Germline genetic contribution to the immune landscape of cancer.

Understanding the contribution of the host's genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-ß-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer.

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-ß signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.

CD74 is associated with inflamed tumor immune microenvironment and predicts responsiveness to PD-1/CTLA-4 bispecific antibody in patients with solid tumors.

INTRODUCTION: Cadonilimab (AK104) is a first-in-class tetravalent bispecific antibody that targets both PD-1 and CTLA-4, showing a manageable safety profile and favorable clinical benefits. This study aimed to identify the biomarkers of clinical response and explore the immune response within the tumor microenvironment upon the AK104 therapy in advanced solid tumors. MATERIAL AND METHODS: Gene expression profiles of paired pre- and post-treatment tumor tissues from twenty-one patients were analyzed. The association of gene expression levels with either clinical efficacy or prognosis was evaluated and subsequently validated with published datasets using log-rank for Kaplan-Meier estimates. Comparative immune profile analyses of tumor microenvironment before and after AK104 treatment were conducted. The visualization of tumor-infiltrating lymphocytes was performed using multiplex immunohistochemistry. The predictive value of CD74 was further validated with protein expression by immunohistochemistry. RESULTS: Baseline CD74 gene expression was associated with favorable patient outcomes (overall survival [OS], HR = 0.33, 95% CI 0.11-1.03, p = 0.0463), which was further confirmed with the published datasets. Tumors with high CD74 gene expression at baseline were more likely to exhibit an immune-inflamed microenvironment. AK104 efficiently enhanced the infiltration of immune cells in the tumor microenvironment. Additionally, high CD74 protein expression (≥ 10% of the tumor area occupied by CD74 stained immune cells) at baseline was associated with better progressive-free survival (HR = 0.21, 95% CI 0.06-0.68, p = 0.0065) and OS (HR = 0.35, 95% CI 0.12-1.08, p = 0.0615). CONCLUSIONS: Our findings demonstrate that CD74 is a promising predictive biomarker for AK104 therapeutic response in advanced solid tumors. Trial registration number NCT03261011.

Identification of novel immune subtypes and potential hub genes of patients with psoriasis.

BACKGROUND: Psoriasis is a common, chronic and relapsing immune-related inflammatory dermal disease. Patients with psoriasis suffering from the recurrences is mainly caused by immune response disorder. Thus, our study is aimed to identify novel immune subtypes and select targeted drugs for the precision therapy in different subtypes of psoriasis. METHODS: Differentially expressed genes of psoriasis were identified from the Gene Expression Omnibus database. Functional and disease enrichment were performed by Gene Set Enrichment Analysis and Disease Ontology Semantic and Enrichment analysis. Hub genes of psoriasis were selected from protein-protein interaction networks using Metascape database. The expression of hub genes was validated in human psoriasis samples by RT-qPCR and immunohistochemistry. Further, novel immune subtypes of psoriasis were identified by ConsensusClusterPlus package and its association with hub genes were calculated. Immune infiltration analysis was performed, and its candidate drugs were evaluated by Connectivity Map analysis. RESULTS: 182 differentially expressed genes of psoriasis were identified from GSE14905 cohort, in which 99 genes were significantly up-regulated and 83 genes were down-regulated. We then conducted functional and disease enrichment in up-regulated genes of psoriasis. Five potential hub genes of psoriasis were obtained, including SOD2, PGD, PPIF, GYS1 and AHCY. The high expression of hub genes was validated in human psoriasis samples. Notably, two novel immune subtypes of psoriasis were determined and defined as C1 and C2. Bioinformatic analysis showed C1 and C2 had different enrichment in immune cells. Further, candidate drugs and mechanism of action that applicable to different subtypes were evaluated. CONCLUSIONS: Our study identified two novel immune subtypes and five potential hub genes of psoriasis. These findings might give insight into the pathogenesis of psoriasis and provide effective immunotherapy regimens for the precise treatment of psoriasis.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment.

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

Identification of tumour antigens and immune subtypes in the development of an anti-cancer vaccine for endometrial carcinoma.

Therapeutic application of vaccines to endometrial carcinoma (EC) remains uncertain. In this study, we aimed to identify potential tumour antigens for use in the development of an anti-tumour mRNA vaccine and clarify immune subtypes and their characteristics for immunotherapeutic application in heterogeneous EC by integrating multi-omics data. Importantly, four potential tumour antigen candidates-PGR, RBPJ, PARVG and MSX1-were identified and significantly correlated with better overall survival, disease-free survival and distinct antigen-presenting cell infiltration in EC. In addition, two different immune subtypes by consensus clustering analysis of the immune-related genes were identified. Patients with C2 immunophenotypes exhibited superior survival outcomes and 'hot' immunoreactivity and harboured higher microsatellite instability scores and tumoral mutation burden but lower copy-number variation burden. Furthermore, the distinct expression of immunogenic cell death modulators and differential microenvironmental characteristics of immune-cell infiltration were also revealed between C1 and C2 immune-subtype tumours. Enrichment analysis of the co-expression of immune-related genes showed enrichment in immune response, immune cell-mediated immunity and antigen processing pathways. These results indicated that these identified tumour antigens can be used for developing antitumour mRNA vaccines, and tumours with C2 immunophenotypic characteristics demonstrated sensitivity and susceptibility to immunotherapy in EC.

Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy.

BACKGROUND: Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries. OBJECTIVE: Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity. METHODS: Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis. RESULTS: Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity. CONCLUSION: Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.

Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities.

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

Identification and Analysis of Immune Microenvironment-Related Genes for Keloid Risk Prediction and Their Effects on Keloid Proliferation and Migration.

Keloid is a kind of proliferative scar with continuous growth, no restriction and easy recurrence, which cannot be cured and bring serious physical injury and psychological burden to patients. The main reason is that the pathological mechanism is not clear. Therefore, this project is expected to reveal the immune microenvironment-related genes and their functions in keloid progression, and provide effective targets for the treatment of keloid. Firstly, 8 kinds of immune infiltrating cells and 19 potential characteristic genes were identified by immune infiltration analysis, ssGSEA, LASSO regression (glmnet algorithm and lars algorithm) and WGCNA, indicating that keloid was closely related to the changes of immune microenvironment. Then, 4 pathological biomarkers of keloid (MAPK1, PTPRC, STAT3 and IL1R1) were identified by differentially analysis, univariate analysis, LASSO regression (lars algorithm), support vector machine recursive feature elimination (SVM-REF) algorithm, multivariate logical regression analysis and six machine learning algorithms. Based on the 4 feature genes, the risk prediction model and nomogram were constructed. Calibration curve and ROC analysis (AUC = 0.930) showed that the model had reliable clinical value. Subsequently, consistent cluster analysis was used to find that there were 2 immune microenvironment subsets in keloid patients, of which subgroup II was immune subgroup. Multiple independent datasets and RT-qPCR showed that the expression trend of the 4 genes was consistent with the analysis. Cell gain-loss experiment confirmed that 4 genes regulated the proliferation and migration of keloid cells. The above data shows that MAPK1, PTPRC, STAT3 and IL1R1 may be personalized therapeutic targets for keloid patients.

A pan-cancer analysis of collagen VI family on prognosis, tumor microenvironment, and its potential therapeutic effect.

BACKGROUND: Collagen VI family (COL6A) is a major member of extracellular matrix protein. There is accumulating evidence that COL6A is involved in tumorigenesis and tumor progression. In this study, we performed a systematic analysis of COL6A in pan-cancer based on their molecular features and clinical significance. METHODS: Based on updated public databases, we integrated several bioinformatics analysis methods to investigate the expression levels of COL6A as well as the relationship between their expression and patient survival, immune subtypes, tumor microenvironment, stemness scores, drug sensitivity, and DNA methylation. RESULTS: The expression levels of COL6A members varied in different cancers, suggesting their expression was cancer-dependent. Among COL6A members, COL6A1/2/3 were predicted poor prognosis in specific cancers. Furthermore, COL6A1/2/3 expression levels revealed a clear correlation with immune subtypes, and COL6A1/2/3 were associated with tumor purity, that is, gene expression levels were generally higher in tumors with higher stromal scores and immune scores. COL6A1/2/3 had a significantly negative correlation with RNA stemness scores, and meanwhile they were also related to DNA stemness scores in different degrees. In addition, the expression of COL6A1/2/3 was significantly related to drug sensitivity of cancer cells. Finally, our study revealed that COL6A1/2/3 expression was mainly negatively correlated with gene methylation, and the methylation levels showed remarkable differences in various cancers. CONCLUSIONS: These findings highlight both the similarities and differences in the molecular characteristics of COL6A members in pan-cancer, and provide comprehensive insights for further investigation into the mechanism of COL6A.

A multi-ethnic analysis of immune-related gene expression signatures in patients with ovarian clear cell carcinoma.

Little is known about the immune environment of ovarian clear cell carcinoma (OCCC) and its impact on various ethnic backgrounds. The aim of this OCCC immune-related gene expression signatures (irGES) study was to address the interaction between tumour and immune environment of ethnically-diverse Asian and Caucasian populations and to identify relevant molecular subsets of biological and clinical importance. Our study included 264 women from three different countries (Singapore, Japan, and the UK) and identified four novel immune subtypes (PD1-high, CTLA4-high, antigen-presentation, and pro-angiogenic subtype) with differentially expressed pathways, and gene ontologies using the NanoString nCounter PanCancer Immune Profiling Panel. The PD1-high and CTLA4-high subtypes demonstrated significantly higher PD1, PDL1, and CTLA4 expression, and were associated with poorer clinical outcomes. Mismatch repair (MMR) protein expression, assessed by immunohistochemistry, revealed that about 5% of OCCCs had deficient MMR expression. The prevalence was similar across the three countries and appeared to cluster in the CTLA4-high subtype. Our results suggest that OCCC from women of Asian and Caucasian descent shares significant clinical and molecular similarities. To our knowledge, our study is the first study to include both Asian and Caucasian women with OCCC and helps to shine light on the impact of ethnic differences on the immune microenvironment of OCCC. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Translational genomics and recent advances in oral squamous cell carcinoma.

Oral squamous cell carcinomas (OSCC) are a heterogeneous group of cancers arising from the mucosal lining of the oral cavity. A majority of these cancers are associated with lifestyle risk habits including smoking, excessive alcohol consumption and betel quid chewing. Cetuximab, targeting the epidermal growth factor receptor was approved for the treatment of OSCC in 2006, and remains the only molecular targeted therapy available for OSCC. Here, we reviewed the current findings from genomic analyses of OSCC and discuss how these studies inform on the biological mechanisms underlying OSCC. Exome sequencing revealed that the significantly mutated genes are mainly tumour suppressors. Mutations in FAT1, CASP8, CDKN2A, and NOTCH1 are more frequently found in OSCC when compared to non-OSCC head and neck cancers and other squamous cell carcinomas, and HRAS and PIK3CA are the only significantly mutated oncogenes. The distribution of these mutations also differs in populations with distinct risk habits. Gene expression-based molecular classification showed that OSCC can be divided into distinct subtypes and these have a preferential response to different types of therapies, suggesting that these classifications could have clinical implications. More recently, with the approval of checkpoint inhibitors for the treatment of cancers including OSCC, genomics studies also dissected the genetic signatures of the immune compartment to delineate immune-active and -exhausted subtypes that could inform on the immune status of OSCC patients and guide the development of novel therapies to improve response to immunotherapy. Taken together, genomics studies are informing on the biology of both the epithelial and stromal compartments underlying OSCC development, and we discuss the opportunities and challenges in using these to derive clinical benefit for OSCC patients.

Tumor-antigens and immune landscapes identification for prostate adenocarcinoma mRNA vaccine.

Prostate adenocarcinoma (PRAD) is a leading cause of death among men. Messenger ribonucleic acid (mRNA) vaccine presents an attractive approach to achieve satisfactory outcomes; however, tumor antigen screening and vaccination candidates show a bottleneck in this field. We aimed to investigate the tumor antigens for mRNA vaccine development and immune subtypes for choosing appropriate patients for vaccination. We identified eight overexpressed and mutated tumor antigens with poor prognostic value of PRAD, including KLHL17, CPT1B, IQGAP3, LIME1, YJEFN3, KIAA1529, MSH5 and CELSR3. The correlation of those genes with antigen-presenting immune cells were assessed. We further identified three immune subtypes of PRAD (PRAD immune subtype [PIS] 1-3) with distinct clinical, molecular, and cellular characteristics. PIS1 showed better survival and immune cell infiltration, nevertheless, PIS2 and PIS3 showed cold tumor features with poorer prognosis and higher tumor genomic instability. Moreover, these immune subtypes presented distinguished association with immune checkpoints, immunogenic cell death modulators, and prognostic factors of PRAD. Furthermore, immune landscape characterization unraveled the immune heterogeneity among patients with PRAD. To summarize, our study suggests KLHL17, CPT1B, IQGAP3, LIME1, YJEFN3, KIAA1529, MSH5 and CELSR3 are potential antigens for PRAD mRNA vaccine development, and patients in the PIS2 and PIS3 groups are more suitable for vaccination.

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

BACKGROUND: The mRNA-based cancer vaccine has been considered a promising strategy and the next hotspot in cancer immunotherapy. However, its application on cholangiocarcinoma remains largely uncharacterized. This study aimed to identify potential antigens of cholangiocarcinoma for development of anti-cholangiocarcinoma mRNA vaccine, and determine immune subtypes of cholangiocarcinoma for selection of suitable patients from an extremely heterogeneous population. METHODS: Gene expression profiles and corresponding clinical information were collected from GEO and TCGA, respectively. cBioPortal was used to visualize and compare genetic alterations. GEPIA2 was used to calculate the prognostic index of the selected antigens. TIMER was used to visualize the correlation between the infiltration of antigen-presenting cells and the expression of the identified antigens. Consensus clustering analysis was performed to identify the immune subtypes. Graph learning-based dimensionality reduction analysis was conducted to visualize the immune landscape of cholangiocarcinoma. RESULTS: Three tumor antigens, such as CD247, FCGR1A, and TRRAP, correlated with superior prognoses and infiltration of antigen-presenting cells were identified in cholangiocarcinoma. Cholangiocarcinoma patients were stratified into two immune subtypes characterized by differential molecular, cellular and clinical features. Patients with the IS1 tumor had immune "hot" and immunosuppressive phenotype, whereas those with the IS2 tumor had immune "cold" phenotype. Interestingly, patients with the IS2 tumor had a superior survival than those with the IS1 tumor. Furthermore, distinct expression of immune checkpoints and immunogenic cell death modulators was observed between different immune subtype tumors. Finally, the immune landscape of cholangiocarcinoma revealed immune cell components in individual patient. CONCLUSIONS: CD247, FCGR1A, and TRRAP are potential antigens for mRNA vaccine development against cholangiocarcinoma, specifically for patients with IS2 tumors. Therefore, this study provides a theoretical basis for the anti-cholangiocarcinoma mRNA vaccine and defines suitable patients for vaccination.

Tumor antigens and immune subtypes guided mRNA vaccine development for kidney renal clear cell carcinoma.

Current treatment strategy for kidney renal clear cell carcinoma (KIRC) is limited. Tumor-associated antigens, especially neoantigen-based personalized mRNA vaccines represent new strategies and manifest clinical benefits in solid tumors, but only a small proportion of patients could benefit from them, which prompts us to identify effective antigens and suitable populations to facilitate mRNA vaccines application in cancer therapy. Through performing expression, mutation, survival and correlation analyses in TCGA-KIRC dataset, we identified four genes including DNA topoisomerase II alpha (TOP2A), neutrophil cytosol factor 4 (NCF4), formin-like protein 1 (FMNL1) and docking protein 3 (DOK3) as potential KIRC-specific neoantigen candidates. These four genes were upregulated, mutated and positively associated with survival and antigen-presenting cells in TCGA-KIRC. Furthermore, we identified two immune subtypes, named renal cell carcinoma immune subtype 1 (RIS1) and RIS2, of KIRC. Distinct clinical, molecular and immune-related signatures were observed between RIS1 and RIS2. Patients of RIS2 had better survival outcomes than those of RIS1. Further comprehensive immune-related analyses indicated that RIS1 is immunologically "hot" and represent an immunosuppressive phenotype, whereas RIS2 represents an immunologically "cold" phenotype. RIS1 and RIS2 also showed differential features with regard to tumor infiltrating immune cells and immune checkpoint-related genes. Moreover, the immune landscape construction identified the immune cell components of each KIRC patient, predicted their survival outcomes, and assisted the development of personalized mRNA vaccines. In summary, our study identified TOP2A, NCF4, FMNL1 and DOK3 as potential effective neoantigens for KIRC mRNA vaccine development, and patients with RIS2 tumor might benefit more from mRNA vaccination.

Genomic stratification based on microenvironment immune types and PD-L1 for tailoring therapeutic strategies in bladder cancer.

BACKGROUND: The tumour microenvironment (TME) not only plays a role during tumour progression and metastasis but also profoundly influences treatment efficacy. Environment-mediated drug resistance is a result of crosstalk between tumour cells and stroma. The presence of a "stromal exhaustion" response is suggested by the T cell exhaustion signature and PD-L1 expression. The prognostic role of PD-L1 in bladder cancer has been investigated in previous studies, but the results remain inconclusive. For a more comprehensive study, we discuss potential strategies to improve effectiveness in patients with various TME statuses and PD-L1 expression levels. METHODS: We estimated the prognostic role of PD-L1 using immunohistochemistry and identified four immune subtypes according to the type of stromal immune modulation and PD-L1 expression status. RESULTS: Patients in the PD-L1-low-exhausted group had the worst prognosis and showed the worst antigen-presenting cell (APC) immunosuppression status. The PD-L1-low-exhausted group showed the highest amount of infiltration by macrophage M2 cells, naïve B cells and resting mast cells. The TMB and the effectiveness of anti-PD-1 treatment were significantly increased in the PD-L1-high expression groups compared with the PD-L1-low expression groups. In the PD-L1-high groups, patients who underwent chemotherapy exhibited better overall survival rates than patients who did not undergo chemotherapy, whereas there was no significant difference in the PD-L1-low groups. We performed gene set enrichment analysis (GSEA) to explore the critical pathways that were active in the PD-L1-low-exhausted group, including the myogenesis, epithelial-mesenchymal transition and adipogenesis pathways. Copy number variations (CNVs) were related to the expression levels of differentially expressed genes upregulated in the PD-L1-low-exhausted group, including LCNL1, FBP1 and RASL11B. In addition, RASL11B played a role in predicting overall survival according to The Cancer Genome Atlas data, and this finding was validated in the PD-L1-low-exhausted group in the Gene Expression Omnibus database (GEO). CONCLUSION: The immune environment of tumours plays an important role in the therapeutic response rate, and defining the immune groups plays a critical role in predicting disease outcome and strategy effectiveness.

Immune gene expression profiling reveals heterogeneity in luminal breast tumors.

BACKGROUND: Heterogeneity of immune gene expression patterns of luminal breast cancer (BC), which is clinically heterogeneous and overall considered as low immunogenic, has not been well studied especially in non-European populations. Here, we aimed at characterizing the immune gene expression profile of luminal BC in an Asian population and associating it with patient characteristics and tumor genomic features. METHODS: We performed immune gene expression profiling of tumor and adjacent normal tissue in 92 luminal BC patients from Hong Kong using RNA-sequencing data and used unsupervised consensus clustering to stratify tumors. We then used luminal patients from The Cancer Genome Atlas (TCGA, N = 564) and a Korean breast cancer study (KBC, N = 112) as replication datasets. RESULTS: Based on the expression of 130 immune-related genes, luminal tumors were stratified into three distinct immune subtypes. Tumors in one subtype showed higher level of tumor-infiltrating lymphocytes (TILs), characterized by T cell gene activation, higher expression of immune checkpoint genes, higher nonsynonymous mutation burden, and higher APOBEC-signature mutations, compared with other luminal tumors. The high-TIL subtype was also associated with lower ESR1/ESR2 expression ratio and increasing body mass index. The comparison of the immune profile in tumor and matched normal tissue suggested a tumor-derived activation of specific immune responses, which was only seen in high-TIL patients. Tumors in a second subtype were characterized by increased expression of interferon-stimulated genes and enrichment for TP53 somatic mutations. The presence of three immune subtypes within luminal BC was replicated in TCGA and KBC, although the pattern was more similar in Asian populations. The germline APOBEC3B deletion polymorphism, which is prevalent in East Asian populations and was previously linked to immune activation, was not associated with immune subtypes in our study. This result does not support the hypothesis that the germline APOBEC3B deletion polymorphism is the driving force for immune activation in breast tumors in Asian populations. CONCLUSION: Our findings suggest that immune gene expression and associated genomic features could be useful to further stratify luminal BC beyond the current luminal A/B classification and a subset of luminal BC patients may benefit from checkpoint immunotherapy, at least in Asian populations.

Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer.

PURPOSE: There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo-/targeted-therapy responsiveness. However, it is unclear whether these signatures correlate with each other or represent distinct immune-related signals. METHODS: We evaluated 57 published immune-related expression signatures in four public breast cancer datasets totaling 3295 samples. For each dataset, we used consensus clustering to group signatures together based on their co-expression pattern. Signatures that were in the same consensus cluster across all four datasets were used to define immune modules. Tumors were then classified into immune subtypes based on their module scores using consensus clustering. Survival analysis was conducted using Cox proportional hazards modeling. RESULTS: Consensus clustering consistently yields four distinct co-expression modules across the datasets. These modules appear to represent distinct immune components and signals, where constituent signatures relate to (1) T-cells and/or B-cells (T/B-cell), (2) interferon (IFN), (3) transforming growth factor beta (TGFB), and (4) core-serum response, dendritic cells, and/or macrophages (CSR). Subtyping of tumors based on these co-expression modules consistently yields subsets that fall into five major immune subtypes: T/B-cell/IFN High, IFN/CSR High, CSR High, TGFB High, and Immune Low. Basal and/or triple-negative breast cancer patients with CSR High tumors have significantly worse outcome relative to those within the T/B-cell/IFN High subtype. CONCLUSION: Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from published immune signatures. Using these modules, we identified five immune subtypes with significant outcome differences in basal breast cancers.