ADSL-generated fumarate binds and inhibits STING to promote tumour immune evasion
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STING directly interacts with PAR to promote apoptosis upon acute ionizing radiation-mediated DNA damage
Acute ionizing radiation (IR) causes severe DNA damage, leading to cell cycle arrest, cell death, and activation of the innate immune system. The role and signaling pathway of stimulator of interferon genes (STING) in IR-induced tissue damage and cell death are not well understood. This study revealed that STING is crucial for promoting apoptosis in response to DNA damage caused by acute IR both in vitro and in vivo. STING binds to poly (ADP‒ribose) (PAR) produced by activated poly (ADP‒ribose) polymerase-1 (PARP1) upon IR. Compared with that in WT cells, apoptosis was suppressed in Stinggt-/gt- cells. Excessive PAR production by PARP1 due to DNA damage enhances STING phosphorylation, and inhibiting PARP1 reduces cell apoptosis after IR. In vivo, IR-induced crypt cell death was significantly lower in Stinggt-/gt- mice or with low-dose PARP1 inhibitor, PJ34, resulting in substantial resistance to abdominal irradiation. STING deficiency or inhibition of PARP1 function can reduce the expression of the proapoptotic gene PUMA, decrease the localization of Bax on the mitochondrial membrane, and thus reduce cell apoptosis. Our findings highlight crucial roles for STING and PAR in the IR-mediated induction of apoptosis, which may have therapeutic implications for controlling radiation-induced apoptosis or acute radiation symptoms.
STING mediates increased self-renewal and lineage skewing in DNMT3A-mutated hematopoietic stem/progenitor cells
Somatic mutations in DNA methyltransferase 3 A (DNMT3A) are frequently observed in patients with hematological malignancies. Hematopoietic stem/progenitor cells (HSPCs) with mutated DNMT3A demonstrate increased self-renewal activity and skewed lineage differentiation. However, the molecular mechanisms underlying these changes remain largely unexplored. In this study, we show that Dnmt3a loss leads to the upregulation of endogenous retroviruses (ERVs) in HSPCs, subsequently activating the cGAS-STING pathway and triggering inflammatory responses in these cells. Both genetic and pharmacological inhibition of STING effectively corrects the increased self-renewal activity and differentiation skewing induced by Dnmt3a deficiency in mice. Notably, targeting STING showed inhibited acute myeloid leukemia (AML) development in a Dnmt3a-KO; Flt3-ITD AML model, comparable to AC220, an FDA-approved FLT3-ITD inhibitor. A patient-derived xenograft (PDX) model further demonstrated that targeting STING effectively alleviates the leukemic burden of DNMT3A-mutant AML. Collectively, our findings highlight a critical role for STING in hematopoietic disorders induced by DNMT3A mutations and propose STING as a potential therapeutic target for preventing the progression of DNMT3A mutation-associated leukemia.
Systemic HER3 ligand-mimicking nanobioparticles enter the brain and reduce intracranial tumour growth
Crossing the blood–brain barrier (BBB) and reaching intracranial tumours is a clinical challenge for current targeted interventions including antibody-based therapies, contributing to poor patient outcomes. Increased cell surface density of human epidermal growth factor receptor 3 (HER3) is associated with a growing number of metastatic tumour types and is observed on tumour cells that acquire resistance to a growing number of clinical targeted therapies. Here we describe the evaluation of HER3-homing nanobiological particles (nanobioparticles (NBPs)) on such tumours in preclinical models and our discovery that systemic NBPs could be found in the brain even in the absence of such tumours. Our subsequent studies described here show that HER3 is prominently associated with both mouse and human brain endothelium and with extravasation of systemic NBPs in mice and in human-derived BBB chips in contrast to non-targeted agents. In mice, systemically delivered NBPs carrying tumoricidal agents reduced the growth of intracranial triple-negative breast cancer cells, which also express HER3, with improved therapeutic profile compared to current therapies and compared to agents using traditional BBB transport routes. As HER3 associates with a growing number of metastatic tumours, the NBPs described here may offer targeted efficacy especially when such tumours localize to the brain.
Characterization of the landscape of the intratumoral microbiota reveals that Streptococcus anginosus increases the risk of gastric cancer initiation and progression
As a critical component of the tumour immune microenvironment (TIME), the resident microbiota promotes tumorigenesis across a variety of cancer types. Here, we integrated multiple types of omics data, including microbiome, transcriptome, and metabolome data, to investigate the functional role of intratumoral bacteria in gastric cancer (GC). The microbiome was used to categorize GC samples into six subtypes, and patients with a high abundance of Streptococcus or Pseudomonas had a markedly worse prognosis. Further assays revealed that Streptococcus anginosus (SA) promoted tumour cell proliferation and metastasis while suppressing the differentiation and infiltration of CD8+ T cells. However, antibiotic treatment significantly suppressed tumorigenesis in SA+ mice in vivo. We further demonstrated that the SA arginine pathway increased the abundance of ornithine, which may be a major contributor to reshaping of the TIME. Our findings demonstrated that SA, a novel risk factor, plays significant roles in the initiation and progression of GC, suggesting that SA might be a promising target for the diagnosis and treatment of GC.
Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles
Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.
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