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Age-related mutational signature negatively associated with immune activity and survival outcome in triple-negative breast cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is characterized by broad genomic and transcriptional heterogeneity and results in a worse prognosis than other breast cancer types. Here, we integrated genomic and transcriptomic data combined with clinicopathologic information from 538 patients with TNBC and identified four novel significantly mutated genes (SMGs), namely, KDM6A, CD86, RBM47, and IFNGR1. A mutational signature (known as age-related signature 1) featured by enrichment of C > T mutations at NpCpG trinucleotides was associated with worse survival in TNBC (HR, 1.76 [95% CI, 1.07-2.90]; P = .025). We also analyzed gene transcriptomic profiles of TNBC samples and identified immune regulation-related gene pathways (e.g., antigen processing presentation and interferon signaling), and the cell cycle was significantly altered in samples with different signature 1 activity groups. The analysis further revealed that signature 1 was associated with decreased tumor immunogenicity and immunocyte infiltration in TNBC. This negative correlation was also observed in lung adenocarcinoma and prostate cancer samples. Furthermore, we found that patients with mutational signature 1 were markedly associated with decreased tumor mutation burden, even after controlling for age, grade, histological type, lymph node status, mutations in BRCA1/2 and ATR, and APOBEC and homologous recombination repair deficiency signatures (OR, 0.19 [95% CI, 0.11-0.32]; P < .001). Overall, this study identified previously unreported SMGs and re-annotated that age-related signature 1 was associated with a weakened immune microenvironment and predictive of poor survival in TNBC, offering opportunities to stratify patients into optimal treatment plans based on genomic subtyping.

SUBMITTER: Chen H

PROVIDER: S-EPMC7458648 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIncreasing evidence showed that the clinical significance of the interaction between hypoxia and immune status in tumor microenvironment. However, reliable biomarkers based on the hypoxia and immune status in triple-negative breast cancer (TNBC) have not been well established. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC.MethodsHypoxia-related genes (HRGs) and Immune-related genes (IRGs) were identified using the weighted gene co-expression network analysis (WGCNA) method and the single-sample gene set enrichment analysis (ssGSEA Z-score) with the transcriptomic profiles from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Then, prognostic hypoxia and immune based genes were identified in TNBC patients from the METABRIC (N = 221), The Cancer Genome Atlas (TCGA) (N = 142), and GSE58812 (N = 107) using univariate cox regression model. A robust hypoxia-immune based gene signature for prognosis was constructed using the least absolute shrinkage and selection operator (LASSO) method. Based on the cross-cohort prognostic hypoxia-immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia-immune status were identified. The prognosis value, hypoxia and immune status, and therapeutic response in different risk groups were analyzed. Furthermore, a nomogram was constructed to predict the prognosis for individual patients, and an independent cohort from the gene expression omnibus (GEO) database was used for external validation.ResultsSix cross-cohort prognostic hypoxia-immune related genes were identified to establish the comprehensive index of hypoxia and immune. Then, patients were clustered into high- and low-risk groups based on the hypoxia-immune status. Patients in the high-risk group showed poorer prognoses to their low-risk counterparts, and the nomogram we constructed yielded favorable performance to predict survival and risk stratification. Besides, the high-risk group had a higher expression of hypoxia-related genes and correlated with hypoxia status in tumor microenvironment. The high-risk group had lower fractions of activated immune cells, and exhibited lower expression of immune checkpoint markers. Furthermore, the ratio of complete response (CR) was greatly declined, and the ratio of breast cancer related events were significantly elevated in the high-risk group.ConclusionThe hypoxia-immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.

Project description:BackgroundAn imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.MethodsWe constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).ResultsCompared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.ConclusionsThe IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

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Age-related mutational signature negatively associated with immune activity and survival outcome in triple-negative breast cancer.

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