Copy number loss of the interferon gene cluster in melanomas is linked to reduced T cell infiltrate and poor patient prognosis

Novel liquid biopsy CNV biomarkers in malignant melanoma

Malignant melanoma (MM) is known for its abundance of genetic alterations and a tendency for rapid metastasizing. Identification of novel plasma biomarkers may enhance non-invasive diagnostics and disease monitoring. Initially, we examined copy number variations (CNV) in CDK genes (CDKN2A, CDKN2B, CDK4) using MLPA (gDNA) and ddPCR (ctDNA) analysis. Subsequently, low-coverage whole genome sequencing (lcWGS) was used to identify the most common CNV in plasma samples, followed by ddPCR verification of chosen biomarkers. CNV alterations in CDK genes were identified in 33.3% of FFPE samples (Clark IV, V only). Detection of the same genes in MM plasma showed no significance, neither compared to healthy plasmas nor between pre- versus post-surgery plasma. Sequencing data showed the most common CNV occurring in 6q27, 4p16.1, 10p15.3, 10q22.3, 13q34, 18q23, 20q11.21-q13.12 and 22q13.33. CNV in four chosen genes (KIF25, E2F1, DIP2C and TFG) were verified by ddPCR using 2 models of interpretation. Model 1 was concordant with lcWGS results in 54% of samples, for model 2 it was 46%. Although CDK genes have not been proven to be suitable CNV liquid biopsy biomarkers, lcWGS defined the most frequently affected chromosomal regions by CNV. Among chosen genes, DIP2C demonstrated a potential for further analysis.

A modified HSV-1 oncolytic virus reconciles antiviral and antitumor immunity via promoting IFNβ expression and inhibiting PKR

Type I interferon (IFN-I) is a potent immune modulator intricately involved in regulating tumor immunity. Meanwhile, the integrity of the IFN-I signaling pathway is essential for radiotherapy, chemotherapy, targeted therapy, and immunotherapy. However, the clinical application of IFN-I remains challenging due to its non-specific cytotoxicity and limited half-life. To overcome these limitations, we developed a gene delivery platform, CRISPR-V, enabling the rapid creation of novel HSV-1 oncolytic viruses. Utilizing this platform, we created an oncolytic virus, OVH-IFNβ, in which the IFNβ gene was incorporated into the HSV-1 genome. However, exogenous IFNβ expression significantly inhibited OVH-IFNβ replication. Through transcriptome data analyses, we identified several ISG genes inhibiting OVH-IFNβ replication. By gene knockout and functional studies of the downstream effectors, we confirmed the prominent antiviral activities of protein kinase R (PKR). To balance the antitumor and antiviral immunity of IFNβ, we developed a novel HSV-1 oncolytic virus, OVH-IFNβ-iPKR, which can express IFNβ while inhibiting PKR, leading to a potent antitumor immunity while reducing the antiviral capacity of IFNβ. OVH-IFNβ-iPKR shows a strong ability to induce immunogenic cell death and activate tumor-specific CD8+ T cells, leading to de novo immune responses and providing a novel strategy for tumor immunotherapy.

Loss of p14 diminishes immunogenicity in melanoma via non-canonical Wnt signaling by reducing the peptide surface density

Immunotherapy has achieved tremendous success in melanoma. However, only around 50% of advanced melanoma patients benefit from immunotherapy. Cyclin-dependent kinase inhibitor 2A (CDKN2A), encoding the two tumor-suppressor proteins p14ARF and p16INK4a, belongs to the most frequently inactivated gene loci in melanoma and leads to decreased T cell infiltration. While the role of p16INK4a has been extensively investigated, knowledge about p14ARF in melanoma is scarce. In this study, we elucidate the impact of reduced p14ARF expression on melanoma immunogenicity. Knockdown of p14ARF in melanoma cell lines diminished their recognition and killing by melanoma differentiation antigen (MDA)-specific T cells. Resistance was caused by a reduction of the peptide surface density of presented MDAs. Immunopeptidomic analyses revealed that antigen presentation via human leukocyte antigen class I (HLA-I) molecules was enhanced upon p14ARF downregulation in general, but absolute and relative expression of cognate peptides was decreased. However, this phenotype is associated with a favorable outcome for melanoma patients. Limiting Wnt5a signaling reverted this phenotype, suggesting an involvement of non-canonical Wnt signaling. Taken together, our data indicate a new mechanism limiting MDA-specific T cell responses by decreasing both absolute and relative MDA-peptide presentation in melanoma.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

IFN-α/β/IFN-γ/IL-15 pathways identify GBP1-expressing tumors with an immune-responsive phenotype

Immunotherapy is widely used in cancer treatment; however, only a subset of patients responds well to it. Significant efforts have been made to identify patients who will benefit from immunotherapy. Successful anti-tumor immunity depends on an intact cancer-immunity cycle, especially long-lasting CD8+ T-cell responses. Interferon (IFN)-α/β/IFN-γ/interleukin (IL)-15 pathways have been reported to be involved in the development of CD8+ T cells. And these pathways may predict responses to immunotherapy. Herein, we aimed to analyze multiple public databases to investigate whether IFN-α/β/IFN-γ/IL-15 pathways could be used to predict the response to immunotherapy. Results showed that IFN-α/β/IFN-γ/IL-15 pathways could efficiently predict immunotherapy response, and guanylate-binding protein 1 (GBP1) could represent the IFN-α/β/IFN-γ/IL-15 pathways. In public and private cohorts, we further demonstrated that GBP1 could efficiently predict the response to immunotherapy. Functionally, GBP1 was mainly expressed in macrophages and strongly correlated with chemokines involved in T-cell migration. Therefore, our study comprehensively investigated the potential role of GBP1 in immunotherapy, which could serve as a novel biomarker for immunotherapy and a target for drug development.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions

OBJECTIVES

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome.

METHODS

Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions.

RESULTS

Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players.

CONCLUSION

This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

cGAS-STING pathway mediates activation of dendritic cell sensing of immunogenic tumors

Type I interferons (IFN-I) play pivotal roles in tumor therapy for three decades, underscoring the critical importance of maintaining the integrity of the IFN-1 signaling pathway in radiotherapy, chemotherapy, targeted therapy, and immunotherapy. However, the specific mechanism by which IFN-I contributes to these therapies, particularly in terms of activating dendritic cells (DCs), remains unclear. Based on recent studies, aberrant DNA in the cytoplasm activates the cyclic GMP-AMP synthase (cGAS)- stimulator of interferon genes (STING) signaling pathway, which in turn produces IFN-I, which is essential for antiviral and anticancer immunity. Notably, STING can also enhance anticancer immunity by promoting autophagy, inflammation, and glycolysis in an IFN-I-independent manner. These research advancements contribute to our comprehension of the distinctions between IFN-I drugs and STING agonists in the context of oncology therapy and shed light on the challenges involved in developing STING agonist drugs. Thus, we aimed to summarize the novel mechanisms underlying cGAS-STING-IFN-I signal activation in DC-mediated antigen presentation and its role in the cancer immune cycle in this review.

Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset

Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Biological basis of extensive pleiotropy between blood traits and cancer risk

BACKGROUND

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations.

METHODS

Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis.

RESULTS

The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis.

CONCLUSIONS

This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes

Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

Mapping the splicing landscape of the human immune system

Most human genes code for more than one transcript. Different ratios of transcripts of the same gene can be found in different cell types or states, indicating differential use of transcription start sites or differential splicing. Such differential transcript use (DTUs) events provide an additional layer of regulation and protein diversity. With the exceptions of PTPRC and CIITA, there are very few reported cases of DTU events in the immune system. To rigorously map DTUs between different human immune cell types, we leveraged four publicly available RNA sequencing datasets. We identified 282 DTU events between five human healthy immune cell types that appear in at least two datasets. The patterns of the DTU events were mostly cell-type-specific or lineage-specific, in the context of the five cell types tested. DTUs correlated with the expression pattern of potential regulators, namely, splicing factors and transcription factors. Of the several immune related conditions studied, only sepsis affected the splicing of more than a few genes and only in innate immune cells. Taken together, we map the DTUs landscape in human peripheral blood immune cell types, and present hundreds of genes whose transcript use changes between cell types or upon activation.

Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes

BACKGROUNDLow-dose anti-thymocyte globulin (ATG) transiently preserves C-peptide and lowers HbA1c in individuals with recent-onset type 1 diabetes (T1D); however, the mechanisms of action and features of the response remain unclear. Here, we characterized the post hoc immunological outcomes of ATG administration and their potential use as biomarkers of metabolic response to therapy (i.e., improved preservation of endogenous insulin production).METHODSWe assessed gene and protein expression, targeted gene methylation, and cytokine concentrations in peripheral blood following treatment with ATG (n = 29), ATG plus granulocyte colony-stimulating factor (ATG/G-CSF, n = 28), or placebo (n = 31).RESULTSTreatment with low-dose ATG preserved regulatory T cells (Tregs), as measured by stable methylation of FOXP3 Treg-specific demethylation region (TSDR) and increased proportions of CD4+FOXP3+ Tregs (P < 0.001) identified by flow cytometry. While treatment effects were consistent across participants, not all maintained C-peptide. Responders exhibited a transient rise in IL-6, IP-10, and TNF-α (P < 0.05 for all) 2 weeks after treatment and a durable CD4+ exhaustion phenotype (increased PD-1+KLRG1+CD57- on CD4+ T cells [P = 0.011] and PD1+CD4+ Temra MFI [P < 0.001] at 12 weeks, following ATG and ATG/G-CSF, respectively). ATG nonresponders displayed higher proportions of senescent T cells (at baseline and after treatment) and increased methylation of EOMES (i.e., less expression of this exhaustion marker).CONCLUSIONAltogether in these exploratory analyses, Th1 inflammation-associated serum and CD4+ exhaustion transcript and cellular phenotyping profiles may be useful for identifying signatures of clinical response to ATG in T1D.TRIAL REGISTRATIONClinicalTrials.gov NCT02215200.FUNDINGThe Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011), the NIH (R01 DK106191 Supplement, K08 DK128628), NIH TrialNet (U01 DK085461), and the NIH NIAID (P01 AI042288).

ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery

BACKGROUND

The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets.

RESULTS

Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment, https://marisshiny.

RESEARCH

chop.edu/ROGUE/ ), a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells.

CONCLUSIONS

User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.

Gene expression signature predicts rate of type 1 diabetes progression

BACKGROUND

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

METHODS

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.

FINDINGS

We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.

INTERPRETATION

There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.

FUNDING

A full list of funding bodies can be found under Acknowledgments.

Confounding factors in profiling of locus-specific human endogenous retrovirus (HERV) transcript signatures in primary T cells using multi-study-derived datasets

BACKGROUND

Human endogenous retroviruses (HERV) are repetitive sequence elements and a substantial part of the human genome. Their role in development has been well documented and there is now mounting evidence that dysregulated HERV expression also contributes to various human diseases. While research on HERV elements has in the past been hampered by their high sequence similarity, advanced sequencing technology and analytical tools have empowered the field. For the first time, we are now able to undertake locus-specific HERV analysis, deciphering expression patterns, regulatory networks and biological functions of these elements. To do so, we inevitable rely on omics datasets available through the public domain. However, technical parameters inevitably differ, making inter-study analysis challenging. We here address the issue of confounding factors for profiling locus-specific HERV transcriptomes using datasets from multiple sources.

METHODS

We collected RNAseq datasets of CD4 and CD8 primary T cells and extracted HERV expression profiles for 3220 elements, resembling most intact, near full-length proviruses. Looking at sequencing parameters and batch effects, we compared HERV signatures across datasets and determined permissive features for HERV expression analysis from multiple-source data.

RESULTS

We could demonstrate that considering sequencing parameters, sequencing-depth is most influential on HERV signature outcome. Sequencing samples deeper broadens the spectrum of expressed HERV elements. Sequencing mode and read length are secondary parameters. Nevertheless, we find that HERV signatures from smaller RNAseq datasets do reliably reveal most abundantly expressed HERV elements. Overall, HERV signatures between samples and studies overlap substantially, indicating a robust HERV transcript signature in CD4 and CD8 T cells. Moreover, we find that measures of batch effect reduction are critical to uncover genic and HERV expression differences between cell types. After doing so, differences in the HERV transcriptome between ontologically closely related CD4 and CD8 T cells became apparent.

CONCLUSION

In our systematic approach to determine sequencing and analysis parameters for detection of locus-specific HERV expression, we provide evidence that analysis of RNAseq datasets from multiple studies can aid confidence of biological findings. When generating de novo HERV expression datasets we recommend increased sequence depth ( > = 100 mio reads) compared to standard genic transcriptome pipelines. Finally, batch effect reduction measures need to be implemented to allow for differential expression analysis.

Type I interferon signaling in malignant blasts contributes to treatment efficacy in AML patients

While type I interferon (IFN) is best known for its key role against viral infection, accumulating preclinical and clinical data indicate that robust type I IFN production in the tumor microenvironment promotes cancer immunosurveillance and contributes to the efficacy of various antineoplastic agents, notably immunogenic cell death inducers. Here, we report that malignant blasts from patients with acute myeloid leukemia (AML) release type I IFN via a Toll-like receptor 3 (TLR3)-dependent mechanism that is not driven by treatment. While in these patients the ability of type I IFN to stimulate anticancer immune responses was abolished by immunosuppressive mechanisms elicited by malignant blasts, type I IFN turned out to exert direct cytostatic, cytotoxic and chemosensitizing activity in primary AML blasts, leukemic stem cells from AML patients and AML xenograft models. Finally, a genetic signature of type I IFN signaling was found to have independent prognostic value on relapse-free survival and overall survival in a cohort of 132 AML patients. These findings delineate a clinically relevant, therapeutically actionable and prognostically informative mechanism through which type I IFN mediates beneficial effects in patients with AML.

Exploration of Tumor Biopsy Gene Signatures to Understand the Role of the Tumor Microenvironment in Outcomes to Lisocabtagene Maraleucel

In the TRANSCEND NHL 001 study, 53% of patients with relapsed/refractory large B-cell lymphoma (LBCL) treated with lisocabtagene maraleucel (liso-cel) achieved a complete response (CR). To determine characteristics of patients who did and did not achieve a CR, we examined the tumor biology and microenvironment from lymph node tumor biopsies. LBCL biopsies from liso-cel-treated patients were taken pretreatment and ∼11 days posttreatment for RNA sequencing (RNA-seq) and multiplex immunofluorescence (mIF). We analyzed gene expression data from pretreatment biopsies (N = 78) to identify gene sets enriched in patients who achieved a CR to those with progressive disease. Pretreatment biopsies from month-3 CR patients displayed higher expression levels of T-cell and stroma-associated genes, and lower expression of cell-cycle genes. To interpret whether LBCL samples were "follicular lymphoma (FL)-like," we constructed an independent gene expression signature and found that patients with a higher "FL-like" gene expression score had longer progression-free survival (PFS). Cell of origin was not associated with response or PFS, but double-hit gene expression was associated with shorter PFS. The day 11 posttreatment samples (RNA-seq, N = 73; mIF, N = 53) had higher levels of chimeric antigen receptor (CAR) T-cell densities and CAR gene expression, general immune infiltration, and immune activation in patients with CR. Further, the majority of T cells in the day 11 samples were endogenous. Gene expression signatures in liso-cel-treated patients with LBCL can inform the development of combination therapies and next-generation CAR T-cell therapies.

Transcriptomic analysis of pathways associated with ITGAV/alpha(v) integrin-dependent autophagy in human B cells

Macroautophagy/autophagy proteins have been linked with the development of immune-mediated diseases including lupus, but the mechanisms for this are unclear due to the complex roles of these proteins in multiple immune cell types. We have previously shown that a form of noncanonical autophagy induced by ITGAV/alpha(v) integrins regulates B cell activation by viral and self-antigens, in mice. Here, we investigate the involvement of this pathway in B cells from human tissues. Our data reveal that autophagy is specifically induced in the germinal center and memory B cell subpopulations of human tonsils and spleens. Transcriptomic analysis show that the induction of autophagy is related to unique aspects of activated B cells such as mitochondrial metabolism. To understand the function of ITGAV/alpha(v) integrin-dependent autophagy in human B cells, we used CRISPR-mediated knockdown of autophagy genes. Integrating data from primary B cells and knockout cells, we found that ITGAV/alpha(v)-dependent autophagy limits activation of specific pathways related to B cell responses, while promoting others. These data provide new mechanistic links for autophagy and B-cell-mediated immune dysregulation in diseases such as lupus.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

A comprehensive assessment of cell type-specific differential expression methods in bulk data

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

A new natural killer cell-specific gene signature predicting recurrence in colorectal cancer patients

The protective role of Natural Killer (NK) cell tumour immunosurveillance has long been recognised in colorectal cancer (CRC). However, as most patients show limited intra-tumoral NK cell infiltration, improving our ability to identify those with high NK cell activity might aid in dissecting the molecular features which underlie NK cell sensitivity. Here, a novel CRC-specific NK cell gene signature that infers NK cell load in primary tissue samples was derived and validated in multiple patient CRC cohorts. In contrast with other NK cell gene signatures that have several overlapping genes across different immune cell types, our NK cell signature has been extensively refined to be specific for CRC-infiltrating NK cells. The specificity of the signature is substantiated in tumour-infiltrating NK cells from primary CRC tumours at the single cell level, and the signature includes genes representative of NK cells of different maturation states, activation status and anatomical origin. Our signature also accurately discriminates murine NK cells, demonstrating the applicability of this geneset when mining datasets generated from preclinical studies. Differential gene expression analysis revealed tumour-intrinsic features associated with NK cell inclusion versus exclusion in CRC patients, with those tumours with predicted high NK activity showing strong evidence of enhanced chemotactic and cytotoxic transcriptional programs. Furthermore, survival modelling indicated that NK signature expression is associated with improved survival outcomes in CRC patients. Thus, scoring CRC samples with this refined NK cell signature might aid in identifying patients with high NK cell activity who could be prime candidates for NK cell directed immunotherapies.

Germline variants associated with toxicity to immune checkpoint blockade

Immune checkpoint inhibitors (ICIs) have yielded remarkable responses but often lead to immune-related adverse events (irAEs). Although germline causes for irAEs have been hypothesized, no individual variant associated with developing irAEs has been identified. We carried out a genome-wide association study of 1,751 patients on ICIs across 12 cancer types. We investigated two irAE phenotypes: (1) high-grade (3-5) and (2) all-grade events. We identified 3 genome-wide significant associations (P < 5 × 10-8) in the discovery cohort associated with all-grade irAEs: rs16906115 near IL7 (combined P = 3.6 × 10-11; hazard ratio (HR) = 2.1); rs75824728 near IL22RA1 (combined P = 3.5 × 10-8; HR = 1.8); and rs113861051 on 4p15 (combined P = 1.2 × 10-8, HR = 2.0); rs16906115 was replicated in 3 independent studies. The association near IL7 colocalized with the gain of a new cryptic exon for IL7, a critical regulator of lymphocyte homeostasis. Patients carrying the IL7 germline variant exhibited significantly increased lymphocyte stability after ICI initiation, which was itself predictive of downstream irAEs and improved survival.

Somatic 9p24.1 alterations in HPV- head and neck squamous cancer dictate immune microenvironment and anti-PD-1 checkpoint inhibitor activity

Somatic copy number alterations (SCNAs), generally (1) losses containing interferons and interferon-pathway genes, many on chromosome 9p, predict immune-cold, immune checkpoint therapy (ICT)-resistant tumors (2); however, genomic regions mediating these effects are unclear and probably tissue specific. Previously, 9p21.3 loss was found to be an early genetic driver of human papillomavirus-negative (HPV^-) head and neck squamous cancer (HNSC), associated with an immune-cold tumor microenvironment (TME) signal, and recent evidence suggested that this TME-cold phenotype was greatly enhanced with 9p21 deletion size, notably encompassing band 9p24.1 (3). Here, we report multi-omic, -threshold and continuous-variable dissection of 9p21 and 9p24 loci (including depth and degree of somatic alteration of each band at each locus, and each gene at each band) and TME of four HPV^- HNSC cohorts. Preferential 9p24 deletion, CD8 T-cell immune-cold associations were observed, driven by 9p24.1 loss, and in turn by an essential telomeric regulatory gene element, JAK2-CD274. Surprisingly, same genetic region gains were immune hot. Related 9p21-TME analyses were less evident. Inherent 9p-band-level influences on anti-PD1 ICT survival rates, coincident with TME patterns, were also observed. At a 9p24.1 whole-transcriptome expression threshold of 60th percentile, ICT survival rate exceeded that of lower expression percentiles and of chemotherapy; below this transcript threshold, ICT survival was inferior to chemotherapy, the latter unaffected by 9p24.1 expression level (P-values < 0.01, including in a PD-L1 immunohistochemistry-positive patient subgroup). Whole-exome analyses of 10 solid-tumor types suggest that these 9p-related ICT findings could be relevant to squamous cancers, in which 9p24.1 gain/immune-hot associations exist.

Lq-based robust analytics on ultrahigh and high dimensional data

Ultrahigh and high dimensional data are common in regression analysis for various fields, such as omics data, finance, and biological engineering. In addition to the problem of dimension, the data might also be contaminated. There are two main types of contamination: outliers and model misspecification. We develop an unique method that takes into account the ultrahigh or high dimensional issues and both types of contamination. In this article, we propose a framework for feature screening and selection based on the minimum Lq-likelihood estimation (MLqE), which accounts for the model misspecification contamination issue and has also been shown to be robust to outliers. In numerical analysis, we explore the robustness of this framework under different outliers and model misspecification scenarios. To examine the performance of this framework, we conduct real data analysis using the skin cutaneous melanoma data. When comparing with traditional screening and feature selection methods, the proposed method shows superiority in both variable identification effectiveness and parameter estimation accuracy.

Single-cell RNA sequencing uncovers the nuclear decoy lincRNA PIRAT as a regulator of systemic monocyte immunity during COVID-19

SARS-CoV-2–infected patients often display characteristic changes in the production of immune mediators that trigger life-threatening courses of COVID-19. The underlying molecular mechanisms are not yet fully understood. Here, we used single-cell RNA sequencing to investigate the involvement of the emerging class of long regulatory RNA in COVID-19. Our data reveal that a previously unknown regulatory RNA in the nucleus of immune cells is altered after SARS-CoV-2 infection. The degradation of this RNA removes a natural brake on the production of critical immune mediators that can promote the development of severe COVID-19. We believe that therapeutic intervention in this nuclear RNA circuit could counteract the overproduction of disease-causing immune mediators and protect against severe COVID-19.

The systemic immune response to viral infection is shaped by master transcription factors, such as NF-κB, STAT1, or PU.1. Although long noncoding RNAs (lncRNAs) have been suggested as important regulators of transcription factor activity, their contributions to the systemic immunopathologies observed during SARS-CoV-2 infection have remained unknown. Here, we employed a targeted single-cell RNA sequencing approach to reveal lncRNAs differentially expressed in blood leukocytes during severe COVID-19. Our results uncover the lncRNA PIRAT (PU.1-induced regulator of alarmin transcription) as a major PU.1 feedback-regulator in monocytes, governing the production of the alarmins S100A8/A9, key drivers of COVID-19 pathogenesis. Knockout and transgene expression, combined with chromatin-occupancy profiling, characterized PIRAT as a nuclear decoy RNA, keeping PU.1 from binding to alarmin promoters and promoting its binding to pseudogenes in naïve monocytes. NF-κB–dependent PIRAT down-regulation during COVID-19 consequently releases a transcriptional brake, fueling alarmin production. Alarmin expression is additionally enhanced by the up-regulation of the lncRNA LUCAT1, which promotes NF-κB–dependent gene expression at the expense of targets of the JAK-STAT pathway. Our results suggest a major role of nuclear noncoding RNA networks in systemic antiviral responses to SARS-CoV-2 in humans.

Impact of cancer evolution on immune surveillance and checkpoint inhibitor response

Intratumour heterogeneity (ITH) is pervasive across all cancers studied and may provide the evolving tumour multiple routes to escape immune surveillance. Immune checkpoint inhibitors (CPIs) are rapidly becoming standard of care for many cancers. Here, we discuss recent work investigating the influence of ITH on patient response to immune checkpoint inhibitor (CPI) therapy. At its simplest, ITH may confound the diagnostic accuracy of predictive biomarkers used to stratify patients for CPI therapy. Furthermore, ITH is fuelled by mechanisms of genetic instability that can both engage immune surveillance and drive immune evasion. A greater appreciation of the interplay between ITH and the immune system may hold the key to increasing the proportion of patients experiencing durable responses from CPI therapy.

Cellular transcriptional alterations of peripheral blood in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD.

METHODS

To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion.

RESULTS

We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains.

CONCLUSIONS

We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.

Metacells untangle large and complex single-cell transcriptome networks

Background

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization.

Results

We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

Conclusions

SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04861-1.

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8⁺ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

NKG7 Enhances CD8+ T Cell Synapse Efficiency to Limit Inflammation

Cytotoxic lymphocytes are essential for anti-tumor immunity, and for effective responses to cancer immunotherapy. Natural killer cell granule protein 7 (NKG7) is expressed at high levels in cytotoxic lymphocytes infiltrating tumors from patients treated with immunotherapy, but until recently, the role of this protein in cytotoxic lymphocyte function was largely unknown. Unexpectedly, we found that highly CD8+ T cell-immunogenic murine colon carcinoma (MC38-OVA) tumors grew at an equal rate in Nkg7^+/+ and Nkg7^-/- littermate mice, suggesting NKG7 may not be necessary for effective CD8+ T cell anti-tumor activity. Mechanistically, we found that deletion of NKG7 reduces the ability of CD8+ T cells to degranulate and kill target cells in vitro. However, as a result of inefficient cytotoxic activity, NKG7 deficient T cells form a prolonged immune synapse with tumor cells, resulting in increased secretion of inflammatory cytokines, including tumor necrosis factor alpha (TNF). By deleting the TNF receptor, TNFR1, from MC38-OVA tumors, we demonstrate that this hyper-secretion of TNF compensates for reduced synapse-mediated cytotoxic activity against MC38-OVA tumors in vivo, via increased TNF-mediated tumor cell death. Taken together, our results demonstrate that NKG7 enhances CD8+ T cell immune synapse efficiency, which may serve as a mechanism to accelerate direct cytotoxicity and limit potentially harmful inflammatory responses.

Identification of chromosomal instability-associated genes as hepatocellular carcinoma progression-related biomarkers to guide clinical diagnosis, prognosis and therapy

Hepatocellular carcinoma (HCC) is a type of cancer characterized by high heterogeneity and a complex multistep progression process. Significantly-altered biomarkers for HCC need to be identified. Differentially expressed genes and weighted gene co-expression network analyses were used to identify progression-related biomarkers. LASSO-Cox regression and random forest algorithms were used to construct the progression-related prognosis (PRP) score. Three chromosomal instability-associated genes (KIF20A, TOP2A, and TTK) have been identified as progression-related biomarkers. The robustness of the PRP scores were validated using four independent cohorts. Immune status was observed using the single-sample gene set enrichment analysis (ssGSEA). Comprehensive analysis showed that the patients with high PRP score had wider genomic alterations, more malignant phenotypes, and were in a state of immunosuppression. The diagnostic models constructed via logistic regression based on the three genes showed satisfactory performances in distinguishing HCC from cirrhotic tissues or dysplastic nodules. The nomogram combining PRP scores with clinical factors had a better performance in predicting prognosis than the tumor node metastasis classification (TNM) system. We further confirmed that KIF20A, TOP2A, and TTK were highly expressed in HCC tissues than in cirrhotic tissues. Downregulation of all three genes aggravated chromosomal instabilities in HCC and suppressed HCC cells viability both in vitro and in vivo. Overall, our study highlights the important roles of chromosomal instability-associated genes during the progression of HCC and their potential clinical diagnosis and prognostic value and provides promising new ideas for developing therapeutic strategies to improve the outcomes of HCC patients.

Immune Cytolytic Activity for Comprehensive Insights of the Immune Landscape in Endometrial Carcinoma

Immune checkpoint blockade (ICB) has been explored as a therapeutic strategy to recover the antitumor immune activities against endometrial cancer (EC) escaping from immune surveillance. Increasing evidence has indicated that microsatellite instability (MSI) is a promising biomarker to stratify patients for the ICB therapy. However, even in patients with MSI-High (MSI-H) endometrial cancers, PD-L1 inhibitors, avelumab, and durvalumab have shown only 27% of response rates. Therefore, there is an urgent need to discover new biomarkers for a predictive response to ICB therapy. In this study, we demonstrated that the immune cytolytic activity (CYT) index was significantly correlated with the development and response to immunotherapy in EC. The data showed that higher CYT was significantly associated with better clinical outcome, more antitumor infiltrating immune cells, fewer somatic copy number alterations, but a higher TMB (Tumor mutational burden) status. Furthermore, CYT-high EC was notably relevant to the high expression of various immune checkpoint molecules and showed more effective responses to ICB treatment. Taken together, this study provided new insights into the connection between diverse genetic events and the immune microenvironment in EC and indicated that the CYT status might be a promising biomarker to stratify patients with EC for ICB therapy.

Chromosome instability and aneuploidy as context-dependent activators or inhibitors of antitumor immunity

Chromosome instability (CIN) and its major consequence, aneuploidy, are hallmarks of human cancers. In addition to imposing fitness costs on tumor cells through several cell-intrinsic mechanisms, CIN/aneuploidy also provokes an antitumor immune response. However, as the major contributor to genomic instability, intratumor heterogeneity generated by CIN/aneuploidy helps tumor cells to evolve methods to overcome the antitumor role of the immune system or even convert the immune system to be tumor-promoting. Although the interplay between CIN/aneuploidy and the immune system is complex and context-dependent, understanding this interplay is essential for the success of immunotherapy in tumors exhibiting CIN/aneuploidy, regardless of whether the efficacy of immunotherapy is increased by combination with strategies to promote CIN/aneuploidy or by designing immunotherapies to target CIN/aneuploidy directly.

Identification of a Seven-Differentially Expressed Gene-Based Recurrence-Free Survival Model for Melanoma Patients

Melanoma is a malignant tumor that originates in melanocytes of the skin or mucous membrane, which has a high mortality rate and worse prognosis. Therefore, perspective prognosis evaluation seems more important for patients' treatment. Gene expression profiles of melanoma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. 130 consistent differentially expressed genes (DEGs) were identified between melanoma and nevus tissues from two GEO cohorts. Prognostic genes were identified by univariate analysis, and 20 of them were regarded to be associated with the recurrence-free survival (RFS) of melanoma patients. Then, the LASSO Cox regression analysis chose seven of them to establish a seven-DEG-based RFS predicting signature. We demonstrated that this model was more powerful to predict RFS risk than other individual clinical features and was able to independently predict the RFS outcomes in different subsets of patients. We attempted to search for the underlying mechanisms by analyzing the coexpression genes of the seven candidates, and the pathway enrichment analyses indicated that immune response-related pathways might play a critical role in melanoma progression. Finally, we establish a robust seven-DEG-based RFS predicting signature, which will facilitate the personalized treatment of melanoma patients.

Evaluating the Immunopathogenesis of Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium

ABSTRACT

The association between acute pancreatitis (AP) and diabetes mellitus (DM) has long been established, with the initial descriptions of AP patients presenting with DM after a bout of AP published in the 1940s and 50s. However, the potential mechanisms involved, particularly those components related to the immune system, have not been well defined. The Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study is a multicenter clinical study designed to understand the frequency and phenotype of DM developing after AP. This article describes one objective of the DREAM study: to determine the immunologic mechanisms of DM after AP, including the contribution of β-cell autoimmunity. This component of the study will assess the presence of islet autoimmunity, as well as the magnitude and kinetics of the innate and adaptive immune response at enrollment and during longitudinal follow-up after 1 or more episodes of AP. Finally, DREAM will evaluate the relationship between immune features, DM development, and pancreatitis etiology and severity.

A transcriptome atlas and interactive analysis platform for autoimmune disease

With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell–cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available.

Database URL: http://galaxy.ustc.edu.cn/IAAA

Differential Regulation of NK Cell Receptors in Acute Lymphoblastic Leukemia

Cancer immunotherapies are preferred over conventional treatments which are highly cytotoxic to normal cells. Focus has been on T cells but natural killer (NK) cells have equal potential. Concepts in cancer control and influence of sex require further investigation to improve successful mobilization of immune cells in cancer patients. Acute lymphoblastic leukemia (ALL) is a hematological malignancy mainly of B cell (B-ALL) and T cell (T-ALL) subtypes. Influence of ALL on NK cell is still unclear. Targeted next-generation sequencing was conducted on 62 activating/inhibitory receptors, ligands, effector, and exhaustion molecules on T-ALL (6 males) and normal controls (NC) (4 males and 4 females). Quantitative PCR (q-PCR) further investigated copy number variation (CNV), methylation index (MI), and mRNA expression of significant genes in T-ALL (14 males), NC (12 males and 12 females), and B-ALL samples (N = 12 males and 12 females). Bioinformatics revealed unique variants particularly rs2253849 (T>C) in KLRC1 and rs1141715 (A>G) in KLRC2 only among T-ALL (allele frequency 0.8-1.0). Gene amplification was highest in female B-ALL compared to male B-ALL (KLRC2, KLRC4, and NCR3, p < 0.05) and lowest in male T-ALL cumulating in deletion of KLRD1 and CD69. MI was higher in male ALL of both subtypes compared to normal (KIR2DL1-2 and 4 and KIR2DS2 and 4, p < 0.05) as well as to female B-ALL (KIR3DL2 and KIR2DS2, p < 0.05). mRNA expressions were low. Thus, ALL subtypes potentially regulated NK cell suppression by different mechanisms which should be considered in future immunotherapies for ALL.

9p21.3 microdeletion involving CDKN2A/2B in a young patient with multiple primary cancers and review of the literature

Germline pathogenic variants in CDKN2A predispose to various cancers, including melanoma, pancreatic cancer, and neural system tumors, whereas CDKN2B variants are associated with renal cell carcinoma. A few case reports have described heterozygous germline deletions spanning both CDKN2A and CDKN2B associated with a cancer predisposition syndrome (CPS) that constitutes a risk of cancer beyond those associated with haploinsufficiency of each gene individually, indicating an additive effect or a contiguous gene deletion syndrome. We report a young woman with a de novo germline 9p21 microdeletion involving the CDKN2A/CDKN2B genes, who developed six primary cancers since childhood, including a very rare extraskeletal osteosarcoma (eOS) at the age of 8. To our knowledge this is the first report of eOS in a patient with CDKN2A/CDKN2B deletion.

Genetic Changes Driving Immunosuppressive Microenvironments in Oral Premalignancy

Oral premalignant lesions (OPLs) are the precursors to oral cavity cancers, and have variable rates of progression to invasive disease. As an intermediate state, OPLs have acquired a subset of the genomic alterations while arising in an oral inflammatory environment. These specific genomic changes may facilitate the transition to an immune microenvironment that permits malignant transformation. Here, we will discuss mechanisms by which OPLs develop an immunosuppressive microenvironment that facilitates progression to invasive cancer. We will describe how genomic alterations and immune microenvironmental changes co-evolve and cooperate to promote OSCC progression. Finally, we will describe how these immune microenvironmental changes provide specific and unique evolutionary vulnerabilities for targeted therapies. Therefore, understanding the genomic changes that drive immunosuppressive microenvironments may eventually translate into novel biomarker and/or therapeutic approaches to limit the progression of OPLs to potential lethal oral cancers.

Cytotoxicity-Related Gene Expression and Chromatin Accessibility Define a Subset of CD4+ T Cells That Mark Progression to Type 1 Diabetes

Type 1 diabetes in children is heralded by a preclinical phase defined by circulating autoantibodies to pancreatic islet antigens. How islet autoimmunity is initiated and then progresses to clinical diabetes remains poorly understood. Only one study has reported gene expression in specific immune cells of children at risk associated with progression to islet autoimmunity. We analyzed gene expression with RNA sequencing in CD4+ and CD8+ T cells, natural killer (NK) cells, and B cells, and chromatin accessibility by assay for transposase-accessible chromatin sequencing (ATAC-seq) in CD4+ T cells, in five genetically at risk children with islet autoantibodies who progressed to diabetes over a median of 3 years ("progressors") compared with five children matched for sex, age, and HLA-DR who had not progressed ("nonprogressors"). In progressors, differentially expressed genes (DEGs) were largely confined to CD4+ T cells and enriched for cytotoxicity-related genes/pathways. Several top-ranked DEGs were validated in a semi-independent cohort of 13 progressors and 11 nonprogressors. Flow cytometry confirmed that progression was associated with expansion of CD4+ cells with a cytotoxic phenotype. By ATAC-seq, progression was associated with reconfiguration of regulatory chromatin regions in CD4+ cells, some linked to differentially expressed cytotoxicity-related genes. Our findings suggest that cytotoxic CD4+ T cells play a role in promoting progression to type 1 diabetes.

Type I interferon activates MHC class I-dressed CD11b+ conventional dendritic cells to promote protective anti-tumor CD8+ T cell immunity

Tumor-infiltrating dendritic cells (DCs) assume varied functional states that impact anti-tumor immunity. To delineate the DC states associated with productive anti-tumor T cell immunity, we compared spontaneously regressing and progressing tumors. Tumor-reactive CD8+ T cell responses in Batf3-/- mice lacking type 1 DCs (DC1s) were lost in progressor tumors but preserved in regressor tumors. Transcriptional profiling of intra-tumoral DCs within regressor tumors revealed an activation state of CD11b+ conventional DCs (DC2s) characterized by expression of interferon (IFN)-stimulated genes (ISGs) (ISG+ DCs). ISG+ DC-activated CD8+ T cells ex vivo comparably to DC1. Unlike cross-presenting DC1, ISG+ DCs acquired and presented intact tumor-derived peptide-major histocompatibility complex class I (MHC class I) complexes. Constitutive type I IFN production by regressor tumors drove the ISG+ DC state, and activation of MHC class I-dressed ISG+ DCs by exogenous IFN-β rescued anti-tumor immunity against progressor tumors in Batf3-/- mice. The ISG+ DC gene signature is detectable in human tumors. Engaging this functional DC state may present an approach for the treatment of human disease.

Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma

ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. T cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of T cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ T cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded T cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action.

New Immunotherapy Combinations Enter the Battlefield of Malignant Mesothelioma

Chimeric antigen receptor (CAR) T cells targeting mesothelin plus pembrolizumab, and atezolizumab plus bevacizumab, have recently shown clinical efficacy in phase I trials in malignant pleural and peritoneal mesothelioma. Despite being tested in a highly selected patient population and requiring a complex engineering that can hardly be upscaled, CAR T cells combined with pembrolizumab bring the first proof of efficacy in cold solid tumors with low genomic heterogeneity, while atezolizumab-bevacizumab offers an easy-to-use combination of antiangiogenics and immunotherapy in an orphan disease.See related article by Raghav et al., p. 2738.See related article by Adusumilli et al., p. 2748.