Establishment of tumor inflammasome clusters with distinct immunogenomic landscape aids immunotherapy

Inhibiting interferon-γ induced cancer intrinsic TNFRSF14 elevation restrains the malignant progression of glioblastoma

BACKGROUND

Prolonged interferon-γ signaling activation induces cancer resistance to therapeutics, especially immunotherapy. However, the detailed mechanisms are not well characterized. In present study, we explored cancer intrinsic resistant mechanisms employing for evading immune checkpoint blockade (ICB) and searched for key immune checkpoints contributing to the constitution of suppressive immune microenvironment of glioblastoma (GBM).

METHODS

We screened key immune checkpoint (IC) associated with IFN signaling activation in GBM according to integrated transcriptomic profiling on the ICs. Expression analysis and functional assays revealed that malignant cells elevated the key IC, TNFRSF14 expression under IFN-γ stimulation, which enhanced their proliferation and in vivo tumorigenicity. Therapeutic efficiency of TNFRSF14 disruption in GBM was evaluated with in vitro and in vivo functional assays, including immunofluorescence, transwell, RT-qPCR, flow cytometry, mass cytometry, and mice preclinical GBM models. Moreover, the improvement of TNFRSF14 blockade on the efficacy of PD-L1 treatment was examined in mice intracranial xenograft bearing models.

RESULTS

TNFRSF14, a previously poorly characterized IC, was disclosed as a checkpoint with malignant intrinsic elevation closely associated with type II not type I IFN signaling activation in GBM. Anti-PD-L1 treatment induces compensatory TNFRSF14 elevation, while enhancing IFN-γ production. TNFRSF14 phosphorylates FAK at Y397 and consequently activates NF-κB, which not only strengthens the tumorigenicity of GBM cells, but also enhances TAMs recruitment through elevating CXCL1/CXCL5 secretion from GBM cells. TNFRSF14 ablation reduces the tumorigenicity of GBM cells, reshapes the immunosuppressive microenvironment, and enhances therapeutic efficacy of anti-PD-L1 in mouse orthotopic GBM model.

CONCLUSION

Our findings highlight a malignant TNFRSF14/FAK axis as a potential target to blunt cancer-intrinsic resistance to ICB treatment, which may help improve the therapeutic efficiency of immunotherapy in malignancies.

Unveiling the link between inflammasomes and skin cutaneous melanoma: Insights into expression patterns and immunotherapy response prediction

Skin cutaneous melanoma (SKCM) is one of the most malignant forms of skin cancer, characterized by its high metastatic potential and low cure rate in advanced stages. Despite advancements in clinical therapies, the overall cure rate for SKCM remains low due to its resistance to conventional treatments. Inflammation is associated with the activation and regulation of inflammatory responses and plays a crucial role in the immune system. It has been implicated in various physiological and pathological processes, including cancer. However, the mechanisms of inflammasome activation in SKCM remain largely unexplored. In this study, we quantified the expression level of six inflammasome-related gene sets using transcriptomic data from SKCM patients. As a result, we found that inflammasome features were closely associated with various clinical characteristics and served as a favorable prognostic factor for patients. A functional enrichment analysis revealed the oncogenic role of inflammasome features in SKCM. Unsupervised clustering was applied to identify immune clusters and inflammatory subtypes, revealing a significant overlap between immune cluster 4 and SKCM subtype 2. The CASP1, GSDMD, NLRP3, IL1B, and IL18 features could predict immune checkpoint blockade therapy response in various SKCM cohorts. In conclusion, our study highlighted the significant association between the inflammasome and cancer treatment. Understanding the role of inflammasome signaling in SKCM pathology can help identify potential therapeutic targets and improve patient prognosis.

Pyroptosis and inflammasomes in cancer and inflammation

Nonprogrammed cell death (NPCD) and programmed cell death (PCD) are two types of cell death. Cell death is significantly linked to tumor development, medication resistance, cancer recurrence, and metastatic dissemination. Therefore, a comprehensive understanding of cell death is essential for the treatment of cancer. Pyroptosis is a kind of PCD distinct from autophagy and apoptosis in terms of the structure and function of cells. The defining features of pyroptosis include the release of an inflammatory cascade reaction and the expulsion of lysosomes, inflammatory mediators, and other cellular substances from within the cell. Additionally, it displays variations in osmotic pressure both within and outside the cell. Pyroptosis, as evidenced by a growing body of research, is critical for controlling the development of inflammatory diseases and cancer. In this paper, we reviewed the current level of knowledge on the mechanism of pyroptosis and inflammasomes and their connection to cancer and inflammatory diseases. This article presents a theoretical framework for investigating the potential of therapeutic targets in cancer and inflammatory diseases, overcoming medication resistance, establishing nanomedicines associated with pyroptosis, and developing risk prediction models in refractory cancer. Given the link between pyroptosis and the emergence of cancer and inflammatory diseases, pyroptosis-targeted treatments may be a cutting-edge treatment strategy.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique innate immune-mediated inflammatory PCD pathway that integrates components from other PCD pathways. Here, we designed a systematic computational framework to determine the pancancer clinical significance of PANoptosis and identify targetable biomarkers. We found that high expression of PANoptosis genes was detrimental in low grade glioma (LGG) and kidney renal cell carcinoma (KIRC). ZBP1, ADAR, CASP2, CASP3, CASP4, CASP8 and GSDMD expression consistently had negative effects on prognosis in LGG across multiple survival models, while AIM2, CASP3, CASP4 and TNFRSF10 expression had negative effects for KIRC. Conversely, high expression of PANoptosis genes was beneficial in skin cutaneous melanoma (SKCM), with ZBP1, NLRP1, CASP8 and GSDMD expression consistently having positive prognostic effects. As a therapeutic proof-of-concept, we treated melanoma cells with combination therapy that activates ZBP1 and showed that this treatment induced PANoptosis. Overall, through our systematic framework, we identified and validated key innate immune biomarkers from PANoptosis which can be targeted to improve patient outcomes in cancers.

Tumor-intrinsic NLRP3-HSP70-TLR4 axis drives premetastatic niche development and hyperprogression during anti-PD-1 immunotherapy

The tumor-intrinsic NOD-, LRR- and pyrin domain-containing protein-3 (NLRP3) inflammasome-heat shock protein 70 (HSP70) signaling axis is triggered by CD8⁺ T cell cytotoxicity and contributes to the development of adaptive resistance to anti-programmed cell death protein 1 (PD-1) immunotherapy by recruiting granulocytic polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) into the tumor microenvironment. Here, we demonstrate that the tumor NLRP3-HSP70 axis also drives the accumulation of PMN-MDSCs into distant lung tissues in a manner that depends on lung epithelial cell Toll-like receptor 4 (TLR4) signaling, establishing a premetastatic niche that supports disease hyperprogression in response to anti-PD-1 immunotherapy. Lung epithelial HSP70-TLR4 signaling induces the downstream Wnt5a-dependent release of granulocyte colony-stimulating factor (G-CSF) and C-X-C motif chemokine ligand 5 (CXCL5), thus promoting myeloid granulopoiesis and recruitment of PMN-MDSCs into pulmonary tissues. Treatment with anti-PD-1 immunotherapy enhanced the activation of this pathway through immunologic pressure and drove disease progression in the setting of Nlrp3 amplification. Genetic and pharmacologic inhibition of NLRP3 and HSP70 blocked PMN-MDSC accumulation in the lung in response to anti-PD-1 therapy and suppressed metastatic progression in preclinical models of melanoma and breast cancer. Elevated baseline concentrations of plasma HSP70 and evidence of NLRP3 signaling activity in tumor tissue specimens correlated with the development of disease hyperprogression and inferior survival in patients with stage IV melanoma undergoing anti-PD-1 immunotherapy. Together, this work describes a pathogenic mechanism underlying the phenomenon of disease hyperprogression in melanoma and offers candidate targets and markers capable of improving the management of patients with melanoma.

Applications of machine learning in tumor-associated macrophages

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

Novel Inflammasome-Based Risk Score for Predicting Survival and Efficacy to Immunotherapy in Early-Stage Non-Small Cell Lung Cancer

Immune checkpoint inhibitors (ICI) for early-stage non-small cell lung cancer (NSCLC) have been approved to improve outcomes and reduce recurrence. Biomarkers for patient selection are needed. In this paper, we proposed an inflammasome-based risk score (IRS) system for prognosis and prediction of ICI response for early-stage NSCLC. Cox regression analysis was used to identify significant genes (from 141 core inflammasome genes) for overall survival (OS) in a microarray discovery cohort (n = 467). IRS was established and independently validated by other datasets (n = 1320). We evaluated the inflammasome signaling steps based on five gene sets, which were IL1B-, CASP-1-, IL18-, GSDMD-, and inflammasome-regulated genes. Gene set enrichment analysis, the Kaplan–Meier curve, receiver operator characteristic with area under curve (AUC) analysis, and advanced bioinformatic tools were used to confirm the ability of IRS in prognosis and classification of patients into ICI responders and non-responders. A 30-gene IRS was developed, and it indicated good risk stratification at 10-year OS (AUC = 0.726). Patients were stratified into high- and low-risk groups based on optimal cutoff points, and high-risk IRS had significantly poorer OS and relapse-free survival. In addition, the high-risk group was characterized by an inflamed immunophenotype and higher proportion of ICI responders. Furthermore, expression of SLAMF8 was the key gene in IRS and indicated good correlation with biomarkers associated with immunotherapy. It could serve as a therapeutic target in the clinical setting of immunotherapy.

The role of pyroptosis in modulating the tumor immune microenvironment

The tumor immune microenvironment (TIME) plays a key role in immunosuppression in cancer, which results in tumorigenesis and tumor progression, and contributes to insensitivity to chemotherapy and immunotherapy. Understanding the mechanism of TIME formation is critical for overcoming cancer. Pyroptosis exerts a dual role in modulating the TIME. In this review, we summarize the regulatory mechanisms of pyroptosis in modulating the TIME and the potential application of targeted pyroptosis therapy in the clinic. Several treatments targeting pyroptosis have been developed; however, the majority of treatments are still in preclinical studies. Only a few agents have been used in clinic, but the outcomes are unsatisfactory. More studies are necessary to determine the role of pyroptosis in cancer, and more research is required to realize the application of treatments targeting pyroptosis in the clinic.

Chemerin enhances mesenchymal features of glioblastoma by establishing autocrine and paracrine networks in a CMKLR1-dependent manner

Glioblastoma multiforme (GBM) with mesenchymal features exhibits enhanced chemotherapeutic resistance and results in reduced overall survival. Recent studies have suggested that there is a positive correlation between the GBM mesenchymal status and immune cell infiltration. However, the mechanisms by which GBM acquires its mesenchymal features in a tumor immune microenvironment-dependent manner remains unknown. Here, we uncovered a chemerin-mediated autocrine and paracrine network by which the mesenchymal phenotype of GBM cells is strengthened. We identified chemerin as a prognostic secretory protein mediating the mesenchymal phenotype-promoting network between tumor-associated macrophages (TAMs) and tumor cells in GBM. Mechanistically, chemerin promoted the mesenchymal features of GBM by suppressing the ubiquitin-proteasomal degradation of CMKLR1, a chemerin receptor predominantly expressed on TAMs and partially expressed on GBM cells, thereby enhancing NF-κB pathway activation. Moreover, chemerin was found to be involved in the recruitment of TAMs in the GBM tumor microenvironment. We revealed that chemerin also enhances the mesenchymal phenotype-promoting ability of TAMs and promotes their M2 polarization via a CMKLR1/NF-κB axis, which further exacerbates the mesenchymal features of GBM. Blocking the chemerin/CMKLR1 axis with 2-(α-naphthoyl) ethyltrimethylammonium iodide disrupted the mesenchymal network and suppressed tumor growth in GBM. These results suggest the therapeutic potential of targeting the chemerin/CMKLR1 axis to block the mesenchymal network in GBM.