Characterization of aging-related profiles in esophageal cancer reveals a signature predicting favorable outcome and immunogenicity

doi:10.21203/rs.3.rs-1516919/v1

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Characterization of aging-related profiles in esophageal cancer reveals a signature predicting favorable outcome and immunogenicity

https://doi.org/10.21203/rs.3.rs-1516919/v1

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Background

It has been revealed that aging plays crucial roles in tumorigenesis, prognosis, and therapy response of tumors including esophageal carcinoma (ESCA). In this work, we aim to establish an aging-relevant risk signature to assess the survival outcome and immunogenicity status of ESCA patients.

Methods

A total of 351 ESCA patients with both gene expression data and clinical information from 3 independent datasets were curated. The Lasso-Cox regression model was applied to identify the aging genes that contributed most to the survival outcome. The risk signature was constructed by combining the specific gene expression level with the corresponding regression coefficients. Microenvironment-based immunologic factors, mutational burden, and significantly mutated genes were evaluated based on the identified risk groups. One cohort under the immune checkpoint inhibitor (ICI) treatment was used to investigate the immunotherapy predictive roles of the determined aging signature.

Results

Based on the 22 aging-relevant genes, a risk signature was constructed. ESCA patients with low-risk scores had improved survival outcomes in both discovery and validation datasets. Subsequent immunologic exploration demonstrated that the enhanced infiltration abundance of immune-response cells, decreased abundance of immune-suppressive cells, immune response-related signals, and the preferable ICI indicator enrichment were found in the low-risk group. Genomic mutation analysis showed the elevated mutational burden and increased mutation rates of significantly mutated genes of TP53, NAV3, and FAT1 were observed in patients with low-risk scores. In the ICI-treated cohort, we noticed that low-risk aging scores were significantly linked to favorable treatment outcomes and elevated response rates.

Conclusions

In summary, our identified aging risk signature showed the associations with survival, immune microenvironment, immunogenicity, and especially, the immunotherapy efficacy, which offers the clues for guiding prognosis evaluation and immune treatment strategies, and promotes precision therapy of ESCA patients.

Esophageal carcinoma

aging risk signature

survival outcome

immune microenvironment

ICI response

Esophageal cancer (ESCA) is the sixth leading cause of death across all cancers, accounting for 5% of all cancer-relevant deaths in 2018 [1]. ESCA is classified into esophageal squamous cell cancer and adenocarcinoma. Approximately half of ESCA patients present with unresectable or metastatic settings after diagnosing [2]. Over the past few years, owing to the improvements of multidisciplinary treatment, better therapy efficacies and clinical benefits for ESCA patients are observed. However, the survival outcome of unresectable or metastatic ESCA was still poor, with a median survival of less than one year [3]. Recent multiple molecular indicators were identified to predict ESCA prognosis outcomes; nevertheless, they are sometimes ineffective [4]. Therefore, more robust biomarkers are necessary to reliably evaluate the survival status of ESCA patients.

The invention of immune checkpoint inhibitor (ICI) treatment has greatly improved the prognosis of several advanced cancers, such as melanoma [5], renal cancer [6], NSCLC [7], and ESCA [2]. Furthermore, the ICI therapy has been becoming a main clinical practice for LUAD, alongside surgery, chemotherapy, and targeted therapy. Blockading the immune checkpoints of programmed cell death protein 1 (PD-1) or its ligand PD-L1 is so far the best-described immunotherapy approach [8] and is becoming the routine first-line treatment strategy for NSCLC [9]. Although the dramatic therapeutic advantage of ICI treatment was found in both clinical trials and real-world data, a major limitation is that only a subset of patients respond to clinical treatment [3, 10]. Therefore, newly effective determinants to select ESCA patients to receive immunotherapy are urgently necessary.

Age is an important risk factor for most diseases including human tumors. Besides age itself, its relevant molecular traits were also demonstrated to be linked with disease prognosis. Recent studies revealed that particular genes (e.g., APOE [11] and FOXO3 [11, 12]), genomic regions (e.g., 5q33.3 [13]), and numerous single-nucleotide polymorphisms [14, 15] were associated with longevity. It is hard to decompose and explore aging owing to the complex interactions between aging and numerous factors in genome, environment, and age-related diseases [16]. To deeply understand the transcriptome landscape of aging, Peters et al. performed a large-scale transcriptomic exploration and identified aging-relevant genes [16].

In our work, we collected a total of 351 ESCA samples from 3 independent datasets based on publicly available sources to establish and confirm an aging-relevant risk signature. To explore the possible molecular functions behind the determined risk signature, multi-level immunologic analysis was performed and results revealed the strong capacity of this risk signature for assessing immune microenvironment. Moreover, the identified aging signature could predict ICI therapy response and outcome. Findings derived from this study may provide ideas for survival evaluation and immunogenicity prediction of ESCA patients.

Sample collection and aging-related genes

All eligible ESCA patients with both transcriptomic data and clinical information were acquired from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) projects. Totaling 351 patients were included in this study, they are from TCGA (N = 172), GSE53624 (N = 119), and GSE53622 (N = 60). Among the ESCA cohorts, the TCGA cohort harbored the largest sample size with 172 samples, we thus considered it as the discovery cohort and used it for constructing the risk signature. The detailed cohort features and detection platforms of all ESCA patients were illustrated in Table S1. A total of 348 urothelial cancer patients who received anti-PD-L1 treatment in the IMvigor210 trial [17] were collected to ulteriorly explore the association of the determined aging signature with ICI response and outcome. Totaling 1438 aging-related genes was identified based on a previously published study [16].

Development of the aging-relevant risk signature

All 1438 aging-relevant genes were performed univariate Cox regression analysis by using transcriptomic profiles of the ESCA discovery dataset to explore the genes with respect to survival status. Then, aging genes associated with survival risk were presented to the Lasso-Cox regression function (achieved by R glmnet package [18]) to identify the genes that contributed most to prognosis. Based on the Lasso regression coefficient results, the optimal gene panel could be determined to establish a risk signature. By integrating particular gene expression values with their corresponding coefficients, risk cores across all ESCA patients could be obtained. The specific calculation formula was: risk score = \({\sum }_{i}^{n}Coefficient of gene \left(i\right)*Expression of gene \left(i\right)\). ESCA samples were partitioned into low- and high-risk subpopulations with median risk score as the cut-off value.

Assessment of tumor infiltrated immunocytes and immune checkpoints

To investigate the different immunocyte infiltration abundance in low- and high-risk groups, we calculated the abundance for 28 tumor-infiltrating immunocytes [19]. The 28 immune cells were stratified into three types: anti-tumor, pro-tumor, and intermediate immune cells. The representative genes for each immunocyte type were exhibited in Table S2. CIBERSORT algorithm could evaluate the infiltration levels of 22 immune cell subtypes based on 547 informative genes (termed LM22) [20]. In our work, we applied both methods to gain a mutual-confirmation association.

A comprehensive integration of immune checkpoint genes was achieved according to a recent immunogenomic research [21]. In the TCGA ESCA cohort, the checkpoint gene of VISTA was not detected due to the distinct sequencing method. We thus calculated the diverse expression of 33 genes in low- versus high-risk groups.

Enrichment of immunogenicity and ICI response indicators

Recent multiple studies have been reported that immune-relevant signatures are associated with immunogenicity and ICI treatment response. Herein, we collected six typical signatures as follows: 1) T cell-inflamed signature [22]; 2) IFNγ signature; 3) cytolytic activity [23]; 4) immune cell signature [24]; 5) cytokines and chemokines [25]; and 6) immune signaling molecules [25]. The feature genes applied for assessing enrichment scores of each signature were shown in Table S3.

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was used to explore signaling pathways enriched in distinct risk subpopulations. The t parameter obtained from the differential analysis (achieved by limma package [26]) was considered as the input factor for the fgsea method conducted by the R fgsea package. Pathways gleaned from KEGG and HALLMARK databases were used as the comparison signals. Pathway enrichment plot was achieved through the clusterProfiler package [27]. Single sample GSEA (ssGSEA) algorithm embedded in the GSVA package [28] was employed to infer enrichment scores of curated immune signatures and immunocytes for each ESCA patients.

Determination of mutational signatures in the genome

We used SignatureAnalyzer [29] to extract mutational signatures based on somatic mutation data from the TCGA ESCA dataset. Bayesian nonnegative matrix factorization was applied to optimally identify the rank of mutational signatures. Mutation portrait matrix A was divided into two nonnegative matrices W and H (i.e., A ≈ W × H), with W indicating the determined mutational signatures and H reflecting the corresponding mutational activities. The identified mutational signatures were then annotated with the 30 well-curated signatures stored in the COSMIC database [30] based on the cosine similarity.

Significantly mutated genes

MutSigCV method [31] was used to determine significantly mutated genes (SMGs) in TCGA ESCA patients. One SMG must meet three criteria: statistically significant, expressed in TCGA ESCA samples, and an encyclopedia of cell lines [32]. SMGs mutational patterns were exhibited using the maftools package [33].

Statistical analysis

We employed R software (version 4.1.1) to perform the relevant calculations. This study regarded tumor mutation burden (TMB) as the total nonsynonymous mutation count per megabase. Heatmap illustration of identified aging genes in different risk subgroups was achieved with the pheatmap package. Kaplan-Meier method was used to produce survival plots and the group difference was evaluated by the Log-rank test. Multivariate regression models of the forestmodel package were applied to eliminate the confounding factors and obtain an adjusted association. The correlation of numerical and categorical variables with two risk groups was calculated with Wilcoxon rank-sum test and Fisher exact test, respectively.

Identification of the aging risk signature

Since the ESCA dataset from the TCGA has the largest sample size (N = 172) and complete clinical information, we, therefore, considered it as the discovery cohort to construct the aging risk signature. Univariate Cox regression analysis of the collected 1438 aging-relevant genes was performed with transcriptomic data of the TCGA dataset. Results demonstrated that 37 genes were linked with prognosis (all P < 0.05; Table S4). We then used the Lasso-Cox regression with 10-fold cross-validation to identify the aging genes that contributed most to the ESCA survival. The Lasso coefficient information between the log (λ) and the gene penal number was shown in Fig. 1A. The minimum deviance was observed when the gene number was selected as 22 (Fig. 1B). Finally, we chose 22 aging-relevant genes to establish a risk signature for ESCA survival assessment.

The determined 22 genes including SDCCAG3, ANXA5, ARHGEF18, VPREB3, MEOX1, RCAN3, EDAR, SYNE2, C22orf29, PCSK5, CHMP7, RPUSD4, MT1E, HLA-DOB, SREBF1, ATF3, LRRN3, HSPD1, HSPH1, KLRB1, GLA, and DYRK2. Their prognosis contribution values for ESCA patients were exhibited in Table S5. We constructed a risk signature to calculate the risk scores for each ESCA patient (Fig. 1C) according to the linear combination between the determined 22 gene expression levels and corresponding regression weights. The risk association plot of the calculated risk scores with survival times and statuses was shown in Fig. 1C. In addition, distinct expression distribution of the determined 22 genes in low- and high-risk groups was also presented with a heatmap (Fig. 1C).

To investigate the survival predictive capacity of the identified risk signature, we stratified ESCA patients from the discovery cohort into low-risk (N = 86) and high-risk (N = 86) groups. We found that patients of low-risk group had a significantly better overall survival than patients of high-risk group (Log-rank test P < 0.001; Fig. 1D). This link was still significant after controlling for age, sex, stage, grade, smoking status, and alcohol status in a multivariate Cox regression model (HR: 0.12 95% CI: 0.05–0.25, P < 0.001; Fig. 1E).

Corroboration of the aging risk signature

To validate the prognosis predictive roles of the constructed risk signature, we used the disease-specific survival (DSS) and progression-free survival (PFS) information from the TCGA ESCA cohort. Besides, two additional ESCA cohorts derived from the GEO were also used for validation. We observed that ESCA patients with a low-risk score exhibited both improved DSS and PSS as compared with high-risk patients in the TCGA cohort (Log-rank test both P < 0.001; Fig. 2A, C). The associations remained still significant when controlling for clinical confounding factors (multivariate Cox regression P < 0.001 and P = 0.003, respectively; Fig. 1B, D). In two GEO cohorts of GSE53624 and GSE53622, the significantly prolonged overall survival outcomes were also observed in patients with low-risk scores (Log-rank test P < 0.001 and P = 0.021, respectively; Fig. 2E, F).

The identified risk signature associated with preferable immunocyte infiltration and immune activity

Previous two studies have been demonstrated that aging and its relevant genomic traits were involved in the immune microenvironment and immunity [34, 35]. Hence, we speculated that the determined aging signature might regulate the immune cell infiltration and biological circuits associated with the immune activity. A heatmap according to ssGSEA algorithm was achieved to show the different infiltrated levels of 28 immunocyte types in low- versus high-risk groups in the TCGA ESCA cohort (Fig. 3A). Results revealed that increased infiltration of anti-tumor immune cell types, such as central memory CD4⁺ and CD8⁺ T cells, and effector memory CD8⁺ T cells were observed in ESCA patients of low-risk group (all P < 0.05). Moreover, the decreased infiltration of pro-tumor immunocytes, like neutrophils and regulatory T cells were noticed in the low-risk group (both P < 0.05). In addition, activated B cells and gamma delta T cells, which belong to the intermediate immune cell type were also markedly enriched in patients with low-risk scores (both P < 0.05). We also performed immunocyte infiltration analysis via the CIBERSORT method (Figure S1), and consistently, more infiltration of immune response cells (e.g., CD8 T cells, activated natural killer cells, and M1 macrophages) and less infiltration of immune suppressive cells (e.g., regulatory T cells and M2 macrophages) were observed in low-risk ESCA patients.

GSEA analysis against KEGG and HALLMARK databases was conducted to investigate the specific biological pathways involved in the aging signature. We found that immune response-relevant circuits in the KEGG database (e.g., T cell receptor signaling pathway and chemokine signaling pathway) and HALLMARK database (e.g., inflammatory response, interferon-gamma response, and IL2-STAT5 signaling) were markedly enriched in the ESCA low-risk subgroup (Fig. 3B, C).

ESCA patients of low-risk group had a significantly enhanced enrichment of T cell-inflamed signature, IFNγ signature, and cytolytic activity than those patients of high-risk group (Wilcoxon test all P < 0.05; Fig. 3D, E, F). Besides, the elevated enrichment of immune cell signature, immune signaling molecules, and cytokines/chemokines signature was also observed in low-risk patients (Wilcoxon test all P < 0.05; Figure S2).

The different expression levels of complete immune checkpoints between low- and high-risk subpopulations were calculated. Results showed that the common immune checkpoints (e.g., CD274, CD276, IDO1, LAG3, and PDCD1) were highly expressed in patients with a low-risk score (all P < 0.05; Figure S3).

TMB and SMGs linked with the determined aging signature

Tumor mutation burden (TMB) was revealed to be associated with tumor prognosis and immunotherapy response [36, 37]. We thus explored the connection between the aging signature and TMB. By using the somatic mutational profile of TCGA ESCA dataset, we calculated the TMB for each ESCA patient and noticed that patients with a low-risk score harbored a markedly enhanced TMB than patients with a high-risk score (Wilcoxon test, P < 0.001; Fig. 4A).

Mutational signatures are characterized by specific combination patterns of nucleotide substitutions and have been demonstrated to associate with TMB. Hence, we detected possible mutational signatures of ESCA samples by dividing the nucleotide substitution matrix under the NMF approach. Based on the cophenetic metric plot, four mutational signatures were determined (Fig. 4B). After annotating them with signatures from the COSMIC database (Fig. 4C), we identified these four mutational signatures as signature 1 (associated with age of cancer diagnosis), signature 4 (associated with smoking), signature 13 (associated with APOBEC enzyme activity), and signature 17 (the etiology of this signature remains unknown). Distinct mutational features of the above four signatures were exhibited in Fig. 4D. Mutational signature activity of all ESCA samples was calculated and illustrated in Fig. 4E and Table S6. Subsequent investigation indicated that low-risk scores were significantly associated with the elevated mutational activity of signature 1 (P = 0.036; Figure S4A) and decreased activity of signature 17 (P = 0.049; Figure S4B). No significant differences were found between two risk groups concerning signatures 4 and 13 (P = 0.829 and 0.779, respectively; Figure S4C, D).

To elucidate whether the enhanced TMB of low-risk group was affected by other clinical confounders, we added clinical features (i.e., age, sex, stage, smoking status, and alcohol status) and determined mutational signatures (i.e., signatures 1, 4, 13, and 17) into a multivariate logistic regression model. The connection between the low-risk scores and the elevated TMB was still remained (OR: 2.38, 95% CI: 1.13–5.69, P = 0.002; Fig. 4F).

Totaling 16 significantly mutated genes (SMGs) were determined by detecting the somatic mutation data of the TCGA cohort. Waterfall plot between low- and high-risk groups (Fig. 5) demonstrated a markedly different mutation frequency in TP53 [66 of 80 (82.5%) vs. 71 of 79 (89.9%); P = 0.038], NAV3 [3 of 80 (3.8%) vs. 11 of 79 (13.9%); P = 0.025], and FAT1 [2 of 80 (2.5%) vs. 6 of 79 (7.6%); P = 0.045].

Roles of the determined aging signature in assessing ICI treatment efficacy

The aforementioned results demonstrated that the established aging risk signature was connected with immune microenvironment and immunogenicity, we hypothesized that the aging signature may play vital roles in immune checkpoint inhibitor (ICI) treatment. Hence, a urothelial cancer anti-PD-L1 dataset (Imvigor210) with both gene expression profiles and immunotherapeutic information was used to investigate the connection between determined aging signature and ICI efficacy. Results revealed that patients of low-risk subgroup had a markedly better ICI prognosis as compared with patients of high-risk subgroup (Log-rank test P = 0.002; Fig. 6A). This association was remained even controlling for sex, baseline ECOG score, smoking status, immune phenotype, and platinum treatment in a multivariate Cox regression model (HR: 0.69, 95% CI: 0.53–0.91, P = 0.007; Fig. 6B). Subsequent analysis showed that a higher proportion of better ICI response status (i.e., CR and PR) was observed in the low-risk group (28.7% vs. 16.9%, P = 0.018; Fig. 6C). Besides, in this ICI-treated cohort, we also observed patients with low-risk scores exhibited an elevated TMB (Wilcoxon test P = 0.004; Fig. 6D). A trend of higher neoantigen burden was also noticed in the low-risk subgroup, although this association did not reach statistical significance (Wilcoxon test P = 0.083; Fig. 6E). Finally, we conducted immune infiltration analysis by using transcriptomic data of this immunotherapeutic cohort to compare the distinct infiltration levels between two risk subgroups. Consistently, the enhanced infiltration of immune response lymphocytes represented by CD8 T cells was significantly enriched in patients with low-risk signature scores (Figure S5).

In this work, by using gene expression profiles and clinical data from several ESCA datasets, we constructed and validated an aging-relevant risk signature for prognosis evaluation, immunogenicity, and ICI response prediction. Future prospective studies are necessary, but findings obtained from our research suggest the potential roles of the aging risk signature in ESCA clinical prognosis and immunotherapeutic efficacy surveillance.

Mutational signatures are the manifestations of endogenous and exogenous factors that indicated specific mutational patterns [38]. Amongst, the aging-relevant mutational signature 1 was revealed to be linked with the poor tumor microenvironment and prognosis in triple-negative breast cancer [39], indicating its possible roles for immune treatment efficacy. Subsequently, a recent study integrated somatic mutational profiles of melanoma and NSCLC samples who received ICI therapy and noticed that both tumors with aging signature showed a worse survival outcome [40]. In our study, different from mutation-level biomarkers, an aging-relevant risk signature was constructed at the transcriptomic level, and we observed this signature harbors the ability to predict prognosis outcome and immunogenicity in ESCA.

Of the identified 22 aging genes, six (i.e., ANXA5, MEOX1, PCSK5, HLA-DOB, SREBF1, and KLRB1) were also demonstrated to be associated with tumor immunity. ANXA5 was associated with infiltration of antigen-presenting cells in glioma [41] and was recently identified as a novel immune checkpoint inhibitor [42]. A molecular signature contained MEOX1 was constructed to predict survival and immune immunologic status in breast cancer [43]. PCSK5 was linked with cancer alternative splicing events, which contribute to carcinogenesis and immune microenvironment in head and neck squamous cell carcinoma [44]. In ovarian cancer, an immune-related prognostic model including HLA-DOB was determined to be connected with prognosis and immunity [45]. Enhanced transcription of SREBF1 promoted invariant natural killer T cell activity, thus increasing lipid biosynthesis and inhibiting anti-tumor effect [46]. A recent study reported that loss of function of KLRB1 enhanced T cell-mediated toxicity and anti-tumor function in glioma [47]. The above evidence further confirms the potential implications of established risk signature in immune infiltration and immunotherapy efficacy.

TP53, NAV3, and FAT1 are frequently mutated in ESCA and their mutations are associated with an inferior survival outcome in ESCA and other several cancers [48–51]. In this study, the relatively lower mutation frequency of the above SMGs was enriched in the ESCA low-risk subgroup, which is consistent with the finding that patients of low-risk group exhibited a favorable prognosis. However, recent studies have also reported that TP53 mutations could predict a better efficacy in cancer immunotherapy [52], this suggests that lesser TP53 mutations in the low-risk ESCA patients contribute to the survival benefits rather than immunotherapy efficacy. Future in-depth studies are warranted.

Given the lack of ESCA datasets with both gene expression profiles and ICI treatment data, we thus used a urothelial cancer immunogenomic dataset [17], which is so far the largest immunotherapy dataset to explore the connection of the aging risk signature with ICI treatment efficacy. Results demonstrated the preferable ICI prognosis and response status (i.e., CR and PR) were presented in patients of low-risk group. Considering the cancer homogeneity in particular situations (e.g., therapy outcome assessment [40]), we conclude that the aging-relevant signature is predictive of ICI efficacy not only in urothelial cancer but also ESCA and other tumor subtypes.

Limitations exist in our study. First, the gene expression profiles of ESCA samples were acquired from publicly available datasets, which might produce deviation in the analysis procedure of different cohorts. Second, relevant results from the genomic mutational profile were calculated only based on the TCGA ESCA dataset, no additional mutational data was used for validation. Finally, experimental verification used for multiple associations is lacking.

In summary, by employing the aging gene expression data of ESCA samples, we constructed a risk prediction model to evaluate prognosis, immunogenicity, and immunotherapy response. The novel determined signature might be a possible indicator for ESCA clinical monitoring and treatment.

Acknowledgments

Not applicable.

Authors’ contributions

JY and JZ conceived this study; WD, WF, JQ, and HC collected the related data and performed main data analysis; WD, WF, JQ, and GJ conducted data analysis and interpretation; WD, WF, and JQ drafted and corrected the paper; JY and JZ supervised this study.

Funding

Not applicable.

Availability of data and materials

All genomic data and clinical information employed in this work are obtained from publicly accessible datasets.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

No potential conflict of interest was reported by the authors.

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Characterization of aging-related profiles in esophageal cancer reveals a signature predicting favorable outcome and immunogenicity

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Sample collection and aging-related genes

Development of the aging-relevant risk signature

Assessment of tumor infiltrated immunocytes and immune checkpoints

Enrichment of immunogenicity and ICI response indicators

Gene set enrichment analysis

Determination of mutational signatures in the genome

Significantly mutated genes

Statistical analysis

Results

Identification of the aging risk signature

Corroboration of the aging risk signature

The identified risk signature associated with preferable immunocyte infiltration and immune activity

TMB and SMGs linked with the determined aging signature

Roles of the determined aging signature in assessing ICI treatment efficacy

Discussion

Declarations

References

Supplementary Files

Status:

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