Dataset Information

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.

ABSTRACT:

Background

Breast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR).

Methods

Breast cancer genomic and transcriptomic data were obtained from The Cancer Genome Atlas (TCGA). Breast cancer samples were dichotomized into high- and low-TMB groups according to TMB values. Differentially expressed DDR genes between high- and low-TMB groups were incorporated into univariate and multivariate cox regression model to build prognosis model. Performance of the prognosis model was validated in an independently new GEO dataset and evaluated by time-dependent ROC curves.

Results

Between high- and low-TMB groups, there were 6,424 differentially expressed genes, including 67 DDR genes. Ten genes associated with prognosis were selected by univariate cox regression analysis, among which seven genes constituted a panel to predict breast cancer prognosis. The seven-gene prognostic model, as well as the gene copy numbers are closely associated with tumor-infiltrating immune cells.

Conclusion

We established a seven-gene prognostic model comprising MDC1, PARP3, PSMB1, PSMB9, PSMD2, PSMD7, and PSMD14 genes, which provides a basis for further exploration of a population-based prediction of prognosis and immunotherapy response in patients with breast cancer.

SUBMITTER: Teng X

PROVIDER: S-EPMC10282748 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.

Teng Xu X Yang Tianshu T Yuan Baowen B Yang Yunkai Y Liu Jiaxiang J Wang Xin X Wang Yong Y Ma Tianyu T Yin Xin X Yu Hefen H Wang Shuang S Huang Wei W

Frontiers in oncology 20230607

<h4>Background</h4>Breast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR).<h4>Methods</h4>Breast cancer genomic and transcriptomic data were obtained from The Cancer Genome Atlas (TCGA). Breast cancer samples were dichotomized into high- and low-TMB groups according to TMB valu ...[more]

PMID: 37350936

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Project description:DNA damage response and repair (DDR) gene alterations increase tumor-infiltrating lymphocytes, genomic instability, and tumor mutational burden (TMB). Whether DDR-related alterations relate to therapeutic response and prognosis in lung cancer lacking oncogenic drivers remains unknown. Pretherapeutic cancer samples of 122 patients [86 non-small cell lung cancer and 36 small cell lung cancer (SCLC)] harboring no EGFR/ALK alterations were collected. Through whole-exome sequencing, we outlined DDR mutational landscape and determined relationships between DDR gene alterations and TMB or intratumoral heterogeneity. Then, we evaluated the impacts of DDR gene alterations on therapeutic response and prognosis and established a DDR-based model for prognosis prediction. In addition, we investigated somatic interactions of DDR genes and immunomodulatory genes, immune expression patterns, immune microenvironment, and immune infiltration characteristics between DDR-deficient and DDR-proficient samples. Samples from cBioportal datasets were utilized for verification. We found that deleterious DDR gene alterations were closely associated with higher TMB than proficient-types (p < 0.001). DDR mechanisms attach great importance to the determination of patients' prognosis after chemotherapy, and alterations of base excision repair pathway in adenocarcinoma, nucleotide excision repair in squamous carcinoma, and homologous recombination pathway in SCLC tend to associate with worse progression-free survival to first-line chemotherapy (all p < 0.05). A predictive nomogram model was constructed incorporating DDR-related alterations, clinical stage, and smoking status, with the area under curve values of 0.692-0.789 for 1- and 2-year receiver operating characteristic curves in training and testing cohorts. Furthermore, DDR-altered tumors contained enhanced frequencies of alterations in various genes of human leukocyte antigen (HLA) class I pathway including TAP1 and TAP2 than DDR-proficient samples. DDR-deficient types had lower expressions of STING1 (p = 0.01), CD28 (p = 0.020), HLA-DRB6 (p = 0.014) in adenocarcinoma, lower TNFRSF4 (p = 0.017), and TGFB1 expressions (p = 0.033) in squamous carcinoma, and higher CD40 (p = 0.012) and TNFRSF14 expressions (p = 0.022) in SCLC. DDR alteration enhanced activated mast cells in adenocarcinoma (p = 0.044) and M2 macrophage in squamous carcinoma (p = 0.004) than DDR-proficient types. Collectively, DDR gene alterations in lung cancer without oncogenic drivers are positively associated with high TMB. Specific DDR gene alterations tend to associate with worse progression-free survival to initial chemotherapy.

Project description:BackgroundHigh tumor heterogeneity contributes to breast cancer recurrence and metastasis. However, the lack of indicators to serve as precise and reliable means of predicting breast cancer prognosis has yet to be addressed. This study aims to reveal the prognostic relevance of mutations in metastatic breast cancer (MBC) by large-scale circulating tumor DNA (ctDNA) analysis in China.MethodsWe performed ctDNA panel-captured sequencing of 958 blood samples from MBC patients including 494 hormone receptor (HR)-positive cases, 130 human epidermal growth factor receptor 2-positive cases, and 177 triple-negative breast cancer (TNBC) cases. The somatic mutations and potential targets were assessed. Progression-free survival (PFS) was analyzed using the Kaplan-Meier method.ResultsIn 801 of the 958 MBC blood samples, 663 mutated genes and 5,829 nonsynonymous alterations were identified. Mutated genes of the highest frequency were tumor protein p53 (TP53, 54%), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA, 41%), estrogen receptor 1 (ESR1, 12%), myeloid/lymphoid or mixed-lineage leukemia protein 3 (MLL3, 11%), DNA (cytosine-5)-methyltransferase 3A (DNMT3A, 10%), erb-b2 receptor tyrosine kinase 2 (ERBB2, 10%), GATA binding protein 3 (GATA3, 8%), FAT atypical cadherin 1 (FAT1, 7%), phosphatase and tensin homolog (PTEN, 6%), and mitogen-activated protein kinase kinase kinase 1 (MAP3K1, 6%). Enriched mutations and driver genes in MBC varied across stages and in multiple subtypes. Moreover, TP53, ERBB2, or coexisting TP53/PIK3CA mutations in MBC were remarkably related with shorter PFS. Mutated DNA damage response (DDR) genes were significantly associated with tumor mutation burden and mutant-allele tumor heterogeneity score, as well as with worse clinical outcome.ConclusionsOur findings indicate that the mutations of TP53, PIK3CA, ERBB2, and in particular, DDR genes, in MBC might be relevant indicators of unfavorable prognosis in MBC.

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Project description:Background: Triple-negative breast cancer (TNBC) is a special subtype of breast cancer with poor prognosis. DNA damage response (DDR) is one of the hallmarks of this cancer. However, the association of DDR genes with the prognosis of TNBC is still unclear. Methods: We identified differentially expressed genes (DEGs) between normal and TNBC samples from The Cancer Genome Atlas (TCGA). DDR genes were obtained from the Molecular Signatures Database through six DDR gene sets. After the expression of six differential genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR), we then overlapped the DEGs with DDR genes. Based on univariate and LASSO Cox regression analyses, a prognostic model was constructed to predict overall survival (OS). Kaplan-Meier analysis and receiver operating characteristic curve were used to assess the performance of the prognostic model. Cox regression analysis was applied to identify independent prognostic factors in TNBC. The Human Protein Atlas was used to study the immunohistochemical data of six DEGs. The prognostic model was validated using an independent dataset. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA regulatory network. Results: Twenty-three differentially expressed DDR genes were detected between TNBC and normal samples. The six-gene prognostic model we developed was shown to be related to OS in TNBC using univariate and LASSO Cox regression analyses. All the six DEGs were identified as significantly up-regulated in the tumor samples compared to the normal samples in qRT-PCR. The GSEA analysis indicated that the genes in the high-risk group were mainly correlated with leukocyte migration, cytokine interaction, oxidative phosphorylation, autoimmune diseases, and coagulation cascade. The mutation data revealed the mutated genes were different. The gene-TF regulatory network showed that Replication Factor C subunit 4 occupied the dominant position. Conclusion: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.

Dataset Information

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.

Background

Methods

Results

Conclusion

Publications

Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.

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