Dataset Information

Combination of genomic instability score and TP53 status for prognosis prediction in lung adenocarcinoma.

ABSTRACT: The genomic instability (GI) /homologous recombination deficiency (HRD) score, calculated as the sum of the events of loss of heterozygosity (LOH), large-scale state transition (LST) and telomere allele imbalance (TAI), is used to guide the choice of treatment in several cancers, but its relationship with genomic features, clinicopathological characteristics and prognosis in lung cancer is poorly understood, which could lead to population bias in prospective studies. We retrospectively analyzed 1011 lung cancer patients whose tumor samples were successfully profiled by high-throughput sequencing panel including GI/HRD score. Alterations of many cancer suppressor genes were associated with higher GI/HRD scores, biallelic inactivation of TP53 was correlated with a high GI/HRD score. A combination of two gene alterations exhibited a higher GI/HRD scores than single gene alterations. The GI/HRD score was associated with advanced stages in lung adenocarcinoma but not in lung squamous cell carcinoma. Furthermore, patients with higher GI/HRD scores had significantly shorter overall survival and progression-free survival than patients with lower GI/HRD scores. Finally, patients with a combination of a higher GI/HRD scores and TP53 alteration exhibited an extremely poor prognosis compared with patients with a lower GI/HRD scores and wild-type TP53 (overall survival, training cohort, hazard ratio (HR) = 8.56, P < 0.001; validation cohort, HR = 6.47, P < 0.001; progression-free survival, HR = 4.76, P < 0.001). Our study revealed the prognostic value of the GI/HRD score in lung adenocarcinoma, but not for all lung cancer. Moreover, the combination of the GI/HRD score and TP53 status could be a promising strategy to predict the prognosis of patients with lung adenocarcinoma.

SUBMITTER: Feng J

PROVIDER: S-EPMC10618567 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Combination of genomic instability score and TP53 status for prognosis prediction in lung adenocarcinoma.

Feng Juan J Lan Yang Y Liu Feng F Yuan Ye Y Ge Jia J Wei Sen S Luo Hu H Li Jianjun J Luo Tao T Bian Xiuwu X

NPJ precision oncology 20231031 1

The genomic instability (GI) /homologous recombination deficiency (HRD) score, calculated as the sum of the events of loss of heterozygosity (LOH), large-scale state transition (LST) and telomere allele imbalance (TAI), is used to guide the choice of treatment in several cancers, but its relationship with genomic features, clinicopathological characteristics and prognosis in lung cancer is poorly understood, which could lead to population bias in prospective studies. We retrospectively analyzed ...[more]

PMID: 37907595

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Project description:BackgroundThe TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.MethodsGenomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein-protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.ResultsTP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.ConclusionsTP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Project description:TP53 mutation is a critical driver mutation that affects the carcinogenesis and prognosis of patients with pancreatic cancer (PC). Currently, there is no driver mutation-derived signature based on TP53 mutational status for prognosis and predicting therapeutic response in PC. In the present study, we characterized the TP53 mutational phenotypes in multiple patient cohorts and developed a prognostic TP53-associated signature based on differentially expressed genes between PC samples with mutated TP53 and wild-type TP53. Comprehensive investigations were carried out in prognostic stratification, genetic variation, immune cell infiltration, and efficacy prediction of chemotherapy and targeted therapy. We found that TP53 mutation commonly occurred as a survival-related driver mutation in PC. In total, 1,154 differentially expressed genes were found between two distinct TP53 mutational phenotypes. A five-gene TP53-associated signature was constructed in The Cancer Genome Atlas (TCGA) cohort by least absolute shrinkage and selection operator (LASSO)-Cox analysis and proven to be a robust prognostic predictor, which performed well in three independent Gene Expression Omnibus (GEO) validating cohorts. Remarkably, patients in the low-risk group were characterized with decreased tumor mutation burden and activity of immunity, with favorable prognosis. Higher fractions of macrophages M0 and impaired CD8 + T cells were observed in patients in the high-risk group, suggesting immunosuppression with poor survival. Patients in the high-risk group also demonstrated enhanced response to specific chemotherapeutic agents, including gemcitabine and paclitaxel. Several targeted inhibitors, like histamine receptor inhibitor, were screened out as promising drugs for PC treatment. Collectively, the TP53-associated signature is a novel prognostic biomarker and predictive indicator of PC. The signature could contribute to optimizing prognostic stratification and guide effective PC treatments.

Dataset Information

Combination of genomic instability score and TP53 status for prognosis prediction in lung adenocarcinoma.

Publications

Combination of genomic instability score and TP53 status for prognosis prediction in lung adenocarcinoma.

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