Identification of tumor antigens and immune subtypes of pancreatic adenocarcinoma for mRNA vaccine development

Abstract

Background: Although mRNA vaccines have been effective against multiple cancers, their efficacy against pancreatic adenocarcinoma (PAAD) remains undefined. Accumulating evidence suggests that immunotyping can indicate the comprehensive immune status in tumors and their immune microenvironment, which is closely associated with therapeutic response and vaccination potential. The aim of this study was to identify potent antigens in PAAD for mRNA vaccine development, and further distinguish immune subtypes of PAAD to construct an immune landscape for selecting suitable patients for vaccination.

Methods: Gene expression profiles and clinical information of 239 PAAD datasets were extracted from ICGC, and RNA-Seq data of 103 samples were retrieved from TCGA. GEPIA was used to calculate differential expression levels and prognostic indices, cBioPortal program was used to compare genetic alterations, and TIMER was used to explore correlation between genes and immune infiltrating cells. Consensus cluster was used for consistency matrix construction and data clustering, DAVID was used for functional annotation, and graph learning-based dimensional reduction was used to depict immune landscape.

Results: Six overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in PAAD, including ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A. Furthermore, five immune subtypes (IS1-IS5) and nine immune gene modules of PAAD were identified that were consistent in both patient cohorts. The immune subtypes showed distinct molecular, cellular and clinical characteristics. IS1 and IS2 exhibited immune-activated phenotypes and correlated to better survival compared to the other subtypes. IS4 and IS5 tumors were immunologically cold and associated with higher tumor mutation burden. Immunogenic cell death modulators, immune checkpoints, and CA125 and CA199, were also differentially expressed among the five immune subtypes. Finally, the immune landscape of PAAD showed a high degree of heterogeneity between individual patients.

Conclusions: ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A are potent antigens for developing anti-PAAD mRNA vaccine, and patients with IS4 and IS5 tumors are suitable for vaccination.

Keywords: Immune landscape; Immunotype; Pancreatic adenocarcinoma; Tumor immune microenvironment; mRNA vaccine.

Fig. 1

Identification of potential tumor antigens of PAAD. a Identification of potential tumor-associated antigens of PAAD. Chromosomal distribution of up- and down-regulated genes in PAAD as indicated. b-e Identification of potential tumor-specific antigens of PAAD. Samples overlapping in (b) altered genome fraction and (c) mutation count groups. Genes with highest frequency in (d) altered genome fraction and (e) mutation count groups

Fig. 2

Identification of tumor antigens associated with PAAD prognosis. a) Potential tumor antigens (total 926) with high expression and mutation in PAAD, and significant association with OS and RFS (total 7 candidates). b-g Kaplan-Meier curves showing OS of PAAD patients stratified on the basis of (b) ADAM9, (c) EFNB2, (d) MET, (e) TMOD3, (f) TPX2 and (g) WNT7A expression levels

Fig. 3

Identification of tumor antigens associated with APCs. Correlation between the expression levels of a ADAM9, b EFNB2, c MET, d TMOD3, e TPX2 and ) WNT7A and infiltration of macrophages, dendritic cells and B cells in PAAD tumors

Fig. 4

Identification of potential immune subtypes of PAAD. a Cumulative distribution function curve and (b) delta area of immune-related genes in ICGC cohort. c Sample clustering heat map. d Kaplan-Meier curves showing OS of PAAD immune subtypes in ICGC cohort. (e, f) Distribution of IS1-IS5 across PAAD (E) stages and (f) grades in ICGC cohort. g Kaplan-Meier curves showing OS of PAAD immune subtypes in TCGA cohort. h, i Distribution ratio of IS1-IS5 across PAAD (h) stages and (i) grades in TCGA cohort

Fig. 5

Association between immune subtypes and TMB and mutation. a TMB and b mutation number in PAAD IS1-IS5. c Eleven highly mutated genes in PAAD immune subtypes. * p < 0.05 and ** p < 0.01

Fig. 6

Association between immune subtypes and ICPs and ICD modulators. a, b Differential expression of ICP genes among the PAAD immune subtypes in (a) ICGC and (b) TCGA cohorts. c, d Differential expression of ICD modulator genes among the PAAD immune subtypes in (c) ICGC and (d) TCGA cohorts. * p < 0.01, ** p < 0.001, *** p < 0.0001, and ****p < 0.00001

Fig. 7

Association between immune subtypes and CA199 and CA125. a, b CA199 (a) and CA125 (b) expression in PAAD immune subtypes in ICGC cohorts. c, d CA199 (a) and CA125 (b) expression in PAAD immune subtypes in TCGA cohorts

Fig. 8

Cellular and molecular characteristics of immune subtypes. a, c Differential enrichment scores of 28 immune cell signatures among PAAD immune subtypes in (a) ICGC and (c) TCGA cohorts. b, d Differential enrichment scores of 7 prognostically relevant immune cell signatures in (b) ICGC and (d) TCGA cohorts. e Overlap of PAAD immune subtypes with 6 pan-cancer immune subtypes. f Differential enrichment scores of 56 immune signatures among PAAD immune subtypes and 22 immune signatures with FDR < 0.01

Fig. 9

Immune landscape of PAAD. a Immune landscape of PAAD. each point represents a patient and the immune subtypes are color-coded. The horizontal axis represents the first principal component and the vertical axis represents the second principal component. b Heat map of two principal components with 28 immune cell signatures. c Immune landscape of the subsets of PAAD immune subtypes. d Differential enrichment scores of 28 immune cell signatures in the above subsets. e Immune landscape of samples from three extreme locations and (f) their prognostic status

Fig. 10

Identification of immune gene co-expression modules of PAAD. a Sample clustering. b Scale-free fit index for various soft-thresholding powers (β). c Mean connectivity for various soft-thresholding powers (d) Dendrogram of all differentially expressed genes clustered based on a dissimilarity measure (1-TOM). e Gene numbers in each module. f Differential distribution of feature vectors of each module in PAAD subtypes

Fig. 11

Identification of immune hub genes of PAAD. a Forest maps of single factor survival analysis of 11 modules of PAAD. b Dot plot showing top 10 KEGG terms in the blue module. The dot size and color intensity represent the gene count and enrichment level respectively. c Correlation between blue module feature vector and second principal component in immune landscape. d Dot plot showing enriched terms in green module. e Correlation between green module feature vector and second principal component in immune landscape. f Differential prognosis in blue module with high and low mean. g Differential prognosis in green module with high and low mean