CXC chemokine x PDL1

Immune checkpoint blockade (ICB) therapy has provided a promising treatment option for patients with triple-negative breast cancer (TNBC). However, existing efficacy prediction strategies based on programmed death-ligand 1 (PD-L1) expression and Tumor Mutational Burden (TMB) have significant limitations, leading to inaccurate patient selection.

This study integrated multi-omics data obtained from GEO, TCGA, and GTEx datasets. Based on ComBat correction results, two deep learning-driven unsupervised clustering methods were constructed to identify potential immune subtypes. These identified TNBC subtypes were subjected to comprehensive bioinformatics analyses to investigate their differences in overall survival, immune cell infiltration, and functional enrichment analysis, which clarified the biological rationale of the proposed classification. Subsequently, differential expression analysis (DEA) and multiple machine learning algorithms were applied to identify subtype-specific features, with the expression and differentiation status of the key feature in immune cells further validated using single-cell sequencing (scRNA-seq). Finally, co-expression networks at multiple cellular levels were constructed to predict the co-expressed regulatory targets of the key feature, and the hypothesized regulatory mechanism was preliminarily validated in in vitro experiments (qRT-PCR, western blotting, immunofluorescence, and ELISA).

After 5-fold cross-validation, the AE-K-means clustering method classified TNBC into three distinct latent subtypes. This algorithm exhibited superior classification efficacy compared with the other models (including NMF, ConsensusClusterPlus and VAE-GMM), as demonstrated by markedly elevated Silhouette Coefficients (training: 0.585 ± 0.030; test: 0.698 ± 0.103; validation: 0.611) and substantially reduced Davies-Bouldin Index values (training: 0.623 ± 0.032; test: 0.385 ± 0.218; validation: 0.558) at K = 3. These three identified TNBC subtypes displayed significant differences in overall survival and exhibited pronounced immune heterogeneity. Specifically, the K2 subtype was characterized by high PD-L1 expression and abundant M1 macrophage infiltration, while the K3 subtype displayed an opposing, immunosuppressive microenvironment. Feature selection results from multiple machine learning models were intersected to identify key features including CXCL9, and an RF model constructed based on these intersected genes showed high efficacy, particularly in distinguishing the K2 and K3 subtypes (AUC = 0.941). Further scRNA-seq analysis revealed that CXCL9 was specifically highly expressed in myeloid cells, and the proportion of CXCL9⁺ macrophages differed significantly between responders and non-responders. Given the strong positive correlation between CXCL9 and IDO1 expression, we subsequently treated macrophages with IDO1 inhibitors and observed a marked upregulation in both the expression and secretion of CXCL9, suggesting that IDO1 overexpression in macrophages may represent a potential mechanism underlying CXCL9 exhaustion in the tumor microenvironment.

Our study developed a novel immune classification system for TNBC, identifying CXCL9 as a key biomarker positively correlated with ICB response and regulated by IDO1. These findings provide a basis for precision immunotherapy in TNBC and support the exploration of IDO1 inhibitors in combination with ICB.

Glioblastomas (GBMs) are highly lethal brain tumors with limited treatment options and resistance to immune checkpoint inhibitors due to their immunosuppressive tumor microenvironment. Here, we identify OLIG2 as a key regulator of immune evasion in GBM stem-like cells, which inhibits CD8+ T cell-dependent antitumor immunity while promoting protumor macrophage polarization. Mechanistically, OLIG2 recruited HDAC7 to repress CXCL10 transcription, inducing STAT3 activation in tumor-associated macrophages (TAMs) and decreasing CD8+ T cell infiltration and activation. Genetic deletion of OLIG2 significantly increased CXCL10 secretion, shifting TAMs toward an antitumor phenotype and enhancing CD8+ T cell activities. Furthermore, upregulated OLIG2 expression was correlated with resistance to immune checkpoint inhibitors in patients with GBMs. OLIG2 inhibition by either genetic deficiency or pharmacological targeting with CT-179 sensitized GBM tumors to anti-PD-L1 therapy, enhancing antitumor immune responses and prolonging survival. Our findings reveal OLIG2+ glioma stem-like cells as critical mediators of immune evasion and identify the OLIG2/HDAC7/CXCL10 axis as a potential therapeutic target to enhance immune checkpoint inhibitor efficacy and improve immunotherapy outcomes in aggressive GBMs.

Adenosine-to-inosine (A-to-I) RNA editing, predominantly catalyzed by the enzyme adenosine deaminase acting on RNA 1 (ADAR1), has attracted interest due to its essential functions in regulating immune response and cancer progression. This research investigates ADAR1 inhibition as a promising strategy aimed at improving immunotherapy efficacy in lung adenocarcinoma (LUAD) and explores the underlying mechanisms. Findings from murine models demonstrate that ADAR1 suppression within tumors notably improves the immune microenvironment, marked by increased PD-L1 expression and enhanced CD8⁺ T-cell infiltration, as well as elevated levels of CXCL9, CXCL10, and CXCL11. These changes promote antitumor T-cell immune responses and amplify the effects of immunotherapy. Mechanistic investigations further reveal that deficiency in ADAR1 leads to an increase in double-stranded RNA (dsRNA), which serves as a substrate for A-to-I editing. This activates downstream signaling via dsRNA receptors, including RIG-I and MAVS, thereby inducing the IFN-β pathway. Significantly, IFN-β contributes to the ADAR1-dependent modulation of the tumor immune microenvironment and carcinogenesis in LUAD. Clinical validation in LUAD patients further confirms that reduced ADAR1 expression is associated with improved immunotherapy responses. These findings suggest inhibiting ADAR1-mediated A-to-I RNA editing is a promising approach to enhance the efficacy of immunotherapy in LUAD.