A metabolomic and proteomic study to elucidate the molecular mechanisms of immunotherapy resistance in patients with oesophageal squamous cell carcinoma

Lijuan Gao, Yongshun Chen, Lijuan Gao, Yongshun Chen

Abstract

Systemic chemotherapy, the standard first-line treatment option for patients with advanced oesophageal squamous cell carcinoma (OSCC), results in a median survival of ~1 year. Immune checkpoint inhibitors are a breakthrough oncology treatment option; however, most patients with advanced OSCC develop primary and acquired resistance to programmed death receptor-1 (PD-1) monoclonal antibody, severely affecting their prognosis. Therefore, there is an urgent need to investigate the molecular mechanism underlying resistance to treatment. The present study aimed to explore the mechanism of resistance to PD-1 monoclonal antibody. Plasma samples were collected from patients with OSCC treated with immunotherapy, who achieved pathological response/partial response (CR/PR) or stable disease/progressive disease (SD/PD) after the fourth treatment cycle. TM-widely targeted metabolomics, widely targeted lipidomics, and DIA proteomics assays were performed. Differential metabolites were screened based on fold change (FC) ≥1.5 or ≤0.67 and a VIP ≥1; differential proteins were screened based on FC >1.5 or <0.67 and P<0.05. The identified metabolites were annotated and mapped using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. The differential proteins were annotated to the Gene Ontology and KEGG pathway databases. A correlation network diagram was drawn using differential expressed proteins and metabolites with (Pearson correlation coefficient) r>0.80 and P<0.05. Finally, 197 and 113 differential metabolites and proteins were screened, respectively, in patients with CR/PR and SD/PD groups. The KEGG enrichment analysis revealed that all of these metabolites and proteins were enriched in cholesterol metabolism and in the NF-κB and phospholipase D signalling pathways. The present study is the first to demonstrate that PD-1 inhibitor resistance may be attributed to cholesterol metabolism or NF-κB and phospholipase D signalling pathway activation. This finding suggests that targeting these signalling pathways may be a promising novel therapeutic approach in OSCC which may improve prognosis in patients undergoing immunotherapy.

Keywords: PD-1 monoclonal antibody; immunotherapy; metabolics; oesophageal squamous cell carcinoma; proteomics.

Conflict of interest statement

The authors declare that they have no competing interests.

Copyright © 2020, Spandidos Publications.

Figures

Figure 1
Figure 1
Workflow of the present study. The plasmas of patients with oesophageal squamous cell carcinoma, who were treatment with anti-programmed death receptor-1 monoclonal antibody combined with chemotherapy after the fourth treatment cycle, were collected. The curative effect was simultaneously determined by the Responsive Evaluation Criteria in Solid Tumours 1.1 at the same period. Finally, the plasma samples were divided into a complete response/partial response group (n=15) and a stable disease/progressive disease group (n=16). The DEPs and DEMs between the two groups, which may mainly contribute to the immunotherapy resistance, were then identified by proteomic (data-independent acquisition proteomics) and metabolic (including TM-widely targeted metabolomics and widely targeted lipidomics) analysis. Furthermore, conjoint analysis of the DEPs and DEMs included Kyoto Encyclopedia of Genes and Genomes enrichment pathway analysis and correlated analysis was used to elucidate the molecular mechanisms. CR/PR, complete response/partial response; SD/PD, stable disease/progressive disease; DEPs, differentially expressed proteins; DEMs, differentially expressed metabolites; DIA, data-independent acquisition; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 2
Figure 2
Metabolic change analysis of stable disease/progressive disease vs. complete response/partial response using TM-widely targeted metabolomics. (A) The volcano plot shows the DEMs in the two groups. Each dot in the volcano map represents a metabolite, with the green dots representing downregulated differential metabolites, the red dots representing upregulated differential metabolites, and the grey dots representing detected but not significantly different metabolites. The x-coordinate represents the logarithmic value (log2FC) of the multiple of the relative content difference of a certain metabolite in the two groups of samples. The greater the absolute value of the x-coordinate is, the greater the relative content difference of the substance between the two groups of samples. Under VIP + FC (fold change) double screening conditions: The ordinate represents the VIP value, and the larger the ordinate value, the more significant the difference, and the more reliable the differentially expressed metabolites obtained by screening. (B) The top 10 up- and downregulated DEMs of the two groups. The horizontal coordinate represents the cumulative number of substances ordered according to the difference multiple from the smallest to the largest, and the vertical coordinate represents the pair value with the difference multiple as base 2. Each point represents a substance; the green points represent the top 10 downregulated substances, and the red points represent the top 10 upregulated substances. (C) The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEMs. The rich factor is the ratio of the number of DEMs in the corresponding pathway to the total number of metabolites detected by the pathway. The higher the value, the greater the enrichment degree. The abscissa represents the rich factor corresponding to each pathway; the ordinate represents the pathway name; the colour of the dots is the P-value; the redder it is, the more significant the enrichment is. The size of the dot represents the number of enriched differential metabolites. DEMs, differentially expressed metabolites; VIP, variable importance in projection.
Figure 3
Figure 3
Metabolic change analysis of stable disease/progressive disease vs. complete response/partial response using widely targeted lipidomics. (A) The Volcano plot shows the DEMs in the two groups. Each dot in the volcano map represents a metabolite, with the green dots representing downregulated differential metabolites, the red dots representing upregulated differential metabolites, and the grey dots representing detected but not significantly different metabolites. The x-coordinate represents the logarithmic value (log2FC) of the multiple of the relative content difference of a certain metabolite in the two groups of samples. The greater the absolute value of the x-coordinate is, the greater the relative content difference of the substance between the two groups of samples. Under VIP + FC (fold change) double screening conditions: The ordinate represents the VIP value, and the larger the ordinate value, the more significant the difference, and the more reliable the DEMs obtained by screening. (B) The top10 up- and downregulated DEMs of the two groups. The horizontal coordinate represents the cumulative number of substances ordered according to the difference multiple from the smallest to the largest, and the vertical coordinate represents the pair value with the difference multiple as base 2. Each point represents a substance; the green points represent the top 10 downregulated substances, and the red points represent the top 10 upregulated substances. (C) The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEMs. The rich factor is the ratio of the number of DEMs in the corresponding pathway to the total number of metabolites detected by the pathway. The higher the value, the greater the enrichment degree. The abscissa represents the rich factor corresponding to each pathway; the ordinate represents the pathway name; the colour of the dots is the P-value; the redder it is, the more significant the enrichment is. The size of the dot represents the number of enriched differential metabolites. DEMs, differentially expressed metabolites; VIP, variable importance in projection.
Figure 4
Figure 4
Protein profiling changes of stable disease/progressive disease vs. complete response/partial response using quantitative proteomics. (A) The volcano plot shows the DEPs in the two groups. The horizontal coordinate represents log2FC, the vertical coordinate represents-log10(P-value), the red and blue scatter plots represent the upregulated and downregulated proteins, respectively, and the dark grey scatter plots represent the non-significantly expressed proteins. (B) The heat map of the protein profiling of the two groups. The row represents the protein cluster, the column represents the sample cluster, and the shorter the cluster branch, the larger the similarity. (C) The top 20 GO biological processes of the DEPs. The x-coordinate represents the enrichment ratio (GeneRatio/BgRatio). The greater the enrichment ratio, the greater the degree of enrichment of the differential protein. The y-coordinate represents the enriched GO items. The change of dot colour from blue to red represents the change in the P-value from large to small. The smaller the P-value, the more statistically significant it is. The size of the dot represents the number of different proteins annotated by the corresponding item. (D) The top 20 KEGG pathways of the DEPs. The x-coordinate represents the enrichment ratio (GeneRatio/BgRatio). The larger the enrichment ratio is, the higher the enrichment degree of differential protein is. The y-coordinate represents the enriched KEGG pathway. The change of dot colour from blue to red represents the change of the P-value from large to small. The smaller the P-value is, the more statistically significant it is. The size of the dot represents the number of different proteins in the corresponding functional annotation. DEPs, differentially expressed proteins; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SD-PD, stable disease-progressive disease; CR-PR, complete response-partial response.
Figure 5
Figure 5
Conjoint analyses of the metabolics and proteomics data. (A and B) The bubble diagram shows the co-enrichment KEGG pathways of the diferentially expressed metabolites and the differentially expressed proteins of the (A) widely targeted lipidomics and (B) TM-widely targeted metabolomics. The x-coordinate represents the enrichment factors (Diff/Background) of the pathway in different omics, and the y-coordinate represents the names of KEGG pathways. The gradient of red, yellow and green represents the change of enrichment significance from high to medium to low, which is represented by the P-value. The shape of the bubble represents different omics, and the size of the bubble represents the number of different metabolites or proteins, and the larger the number, the larger the dot. KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 6
Figure 6
Enrichment of DEMs and DEPs in the cholesterol metabolism, NF-kappaB and phospholipase D signaling pathways. (A) Enrichment of DEMs and DEPs in the cholesterol metabolism signaling pathway. (B) Enrichment of DEMs and DEPs in the NF-kappaB signaling pathway. (C) Enrichment of DEMs and DEPs in the phospholipase D signaling pathway. (D) The correlation network of the DEMs and DEPs in the cholesterol metabolism signaling pathway. Blue circles represent metabolites, red circles represent proteins, red lines represent positive correlations, and blue lines represent negative correlations. (E) The correlation network of the DEMs and DEPs in the bile secretion signaling pathway. Blue circles represent metabolites, red circles represent proteins, red lines represent positive correlations, and blue lines represent negative correlations. (F) The cholesterol metabolism pathway; the upregulated metabolites are indicated in red. DEMs, differentially expressed metabolites; DEPs, differentially expressed proteins; SD-PD, stable disease-progressive disease; CR-PR, complete response-partial response; APOH, apolipoprotein H.

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