Gut flora metagenomic analysis coupled with metabolic and deep immune profiling in chronic kidney disease.

BACKGROUND: Perturbation of gut microbiota has been linked to chronic kidney disease (CKD), which was correlated with a sophisticated milieu of metabolic and immune dysregulation. METHODS: To clarify the underlying host-microbe interaction in CKD, we performed multi-omics measurements, including systems-level gut microbiome, targeted serum metabolome and deep immunotyping, in a cohort of patients and non-CKD controls. RESULTS: Our analyses on functional profiles of the gut microbiome showed a decrease in the diversity and abundance of carbohydrate-active enzyme (CAZyme) genes but an increase in the abundance of antibiotic resistance, nitrogen cycling enzyme and virulence factor genes in CKD. Moreover, models generated using measurements of serum metabolites (amino acids, bile acids and short-chain fatty acids) or immunotypes were predictive of renal impairment but less so than many of the functional profiles derived from gut microbiota, with the CAZyme genes being the top-performing model to accurately predict the early stage of diseases. In addition, co-occurrence analyses revealed coordinated host-microbe relationships in CKD. Specifically, the highest fractions of significant correlations were identified with circulating metabolites by several taxonomic and functional profiles of gut microbiome, while immunotype features were moderately associated with the abundance of microbiome-encoded metabolic pathways and serum levels of amino acids (e.g. B cell cluster tryptophan and B cell cluster tryptophan metabolism). CONCLUSION: Overall, our multi-omics integration revealed several signatures of systems-level gut microbiome in robust associations with host-microbe co-metabolites and renal function, which may have aetiological and diagnostic implications in CKD.

Distinct trajectories distinguish antigen-specific T cells in peanut-allergic individuals undergoing oral immunotherapy.

BACKGROUND: Despite similar clinical symptoms, peanut-allergic (PA) individuals may respond quite differently to the same therapeutic interventions. OBJECTIVE: This study aimed to determine whether inherent qualities of cell response at baseline could influence response to peanut oral immunotherapy (PnOIT). METHODS: We first performed ex vivo T-cell profiling on peanut-reactive CD154+CD137+ T (pTeff) cells from 90 challenge-confirmed PA individuals. We developed a gating strategy for unbiased assessment of the phenotypic distribution of rare pTeff cells across different memory CD4+ T-cell subsets to define patient immunotype. In longitudinal samples of 29 PA participants enrolled onto the IMPACT trial of PnOIT, we determined whether patient immunotype at baseline could influence response to PnOIT. RESULTS: Our data emphasize the heterogeneity of pTeff cell responses in PA participants with 2 mutually exclusive phenotypic entities (CCR6-CRTH2+ and CCR6+CRTH2-). Our findings lead us to propose that peanut allergy can be classified broadly into at least 2 discrete subtypes, termed immunotypes, with distinct immunologic and clinical characteristics that are based on the proportion of TH2A pTeff cells. PnOIT induced elimination of TH2A pTeff cells in the context of the IMPACT clinical trial. Only 1 PA patient with a low level of TH2A pTeff cells at baseline experienced long-lasting benefit of remission after PnOIT discontinuation. CONCLUSION: Dividing PA patients according to their individual peanut-specific T-cell profile may facilitate patient stratification in clinical settings by identifying which immunotypes might respond best to different therapies.

Assessment of biomarkers in pediatric atopic dermatitis by tape strips and skin biopsies.

BACKGROUND: The cytokine profile of atopic dermatitis (AD) depends on age, ethnicity, and disease severity. This study examined biomarkers in children with AD collected by tape strips and skin biopsies, and examined whether the levels differed with filaggrin genotype, disease severity, and food allergy. METHODS: Twenty-five children aged 2-14 years with AD were clinically examined. Skin biopsies were collected from lesional skin and tape strips were collected from lesional and non-lesional skin. We analyzed natural moisturizing factor (NMF) and 17 immune markers represented by mRNA levels in skin biopsies and protein levels in tape strips. Common filaggrin gene mutations were examined in all children. RESULTS: The cytokine profile in lesional skin was dominated by a T helper (Th) 2 response in skin biopsies, and by a general increase in innate inflammation markers (interleukin (IL)-1α, IL-1ß, IL-8, IL-18) along with TARC and CTACK in tape strips. The levels of TARC, CTACK, IL-8, IL-18 showed significant correlation with AD severity in both lesional and non-lesional tape stripped skin, while no significant correlations were observed in skin biopsy data. In tape strips from lesional and non-lesional skin, the levels of NMF and selected cytokines differed significantly between children with and without FLG mutations and food allergy. CONCLUSION: Sampling of the stratum corneum with non-invasive tape strips can be used to identify biomarkers that are associated with disease severity, food allergy and FLG mutations. Skin biopsies showed robust Th2 signature but was inferior for association analysis regarding severity.

Identification of The Immune Subtype Among Muscle-invasive Bladder Cancer Patients by Multiple Datasets.

BACKGROUND: Immunotherapies including PD-1/PD-L1 antibodies have been approved for the treatment of Muscle-invasive Bladder Cancer (MIBC) patients. However, immunotherapies could only be beneficial for about 20% MIBC patients. Thus, identification of the immune subtype is becoming increasingly important. This study aimed to explore the immune subtype by analyzing the gene expression profiles. METHODS: A total of 6 datasets including (GSE13507, GSE31684, GSE32548, GSE32894, GSE69795, and TCGA-BLCA) were downloaded. The gene expression profiles from different datasets were combined since the batch effects were removed. We performed unsupervised clustering analysis to identify the immune subtype by the combined gene expression profiles. The tumor-infiltration levels of 22 immune cells, immune scores, and tumor purity were calculated, and the survival analysis was performed to investigate the prognosis difference between immune subtypes. The enriched pathways for each immune subtype were obtained. RESULTS: We identified four novel immune subtypes (referred to S1, S2, S3, and S4) among MIBC patients. We found that S1 was enriched in immune scores had the best prognosis. In contrast, S3 was poor in immune scores and had the worst prognosis. Subtype S1, S2, S3, and S4 were enriched in immune-related pathways, extracellular matrix-related pathways, metabolism-related pathways, and cancer-related pathways, respectively. CONCLUSION: The current study suggests that the immune subtypes based on gene expression profiles could contribute to select the appropriate MIBC patient for immunotherapies.

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

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.

Defining muscle-invasive bladder cancer immunotypes by introducing tumor mutation burden, CD8+ T cells, and molecular subtypes.

Immunotherapy, especially anti-PD-1, is becoming a pillar of modern muscle-invasive bladder cancer (MIBC) treatment. However, the objective response rates (ORR) are relatively low due to the lack of precise biomarkers to select patients. Herein, the molecular subtype, tumor mutation burden (TMB), and CD8+ T cells were calculated by the gene expression and mutation profiles of MIBC patients. MIBC immunotypes were constructed using clustering analysis based on tumor mutation burden, CD8+ T cells, and molecular subtypes. Mutated genes, enriched functional KEGG pathways and GO terms, and co-expressed network-specific hub genes have been identified. We demonstrated that ORR of immunotype A patients identified by molecular subtype, CD8+ T cells, and TMB is about 36% predictable. PIK3CA, RB1, FGFR3, KMT2C, MACF1, RYR2, and EP300 are differentially mutated among three immunotypes. Pathways such as ECM-receptor interaction, PI3K-Akt signaling pathway, and TGF-beta signaling pathway are top-ranked in enrichment analysis. Low expression of ACTA2 was associated with the MIBC survival benefit. The current study constructs a model that could identify suitable MIBC patients for immunotherapy, and it is an important step forward to the personalized treatment of bladder cancers.

Patterns of immune-cell infiltration in murine models of melanoma: roles of antigen and tissue site in creating inflamed tumors.

Immune-cell infiltration is associated with improved survival in melanoma. Human melanoma metastases may be grouped into immunotypes representing patterns of immune-cell infiltration: A (sparse), B (perivascular cuffing), and C (diffuse). Immunotypes have not been defined for murine melanomas, but may provide opportunities to understand mechanism-driving immunotype differences. We performed immunohistochemistry with immune-cell enumeration, immunotyping, and vascular density scoring in genetically engineered (Braf/Pten and Braf/Pten/ß-catenin) and transplantable (B16-F1, B16-OVA, and B16-AAD) murine melanomas. The transplantable tumors were grown in subcutaneous (s.c.) or intraperitoneal (i.p.) locations. Braf/Pten and Braf/Pten/ß-catenin tumors had low immune-cell densities, defining them as Immunotype A, as did B16-F1 tumors. B16-OVA (s.c. and i.p.) and B16-AAD s.c. tumors were Immunotype B, while B16-AAD i.p. tumors were primarily Immunotype C. Interestingly, the i.p. location was characterized by higher immune-cell counts in B16-OVA tumors, with counts that trended higher for B16-F1 and B16-AAD. The i.p. location was also characterized by higher vascularity in B16-F1 and B16-AAD tumors. These findings demonstrate that spontaneously mutated neoantigens in B16 melanomas were insufficient to induce robust intratumoral immune-cell infiltrates, but instead were Immunotype A tumors. The addition of model neoantigens (OVA or AAD) to B16 enhanced infiltration, but this most often resulted in Immunotype B. We find that tumor location may be an important element in enabling Immunotype C tumors. In aggregate, these data suggest important roles both for the antigen type and for the tumor location in defining immunotypes.

Screening of tumor antigens and immunogenic cell death landscapes of prostate adenocarcinoma for exploration of mRNA vaccine.

BACKGROUND: In this study, effective antigens of mRNA vaccine were excavated from the perspective of ICD, and ICD subtypes of PRAD were further distinguished to establish an ICD landscape, thereby determining suitable vaccine recipients. RESEARCH DESIGN AND METHODS: TCGA and MSKCC databases were applied to acquire RNA-seq data and corresponding clinical data of 554 and 131 patients, respectively. GEPIA was employed to measure prognostic indices. Then, a comparison of genetic alterations was performed utilizing cBioPortal, and correlation of identified ICD antigens with immune infiltrating cells was analyzed employing TIMER. Moreover, ICD subtypes were identified by means of consensus cluster, and ICD landscape of PRAD was depicted utilizing graph learning-based dimensional reduction. RESULTS: In total, 4 PRAD antigens were identified in PRAD, including FUS, LMNB2, RNPC3, and ZNF700, which had association with adverse prognosis and infiltration of APCs. PRAD patients were classified as two ICD subtypes based on their differences in molecular, cellular, and clinical features. Furthermore, ICD modulators and immune checkpoints were also differentially expressed between two ICD subtype tumors. Finally, the ICD landscape of PRAD showed substantial heterogeneity among individual patients. CONCLUSIONS: In summary, the research may provide a theoretical foundation for developing mRNA vaccine against PRAD as well as determining appropriate vaccine recipients.

Potential tumor-specific antigens and immune landscapes identification for mRNA vaccine in thyroid cancer.

Background: Tumor mRNA vaccines have been identified as a promising technology for cancer therapy in multiple cancer types, while their efficacy in thyroid cancer (THCA) is unclear. Immunotyping is strongly associated with the immune microenvironment and immune status in cancer, thus it is important in vaccination and therapeutic response. This study is to identify potential valuable antigens and novel immune subtypes of THCA for immune landscape construction, thus screening patients suitable for mRNA vaccination. Methods: The clinical information and gene expression files of 568 THCA cases were obtained from the TCGA dataset. The DNA copy number variation and the somatic mutation of THCA were visualized by the cBioPortal database. TIMER was used to investigate the immune infiltrating correlation with candidate antigens. Consensus clustering analysis was conducted to cluster data using the ConsensusClusterPlus package. The immune landscapes of THCA patients were visualized using the Monocle package. The critical hub genes for THCA mRNA vaccines were identified by WGCNA package. To validate, the immunohistochemistry and real-time quantitative PCR (RT-qPCR) were performed to detect the expression level of potential antigen for mRNA vaccine in tissue and cell lines in THCA. Results: Thymidine kinase 1 (TK1) was identified as a potential biomarker of mRNA vaccine against THCA. It was confirmed to be significantly upregulated in THCA tissues and cells lines. Moreover, three novel immune subtypes of THCA were obtained based on the expression consistency of immune-associated genes. The S2 subtype was characterized as an immunological "cold" phenotype with a high expression of immunogenic cell death modulators. S1 and S3 subtypes were immunological "hot" phenotypes with immune checkpoints upregulation. Further, the immune landscape of THCA patients was visualized and ten hub genes for mRNA vaccines were identified. Conclusion: TK1 was a tumor-specific antigen of mRNA vaccines. The patients belonging to the S2 subtype ("cold" tumor) were suitable for mRNA vaccine therapy in THCA. Notably, ten hub genes were conducted as potential biomarkers for identifying suitable patients for mRNA vaccination. These findings provided novel insights into mRNA vaccine development against THCA.

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Identification of tumor antigens and immune subtypes of hepatocellular carcinoma for mRNA vaccine development.

BACKGROUND: mRNA vaccines have been investigated in multiple tumors, but limited studies have been conducted on their use for hepatocellular carcinoma (HCC). AIM: To identify candidate mRNA vaccine antigens for HCC and suitable subpopulations for mRNA vaccination. METHODS: Gene expression profiles and clinical information of HCC datasets were obtained from International Cancer Genome Consortium and The Cancer Genome Atlas. Genes with somatic mutations and copy number variations were identified by cBioPortal analysis. The differentially expressed genes with significant prognostic value were identified by Gene Expression Profiling Interactive Analysis 2 website analysis. The Tumor Immune Estimation Resource database was used to assess the correlation between candidate antigens and the abundance of antigen-presenting cells (APCs). Tumor-associated antigens were overexpressed in tumors and associated with prognosis, genomic alterations, and APC infiltration. A consensus cluster analysis was performed with the Consensus Cluster Plus package to identify the immune subtypes. The weighted gene coexpression network analysis (WGCNA) was used to determine the candidate biomarker molecules for appropriate populations for mRNA vaccines. RESULTS: AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 were identified as candidate HCC antigens for mRNA vaccine development. Four immune subtypes (IS1-IS4) and five immune gene modules of HCC were identified that were consistent in both patient cohorts. The immune subtypes showed distinct cellular and clinical characteristics. The IS1 and IS3 immune subtypes were immunologically "cold". The IS2 and IS4 immune subtypes were immunologically "hot", and the immune checkpoint genes and immunogenic cell death genes were upregulated in these subtypes. IS1-related modules were identified with the WGCNA algorithm. Ultimately, five hub genes (RBP4, KNG1, METTL7A, F12, and ABAT) were identified, and they might be potential biomarkers for mRNA vaccines. CONCLUSION: AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 have been identified as candidate HCC antigens for mRNA vaccine development. The IS1 and IS3 immune subtypes are suitable populations for mRNA vaccination. RBP4, KNG1, METTL7A, F12, and ABAT are potential biomarkers for mRNA vaccines.

Identification of tumor-specific neoantigens and immune clusters of hepatocellular carcinoma for mRNA vaccine development.

BACKGROUND: To screen efficacious neoantigens for the development of LIHC mRNA vaccines, construct LIHC immune clusters, and therefore select patients who might benefit from vaccination. METHODS: RNA-seq data and clinical information of 371 TCGA-LIHC and 231 ICGC-LIHC cohorts were downloaded. Differentially expressed genes and their associations with prognosis were analyzed by GEPIA, genetic alterations were examined in the cBioPortal portal, and the association between genes and immune infiltrating cells was explored by TIMER. The immune clusters were constructed by consistency clustering, and the immune landscape was described using CIBERSORT. RESULTS: POLR3C and KPNA2 were identified as LIHC tumor neoantigens related to inferior prognosis and antigen-presenting cell infiltration. In addition, three immune clusters (IC1, IC2 and IC3) with significant differences in molecular, immune cytological, and clinical features were identified in both the TCGA and ICGC LIHC cohorts. Immune "hot" phenotype IC3 displayed a better survival than IC2, and immune "cold" phenotype IC1 exhibited a high tumor mutation burden. CONCLUSION: In conclusion, for the development of anti-LIHC mRNA vaccines, we identified efficacious neoantigens POLR3C and KPNA2, profiled the tumor microenvironment of LIHC, and identified IC1 patients as the subgroup who might not most benefit from vaccination.

Identification of tumor antigens and immune subtypes of glioma for mRNA vaccine development.

Recent evidence suggested that the mRNA vaccine has been effective for many tumors, but its progress in gliomas was slow. In this study, we screened potential tumor antigens and suitable populations for mRNA vaccine to develop mRNA vaccine for glioma. We integrated the normalized RNA sequencing expression data and somatic mutation data from TCGA-GBM, TCGA-LGG, and CGGA datasets. Putative antigens in glioma were identified by selecting highly mutated genes with intimate correlation with clinical survival and immune infiltration. An unsupervised partition around medoids algorithm was utilized to stably cluster the patients into five different immune subtypes. Among them, IS1/2 was cold tumor with low tumor mutation burden (TMB), immunogenic cell death (ICDs), and immune checkpoints (ICPs), and IS4/5 was hot tumor with high TMB, ICDs, and ICPs. Monocle3 package was used to evaluate the immune status similarity and evolution in glioma, which identified cluster IS2A/2B within IS2 subtype to be more suitable vaccination receivers. Weighted gene co-expression network analysis identified five hub immune genes as the biomarkers of patients' immune status in glioma. In conclusion, NAT1, FRRS1, GTF2H2C, BRCA2, GRAP, NR5A2, ABCB4, ZNF90, ERCC6L, and ZNF813 are potential antigens suitable for glioma mRNA vaccine. IS1/2A/2B are suitable for mRNA vaccination.

Immune Gene Signatures and Immunotypes in Immune Microenvironment Are Associated With Glioma Prognose.

Glioma is the most common primary malignant brain tumor in adults with very poor prognosis. The limited new therapeutic strategies for glioma patients can be partially attributed to the complex tumor microenvironment. However, knowledge about the glioma immune microenvironment and the associated regulatory mechanisms is still lacking. In this study, we found that, different immune subtypes have a significant impact on patient survival. Glioma patients with a high immune response subtype had a shorter survival compared with patients with a low immune response subtype. Moreover, the number of B cell, T cell, NK cell, and in particular, the macrophage in the immune microenvironment of patients with a high immune response subtype were significantly enhanced. In addition, 132 genes were found to be related to glioma immunity. The functional analysis and verification of seven core genes showed that their expression levels were significantly correlated with the prognosis of glioma patients, and the results were consistent at tissue levels. These findings indicated that the glioma immune microenvironment was significantly correlated with the prognosis of glioma patients and multiple genes were involved in regulating the progression of glioma. The identified genes could be used to stratify glioma patients based on immune subgroup analysis, which may guide their clinical treatment regimen.

Development and validation of a novel immunotype for prediction of overall survival in patients with clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor. The purpose of the present study was to establish a novel immunotype for different immune infiltration and overall survival (OS) of patients with ccRCC. Methods: Based on the Cancer Genome Atlas Project (TCGA) database (discovery set), a novel immunotype was established using ssGSEA methods. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine (XHH) served as an external validation set. GSEA was carried out to identify the immunotype associated signal transduction pathways. Results: A total of 652 ccRCC patients were included in our study. We constructed a novel immunotype of ccRCC to classify patients into three groups: high-immunity, moderate-immunity, and low-immunity. The high-immunity and moderate-immunity groups had higher ImmuneScores, ESTIMATEScores, StromalScores, and lower tumor purity than that of the low-immunity group in both sets. Additionally, the patients from the high-immunity and moderate-immunity groups had longer survival than patients from low-immunity group in both discovery set and validation set (HR = 2.54, 95% CI: 1.56-4.13, p < 0.01; HR = 2.75, 95% CI: 1.24-6.11, p = 0.01). Conclusion: In summary, we defined a novel immunotype of ccRCC. The immune types could be used as a clinical predictive tool to identify ccRCC patients with different survival. In addition, the immune-related biological signaling pathway also brought new insights on the mechanism of ccRCC.

Bioinformatics analyses for the identification of tumor antigens and immune subtypes of gastric adenocarcinoma.

Background: Although mRNA vaccines have been effective against multiple cancers, their efficacy against stomach adenocarcinoma (STAD) remains undefined. 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 potential antigens in STAD for mRNA vaccine development, and further distinguish immune subtypes of STAD to construct an immune landscape for selecting suitable patients for vaccination. Methods: The gene expression and clinicopathological features of patients with gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Program (GTEx). 729 samples from GSE66229 and GSE84437 were downloaded through GEO and were used as the validation cohorts. Differential gene expression, genetic alterations and prognosis were analyzed using the R package, cBioPortal program and Kaplan-Meier. The relationship between tumor antigens and immune cells was evaluated and plotted by TIMER. ConsensusClusterPlus was used for consistency matrix construction and data clustering, and graph learning-based dimensional reduction was used to depict immune landscape. WGCNA was used to estimate the relationship between the color modules and immune subtypes. Results: Two overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in STAD, including RAI14 and NREP. The immune subtypes showed distinct molecular, cellular and clinical characteristics. IS1 and IS2 exhibited immune-activated phenotypes and correlated to better survival compared to IS3, while IS3 tumors was immunologically cold. Immunogenic cell death modulators, immune checkpoints, and CA125, and CEA were also differentially expressed among the three immune subtypes. Finally, the immune landscape of STAD showed a high degree of heterogeneity between individual patients. Conclusion: RAI14 and NREP are potential antigens for developing anti-STAD mRNA vaccine, and patients with IS1 and IS3 tumors may be suitable for vaccination.

Identification of Neoantigens and Construction of Immune Subtypes in Prostate Adenocarcinoma.

Background: Messenger ribonucleic acid (mRNA) vaccine has been considered as a potential therapeutic strategy and the next research hotspot, but their efficacy against prostate adenocarcinoma (PRAD) remains undefined. This study aimed to find potential antigens of PRAD for mRNA vaccine development and identify suitable patients for vaccination through immunophenotyping. Methods: Gene expression profiles and clinical information were obtained from TCGA and ICGC. GEPIA2 was used to calculate the prognostic index of the selected antigens. The genetic alterations were compared on cBioPortal and the correlation between potential antigen and immune infiltrating cells was explored by TIMER. ConsensusClusterPlus was used to construct a consistency matrix, and identify the immune subtypes. Graph learning-based dimensional reduction was performed to depict immune landscape. Boruta algorithm and LASSO logistic analysis were used to screen PRAD patients who may benefit from mRNA vaccine. Results: Seven potential tumor antigens selected were significantly positively associated with poor prognosis and the antigen-presenting immune cells (APCs) in PRAD, including ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1. Five immune subtypes of PRAD were identified by differential molecular, cellular, and clinical characteristics in both cohorts. C3 and C5 had immune "hot" and immunosuppressive phenotype, On the contrary, C1&C2 had immune "cold" phenotype. Finally, the immune landscape characterization showed the immune heterogeneity among patients with PRAD. Conclusions: ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1 are potential antigens for mRNA vaccine development against PRAD, and patients in type C1 and C2 are suitable for vaccination.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic and Neoadjuvant Chemotherapy Relevant Signatures.

Immune response which involves distinct immune cells is associated with prognosis of breast cancer. Nonetheless, less study have determined the associations of different types of immune cells with patient survival and treatment response. In this study, A total of 1,502 estrogen receptor(ER)-negative breast cancers from public databases were used to infer the proportions of 22 subsets of immune cells. Another 320 ER-negative breast cancer patients from Guangdong Provincial People's Hospital were also included and divided into the testing and validation cohorts. CD8+ T cells, CD4+ T cells, B cells, and M1 macrophages were associated with favourable outcome (all p <0.01), whereas Treg cells were strongly associated with poor outcome (p = 0.005). Using the LASSO model, we classified patients into the stromal immunotype A and B subgroups according to immunoscores. The 10 years OS and DFS rates were significantly higher in the immunotype A subgroup than immunotype B subgroup. Stromal immunotype was identified as an independent prognostic indicator in multivariate analysis in all cohorts and was also related to pathological complete response(pCR) after neoadjuvant chemotherapy. The nomogram that integrated the immunotype and clinicopathologic features showed good predictive accuracy for pCR and discriminatory power. The stromal immunotype A subgroup had higher expression levels of immune checkpoint molecules (PD-L1, PD-1, and CTLA-4) and cytokines (IL-2, INF-γ, and TGF-ß). In addition, patients with immunotype A and B diseases had distinct mutation signatures. Therefore, The stromal immunotypes could predict survival and responses of ER-negative breast cancer patients to neoadjuvant chemotherapy.

A Novel Immunotype-based Risk Stratification Model Predicts Postoperative Prognosis and Adjuvant TACE Benefit in Chinese Patients with Hepatocellular Carcinoma.

Background and Aims: The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8+ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC). Methods: A total of 220 eligible HCC patients participated in this study. CD8 + T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model. Results: A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of HCC, respectively. Univariate and multivariate analyses identified immunotype and PD-L1 expression mode as independent prognostic factors for overall survival time (OS) and recurrence-free survival time (RFS). The nomogram was constructed by integrating immunotype, PD-L1 expression, Barcelona Clinic Liver Cancer (BCLC) stage and tumor grade. The C-index was 0.794 in the training cohort and 0.813 in the validation cohort. A risk stratification system was constructed based on the nomogram classifying HCC patients into 3 risk groups. The average OS times in the low-risk, intermediate-risk and high-risk groups in all cohorts were 77.1 months (95% CI 71.4-82.9), 53.7 months (95% CI 48.2-59.2), and 25.6 months (95% CI 21.4-29.7), respectively. Further analysis showed that OS was significantly improved by adjuvant TACE in the low- and intermediate-risk groups (P=0.041 and P=0.010, respectively) but not in the high-risk group (P=0.398). Conclusion: A novel immunotype-based risk stratification model was built to predict postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.

Microsatellite instability in Chinese gastric cancer and its correlation with clinical characteristics.

BACKGROUND: Microsatellite instability (MSI) remains a focus of interest in cancer research, but the characteristics of MSI in gastric cancer (GC) are ambiguous. METHODS: In this retrospective study, we analyzed the prevalence of MSI and the expression of programmed death-ligand 1 (PD-L1) and cluster of differentiation 8 (CD8) cells in Chinese GC patients. A total of 393 GC patients admitted to two centers from January 2010 to December 2017 were enrolled. RESULTS: The prevalence of MSI in this cohort was 3.4% and most frequently occurred in females, patients aged between 59 and 69 years, and patients at a lower clinical stage. All MSI GCs had CD8 expression but lacked PD-L1 expression, indicating that MSI was related to CD8 expression but irrelevant to PD-L1 expression. However, there was no significant difference in the expression of CD8/PD-L1 between MSI GC and microsatellite stable (MSS) GC. Kaplan-Meier survival curves revealed that patients with MSI had a significantly longer overall survival (OS) than patients with MSS. CONCLUSIONS: In Chinese GC patients, MSI frequently occurred in females, patients aged between 59 and 69, and patients with lower clinical stages. Patients with MSI-High (MSI-H) and MSI-Low (MSI-L) had a longer OS than patients with MSS. MSI was related to CD8 expression but irrelevant to PD-L1 expression.