Nonfollicular reactivation of bone marrow resident memory CD4 T cells in immune clusters of the bone marrow.

The bone marrow maintains memory CD4 T cells, which provide memory to systemic antigens. Here we demonstrate that memory CD4 T cells are reactivated by antigen in the bone marrow. In a secondary immune response, antigen-specific T cells of the bone marrow mobilize and aggregate in immune clusters together with MHC class II-expressing cells, mostly B lymphocytes. They proliferate vigorously and express effector cytokines, but they do not develop into follicular T-helper cells. Neither do the B lymphocytes develop into germinal center B cells in the bone marrow. Within 10 days, the immune clusters disappear again. Within 30 days, the expanded antigen-specific memory CD4 T cells return to memory niches and are maintained again individually as resting cells. Thus, in secondary immune responses in the bone marrow T-cell memory is amplified, while in germinal center reactions of secondary lymphoid organs humoral memory is adapted by affinity maturation.

Identification of tumor antigens and immune landscapes for bladder urothelial carcinoma mRNA vaccine.

Background: Bladder urothelial carcinoma (BLCA) is associated with high mortality and recurrence. Although mRNA-based vaccines are promising treatment strategies for combating multiple solid cancers, their efficacy against BLCA remains unclear. We aimed to identify potential effective antigens of BLCA for the development of mRNA-based vaccines and screen for immune clusters to select appropriate candidates for vaccination. Methods: Gene expression microarray data and clinical information were retrieved from The Cancer Genome Atlas and GSE32894, respectively. The mRNA splicing patterns were obtained from the SpliceSeq portal. The cBioPortal for Cancer Genomics was used to visualize genetic alteration profiles. Furthermore, nonsense-mediated mRNA decay (NMD) analysis, correlation analysis, consensus clustering analysis, immune cell infiltration analysis, and weighted co-expression network analysis were conducted. Results: Six upregulated and mutated tumor antigens related to NMD, and infiltration of APCs were identified in patients with BLCA, including HP1BP3, OSBPL9, SSH3, ZCCHC8, FANCI, and EIF4A2. The patients were subdivided into two immune clusters (IC1 and IC2) with distinct clinical, cellular and molecular features. Patients in IC1 represented immunologically 'hot' phenotypes, whereas those in IC2 represented immunologically 'cold' phenotypes. Moreover, the survival rate was better in IC2 than in IC1, and the immune landscape of BLCA indicated significant inter-patient heterogeneity. Finally, CALD1, TGFB3, and ANXA6 were identified as key genes of BLCA through WGCNA analysis, and their mRNA expression levels were measured using qRT-PCR. Conclusion: HP1BP3, OSBPL9, SSH3, ZCCHC8, FANCI, and EIF4A2 were identified as potential antigens for developing mRNA-based vaccines against BLCA, and patients in IC2 might benefit more from vaccination.

Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma.

Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (clusters A, B, C, and D) by bioinformatics methods in TCGA-LGG and two CGGA datasets. Cluster A was an "immune-cold" phenotype with the lowest immune infiltration and longest survival expectation, whereas cluster D was an "immune-rich" subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with immune infiltration degrees among the clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX, and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine, and immune checkpoint PD-1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine-cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules (CAMs), and ECM-receptor interaction were also enriched in cluster D. Hub genes were selected by the Matthews correlation coefficient (MCC) algorithm in the blue module of a gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG.

Integrated proteogenomic characterization of urothelial carcinoma of the bladder.

BACKGROUND: Urothelial carcinoma (UC) is the most common pathological type of bladder cancer, a malignant tumor. However, an integrated multi-omics analysis of the Chinese UC patient cohort is lacking. METHODS: We performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 116 Chinese UC patients, comprising 45 non-muscle-invasive bladder cancer patients (NMIBCs) and 71 muscle-invasive bladder cancer patients (MIBCs). RESULT: Proteogenomic integration analysis indicated that SND1 and CDK5 amplifications on chromosome 7q were associated with the activation of STAT3, which was relevant to tumor proliferation. Chromosome 5p gain in NMIBC patients was a high-risk factor, through modulating actin cytoskeleton implicating in tumor cells invasion. Phosphoproteomic analysis of tumors and morphologically normal human urothelium produced UC-associated activated kinases, including CDK1 and PRKDC. Proteomic analysis identified three groups, U-I, U-II, and U-III, reflecting distinct clinical prognosis and molecular signatures. Immune subtypes of UC tumors revealed a complex immune landscape and suggested the amplification of TRAF2 related to the increased expression of PD-L1. Additionally, increased GARS, related to subtype U-II, was validated to promote pentose phosphate pathway by inhibiting activities of PGK1 and PKM2. CONCLUSIONS: This study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in urothelial carcinoma of the bladder.

Characterization of immune landscape in papillary thyroid cancer reveals distinct tumor immunogenicity and implications for immunotherapy.

Although the vast majority of patients with papillary thyroid cancer (PTC) have a favorable prognosis when conventional treatments are implemented, local recurrence and distant metastasis of advanced PTCs still hamper the survival and clinical management in certain patients. As immune checkpoint blockade (ICB) therapy achieves a great success in some advanced cancers, we aimed to investigate the immune landscape in PTC and its potential implications for prognosis and immunotherapy. In this study, different algorithms were conducted to estimate immune infiltration in PTC samples. A series of bioinformatic and machine learning approaches were performed to identify PTC-specific immune-related genes (IRGs) and distinct immune clusters. Differences in intrinsic tumor immunogenicity and potential immunotherapy response were observed between distinct immune clusters. A prognostic immune-related signature (IRS) was established to predict progression-free survival (PFS). IRS exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, altered pathways, mutational patterns, and potential applicable drugs were analyzed in different immune-related risk groups. Our study gained some insight into the immune landscape of PTC, and provided some useful clues for introducing immune-based molecular classification into risk stratification and guiding ICB decision-making.

Computational methods in tumor immunology.

The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.