CXCL9 as a key feature for deep learning-based immune subtyping and prediction of immune checkpoint blockade response in triple-negative breast cancer

Clinical Snapshot

Key Findings

• P Value: Significant survival differences between subtypes (specific p-values not provided)

• Effect Size: AUC = 0.941 for distinguishing K2 and K3 subtypes

• Primary Outcome: AE-K-means clustering identified three distinct TNBC immune subtypes with significant survival differences

• Nnt Or Sensitivity: Superior classification efficacy compared to existing methods (NMF, ConsensusClusterPlus, VAE-GMM)

• Confidence Interval: Silhouette Coefficients: training 0.585 ± 0.030, validation 0.611

Clinical Application

Requires RNA sequencing capabilities and bioinformatics infrastructure, feasible in major cancer centres Applicable to Australian practice where ICB agents like pembrolizumab are PBS-listed for TNBC. Could inform precision medicine approaches in line with NHMRC precision medicine initiatives TNBC patients being considered for immune checkpoint blockade therapy

Abstract

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.