as protein-protein interaction of S100A9 with RAGE, TLR4/MD2, or CD147 (EMMPRIN) hinders the rational drug design of S100A9 inhibitors. Herein we first report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness. Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits. Moreover, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through a consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into designing novel drugs targeting S100A9.