APOBEC mutational signature predicts prognosis and immunotherapy response in nonsmoking patients with lung adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) with poor survival in advanced stage. Nowadays the rate of nonsmoking patients has dramatically increased and may be associated with the presence of driver mutations. Better understanding of the mutation profile data of nonsmoking LUAD patients are critical to predict survival and provide greater benefits to more patients. The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) has been shown to play an important role in molecular tumorigenesis of NSCLC. However, the clinical relevance of APOBEC in nonsmoking LUAD remains to be understood.

Methods: LUAD patients with somatic mutation and RNA sequencing data obtained from The Cancer Genome Atlas (TCGA) were assessed and screened in the Gene Expression Omnibus. Transcriptome data and mutational signatures were analyzed using R package. Then, we used the least absolute shrinkage and selection operator (LASSO) regression model to construct the APOBEC3 score (APOBEC3 score) model. The prognostic value was evaluated using Kaplan-Meier analysis. Finally, the functional enrichment analysis of differential expressed genes (DEGs) and the immune-related features were also estimated using R package.

Results: By analyzing the mutational profile data of NSCLC in the TCGA database, we found that different mutation patterns existed between smoking and nonsmoking patients, and the APOBEC3 family played an important role in the mutation pattern of nonsmoking patients with LUAD. We established an APOBEC3 score and found that TCW (W = A or T) mutation counts were significantly greater in the high APOBEC3 score group than in the low APOBEC3 score group. Furthermore, there were different immune feathers and prognostic values between the high and low APOBEC3 score patients, suggesting an independent prognostic factor of APOBEC3 in nonsmoking LUAD patients.

Conclusions: We established a comprehensive view of APOBEC3 mutations in nonsmoking LUAD patients. Our review provides new insights into using the APOBEC3 mutation to predict prognosis and improve the immunotherapy response for future applications.

Keywords: Apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3 (APOBEC3); immunotherapy; mutational signature; nonsmoking; prognosis.

Figure 1

Mutation distribution patterns and tumor mutation load in LUAD and LUSC. (A-D) Missense mutations are most frequent in different mutation categories with a median mutation load. SNPs are more frequent than other categories, and the most frequent among SNVs is C-A. (E,G) For each LUAD and LUSC patient, the relative contribution of each feature code (bottom panel) and the estimated number of copy number segments (top panel) are shown as bar charts. The lung adenocarcinoma samples were divided into 2 groups based on the consensus matrix of multiple NMF runs, with each group specified by an enriched feature code. (F,H) Maps of the de novo extracted mutation features identified from LUAD and LUSC mutation data. Each feature is shown as the percentage (y-axis) of mutations attributed to the 96 SBS categories (x-axis) defined by color-coded substitution categories and sequence contexts. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SNPs, single nucleotide polymorphisms; SNVs, single nucleotide variants; SBS, single base substitutions.

Figure 2

The mutational landscape of smoking patients and nonsmoking patients in LUAD. (A) The spectrum of SNV mutations [6] in smoking versus nonsmoking patients in the TCGA-LUAD cohort. (B) The proportion of 6 SNV mutation spectrums in each TCGA-LUAD patient. (C) Box plots comparing the difference in the frequencies of the 6 SNV mutations between smoking and nonsmoking patients in the TCGA-LUAD cohort separately (ns: P>0.05, **: P<0.01, ****: P<0.0001). (D) The bar graphs show the relative contribution of each mutation signature in each nonsmoking patient (top) and the estimated copy number segment estimates (bottom). (E) Mapping of mutation signatures extracted from the mutation data of nonsmoking patients, sown as the percentage of mutations in the 96 SBS categories. (F) The pie chart showing the proportion of patients with both mutation features extracted from nonsmoking patients. (G) The bar chart showing the relative contribution of each mutation signature in each smoking patient (top) and the estimated copy number segment estimates (bottom). (H) Mapping of mutation signatures extracted from smoking patient mutation data, shown as the percentage of mutations in the 96 SBS categories. (I) The pie chart showing the percentage of patients with the 3 mutation signatures extracted from smoking patients. LUAD, lung adenocarcinoma; TCGA, the cancer genome atlas; SNV, single nucleotide variants; SBS, single base substitutions.

Figure 3

Construction of APOBEC3-score in nonsmoking LUAD patients. (A-D) Correlations between APOBEC3 family gene expression and TCW, showing only APOBEC3A, APOBEC3B, APOBEC3D, and APOBEC3F with significant correlations. (E) LASSO coefficient curves generated by the APOBEC3 family determined by non-zero coefficients of the best parameter lambda. (F) Adjusted parameter selection for LASSO regression after 10 cross-validations. (G) Comparison of the correlation between APOBEC3s_score and normalized TCW mutation number in TCGA nonsmoking lung adenocarcinoma patients. (H) Comparison of the correlations between APOBEC3s_score and standardized TCW mutation counts in nonsmoking lung adenocarcinoma patients in the lad_CPTAC_2020 dataset. (I) Classification of samples into high and low subgroups based on the APOBEC3 score. The blue dots represent low APOBEC3 scores and the red dots represent high APOBEC3 scores. (J) Box plots of TCW counts in the high and low APOBEC3 score scoring groups. (K) Analysis of tumor neoantigen load from protein level and RNA level in non-smoking lung cancer patients. LUAD, lung adenocarcinoma; LASSO, the least absolute shrinkage and selection operator; TCGA, the cancer genome atlas.

Figure 4

Immune features and immune cells related to the APOBEC3 score. (A,C) Gene Ontology (GO) enrichment analysis of mRNA of TCGA nonsmoking LUAD and differential genes in high and low scoring groups in luad_CPTAC_2020 protein samples. (B,D) KEGG enrichment analysis of the mRNA of TCGA nonsmoking LUAD and the differential genes of the high and low scoring groups in the luad_CPTAC_2020 protein samples. (E) A superimposed bar graph of the proportion of immune cell infiltration in each TCGA nonsmoking LUAD patient. (F) A box plot demonstrating the difference in the proportion of immune infiltrating cells between the high and low scoring groups (ns, P>0.05, *P<0.05, **P<0.01, ***P<0.001). (G) A heat map of the ssGSEA algorithm statistics of immune infiltration fraction in LUAD_CPTAC_2020 nonsmoking patients. TCGA, the cancer genome atlas; LUAD, lung adenocarcinoma; KEGG, Kyoto Encyclopedia of genes and genomes; ssGSEA, the single sample gene set enrichment analysis.

Figure 5

Potential predictive performance of the APOBEC3 score in nonsmoking LUAD patients. (A) Kaplan–Meier curves showing the relationship between the high score and low score groups of TCGA-LUAD patients and survival time, respectively. The fuchsia line indicates the low scoring group with high APOBEC3 score. The blue line indicates the high scoring group with low APOBEC3 scores. (B) A prognostic nomogram predicts 1 or 3 years OS in patients with TCGA-LUAD (**P<0.01). (C,D) Calibration curves were used to assess the predictive power of the split-line model for OS in patients with TCGA nonsmoking LUAD. (E,F,H) The relationship between risk models and OS in different TCGA nonsmoking LUAD patient cohorts was validated in 3 other cohorts. Results were consistent with the TCGA nonsmoking LUAD cohort. (G) Box plot showing that in the cohort treated with anti-PD1, patients with sustained benefit (DCB) had significantly higher A3 scores than those without sustained benefit (NDB) (*P<0.05). (I) Box plot showing a significant difference in the number of tumor mutations between patients with sustained response (DCB) and patients with sustained response (NDB) in the cohort treated with anti-PD1 (*P<0.05). The results were consistent with the TCGA nonsmoking LUAD cohort. LUAD, lung adenocarcinoma; TCGA, the cancer genome atlas; OS, overall survival.