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Landscape of cancer-associated fibroblasts identifies the secreted biglycan as a protumor and immunosuppressive factor in triple-negative breast cancer.

ABSTRACT: Cancer-associated fibroblasts (CAFs) are essential for tumor microenvironment remodeling and correlate with tumor progression. However, interactions between CAFs and tumor cells and immune cells in triple-negative breast cancer (TNBC) are still poorly explored. Here, we investigate the role of CAFs in TNBC and potential novel mediators of their functions. The clustering of classic markers was applied to estimate the relative abundance of CAFs in TNBC cohorts. Primary fibroblasts were isolated from normal and tumor samples. The RNA and culture medium of fibroblasts were subjected to RNA sequencing and mass spectrometry to explore the upregulated signatures in CAFs. Microdissection and single-cell RNA sequencing datasets were used to examine the expression profiles. CAFs were associated with hallmark signalings and immune components in TNBC. Clustering based on CAF markers in the literature revealed different CAF infiltration groups in TNBC: low, medium and high. Most of the cancer hallmark signaling pathways were enriched in the high CAF infiltration group. Furthermore, RNA sequencing and mass spectrometry identified biglycan (BGN), a soluble secreted protein, as upregulated in CAFs compared to normal cancer-adjacent fibroblasts (NAFs). The expression of biglycan was negatively correlated with CD8 + T cells. Biglycan indicated poor prognostic outcomes and might be correlated with the immunosuppressive tumor microenvironment (TME). In conclusion, CAFs play an essential role in tumor progression and the TME. We identified an extracellular protein, biglycan, as a prognostic marker and potential therapeutic target in TNBC.

SUBMITTER: Zheng S

PROVIDER: S-EPMC8741292 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBreast cancer development and progression involve both germline and somatic mutations. High-throughput genotyping and next-generation sequencing technologies have enabled discovery of genetic risk variants and acquired somatic mutations driving the disease. However, the possible oncogenic interactions between germline genetic risk variants and somatic mutations in triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) have not been characterized. Here, we delineated the possible oncogenic interactions between genes containing germline and somatic mutations in TNBC and non-TNBC and investigated whether there are differences in gene expression and mutation burden between the two types of breast cancer.MethodsWe addressed this problem by integrating germline mutation information from genome-wide association studies with somatic mutation information from next-generation sequencing using gene expression data as the intermediated phenotype. We performed network and pathway analyses to discover molecular networks and signalling pathways enriched for germline and somatic mutations.ResultsThe investigation revealed signatures of differentially expressed and differentially somatic mutated genes between TNBC and non-TNBC. Network and pathway analyses revealed functionally related genes interacting in gene regulatory networks and multiple signalling pathways enriched for germline and somatic mutations for each type of breast cancer. Among the signalling pathways discovered included the DNA repair and Androgen and ATM signalling pathways for TNBC and the DNA damage response, molecular mechanisms of cancer, and ATM and GP6 signalling pathways for non-TNBC.ConclusionsThe results show that integrative genomics is a powerful approach for delineating oncogenic interactions between genes containing germline and genes containing somatic mutations in TNBC and non-TNBC and establishes putative functional bridges between genetic and somatic alterations and the pathways they control in the two types of breast cancer.

Project description:PurposeImmune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment.Patients and methodsRNA sequence (RNA-seq) data from 303 TNBC samples were analyzed using an unsupervised cluster analysis approach to reveal distinctive cellular gene expression patterns within the TNBC tumor microenvironment (TME). A panel of T cell exhaustion signatures, immunosuppressive cell subtypes and clinical features were correlated with the immunotherapeutic response, as assessed according to gene expression patterns. The test dataset was then used to confirm the occurrence of immune depletion status and prognostic features and to formulate clinical treatment recommendations. Concurrently, a reliable risk prediction model and clinical treatment strategy were proposed based on TME immunosuppressive signature differences between TNBC patients with good versus poor survival status and other clinical prognostic factors.ResultsSignificantly enriched TNBC microenvironment T cell depletion signatures were detected in the analyzed RNA-seq data. A high proportion of certain immunosuppressive cell subtypes, 9 inhibitory checkpoints and enhanced anti-inflammatory cytokine expression profiles were noted in 21.4% of TNBC patients that led to the designation of this group of immunosuppressed patients as the immune depletion class (IDC). Although IDC group TNBC samples contained tumor-infiltrating lymphocytes present at high densities, IDC patient prognosis was poor. Notably, PD-L1 expression was relatively elevated in IDC patients that indicated their cancers were resistant to ICB treatment. Based on these findings, a set of gene expression signatures predicting IDC group PD-L1 resistance was identified then used to develop risk models for use in predicting clinical therapeutic outcomes.ConclusionA novel TNBC immunosuppressive tumor microenvironment subtype associated with strong PD-L1 expression and possible resistance to ICB treatment was identified. This comprehensive gene expression pattern may provide fresh insights into drug resistance mechanisms for use in optimizing immunotherapeutic approaches for TNBC patients.

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Landscape of cancer-associated fibroblasts identifies the secreted biglycan as a protumor and immunosuppressive factor in triple-negative breast cancer.

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