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Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy.

ABSTRACT: Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC9296841 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe high heterogeneity of triple negative breast cancer (TNBC) is the main clinical challenge for individualized therapy. Considering that fatty acid metabolism (FAM) plays an indispensable role in tumorigenesis and development of TNBC, we proposed a novel FAM-based classification to characterize the tumor microenvironment immune profiles and heterogeneous for TNBC.MethodsWeighted gene correlation network analysis (WGCNA) was performed to identify FAM-related genes from 221 TNBC samples in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Then, non-negative matrix factorization (NMF) clustering analysis was applied to determine FAM clusters based on the prognostic FAM-related genes, which chosen from the univariate/multivariate Cox regression model and the least absolute shrinkage and selection operator (LASSO) regression algorithm. Then, a FAM scoring scheme was constructed to further quantify FAM features of individual TNBC patient based on the prognostic differentially expressed genes (DEGs) between different FAM clusters. Systematically analyses were performed to evaluate the correlation between the FAM scoring system (FS) with survival outcomes, genomic characteristics, tumor microenvironment (TME) features and immunotherapeutic response for TNBC, which were further validated in the Cancer Genome Atlas (TCGA) and GSE58812 datasets. Moreover, the expression level and clinical significancy of the selected FS gene signatures were further validated in our cohort.Results1860 FAM-genes were screened out using WGCNA. Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features. Then, prognostic gene signatures based on the DEGs between different FAM clusters were identified using univariate Cox regression analysis and Lasso regression algorithm. A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups. Low FS subgroup, characterized by better prognosis and abundance with effective immune infiltration. While patients with higher FS were featured with poorer survival and lack of effective immune infiltration. In addition, two independent immunotherapy cohorts (Imvigor210 and GSE78220) confirmed that patients with lower FS demonstrated significant therapeutic advantages from anti-PD-1/PD-L1 immunotherapy and durable clinical benefits. Further analyses in our cohort found that the differential expression of CXCL13, FBP1 and PLCL2 were significantly associated with clinical outcomes of TNBC samples.ConclusionsThis study revealed FAM plays an indispensable role in formation of TNBC heterogeneity and TME diversity. The novel FAM-based classification could provide a promising prognostic predictor and guide more effective immunotherapy strategies for TNBC.

Project description:BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

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Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy.

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