Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

Abstract

Background: The 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.

Methods: Genomic, 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.

Results: TP53 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.

Conclusions: TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Keywords: Immune checkpoint; Lung adenocarcinoma; Signaling pathways; TP53; Tumor microenvironment.

Fig. 1

Analysis of copy number variation (CNV) and somatic mutation patterns of patients with lung adenocarcinoma (LUAD). A Mutation information statistic of LUAD patients in LUAD cohort of TCGA. B The top 10 most frequently mutated genes from LUAD patients in the cohort of TCGA. Left side of the panel shows the high mutation frequency genes in the waterfall plot, and the colors indicates different mutation types of the high mutation frequency genes in the right panel. (Genes are ordered by their mutation frequencies, and samples are ordered according to disease histology as indicated by the annotation bottom). C Lollipop plot displaying mutation distribution and protein domains for TP53 in LUAD with the labeled recurrent hotspots. Somatic mutation rate and transcript names are indicated by plot title and subtitle. D Schematic representation of the CNV in the TCGA-LUAD, the outermost ring represents the chromosomes, the red ring represents the gene expanded, and the green ring represents the gene deletion. E–G Identification of significantly differing gene amplifications and deletions. False discovery rates (Q-value) and score alteration of GISTIC2.0 (x axis) is plotted versus genome positions (y axis). The broken line represents centromeres. The green line represents the cut-off point of 0.25 Q for determining significance

Fig. 2

TP53 mutation and response to immunotherapy. AThe TP53 mutation has a significance effect on TMB in lung adenocarcinoma patients. B The effect of TP53 mutation on the mutational signature. C The MSI status predicted by TP53 mutation grouping. D Difference of immune checkpoints expression on the TP53-MUT

Fig. 3

Analysis of drug sensitivity and differences in biological characteristics in patients with lung adenocarcinoma harboring mutations in TP53. A Difference in drug Sensitivity to LUAD with TP53-MUT and TP53-WT TP53, the horizontal axis is the TP53 mutation grouping, and the vertical axis is the 50% inhibitory concentration (IC50). B The difference of KEGG pathway between TP53-MUT and TP53-WT, the horizontal axis is the KEGG pathway, and the vertical axis is the signaling pathway enrichment score. C The difference of Hallmark between TP53-MUTand TP53-WT, the horizontal axis is hallmark, and the vertical axis is hallmark enrichment score

Fig. 4

Differentially expressed genes analysis in mutated and wild-type TP3 groups in the cohort of patients with lung adenocarcinoma. A Association between the TP53 mutation and the TP53 expression. B–D Differential expression analysis. The horizontal axis is the log2 Fold Change, and the vertical axis is -log10(Adjust P value), Red nodes represent upregulation, blue node represent downregulation, and the gray node represents non-significant expression. B represents differentially expressed lncRNA, C represents differentially expressed miRNA, and D represents differentially expressed mRNA. E GO enrichment analysis was performed on differentially expressed mRNA. (F)KEGG pathway enrichment analysis

Fig. 5

Gene set enrichment analysis (GSEA) function enrichment analysis. A–G Results of GSEA enrichment analysis. A–C Top 3 GO enrichments. D–F Top 3 KEGG pathway enrichments. G–I Top 3 Hallmark pathway enrichments

Fig. 6

Protein–protein interaction network analysis. A Protein–protein intersection network of differentially expressed genes in TP53-MUT and TP53-WT patients. Node size represents the degree of connectivity of the indicated protein in the network. B The sub-network module 1 in PPI. Color node denote the MOCDE score for the module and node size represent the degree of connectivity of the module. C The sub-network module 2 in PPI. Color node denote the MOCDE score for the module and node size represent the degree of connectivity of the module. D The sub-network module 3 in PPI. Color node denote the MOCDE score for the module and node size represent the degree of connectivity of the module. E ceRNA (mRNA-miRNA-lncRNA) network. Yellow dots indicate miRNA and red arrows indicate mRNA, whereas green rectangles indicate lncRNA

Fig. 7

TP53 mutation and tumor infiltrates immune cells (TIICs). A Overall immune infiltration in the TP53-MUT and the TP53-WT. B Immune cell content in TP53-MUT and TP53-WT group. The horizontal axis is the immune cell, the vertical axis is the immune cell content. C Immunocyte-associated Heatmap. Blue is positive correlation and red is negative correlation. D Association between TP53-MUT and Plasma cell. E Association between TP53-MUT and the immune gene. F Association between the family of HLA gene

Fig. 8

Construction and validation of a prognostic model in lung adenocarcinoma (LUAD). A Survival analysis of TP53 mutation. B Nomogram. C, D Prediction curve of 3-year survival and 5-year survival of LUAD patients with NOMO model. E Univariate COX Analysis. F Multivariate COX Analysis