Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma.

BACKGROUND: Pyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs). METHODS: Here, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy. RESULTS: Pyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response. CONCLUSIONS: Collectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.

The impacts of biologic treatment on metabolic profiling in psoriasis.

Psoriasis is an immune-mediated inflammatory disease commonly accompanied by various metabolic disorders. It is widely known that biologics could affect the metabolic status and comorbidities in psoriasis patients, however, the effects of biologics on metabolism in psoriasis patients remain poorly understood. The aim of this study was to elucidate the characteristic changes of metabolic profiling in psoriasis vulgaris (PsV) patients before and after applying biologics. Plasma samples were collected from a retrospective cohort of 43 PsV patients. Non-targeted metabolomics analyses were performed using liquid chromatography-mass spectrometry (LC-MS) to compare the metabolic profiles before and after applying adalimumab (ADA) or ixekizumab (IXE) for 4 weeks. Additionally, correlation analyses were conducted to investigate the associations between metabolite expression levels and clinical characteristics. The biologics significantly affected the metabolic profiles of PsV patients especially in glycerophospholipids (GPs). First, phosphatidylcholine (PC), unsaturated lysophosphatidylcholine (LPC), unsaturated lysophosphatidic acid (LPA) and unsaturated lysophosphatidylethanolamine (LPE) were significantly up-regulated, whereas phosphatidylethanolamine (PE), saturated LPC, saturated LPA and saturated LPE were predominantly down-regulated after biologic treatment. What is more, the changes in PE and LPA were mainly observed after applying IXE instead of ADA. Second, we also found GPs including PC, unsaturated LPC, unsaturated LPA and unsaturated LPE were primarily negatively correlated with disease severity, whereas, PE, saturated LPC, saturated LPA and saturated LPE displayed inverse correlations. Biologics could affect GP metabolism and facilitate the transition of metabolic status from a pro-inflammatory to an anti-inflammatory phenotype in PsV patients.

Single-cell profiling of ineffective erythropoiesis in a mouse model of ß-thalassaemia intermedia.

Beta-thalassaemia is an inherited haemoglobin disorder characterised by ineffective erythropoiesis (IE). The detailed pathogenesis of IE remains unclear. In this study, we used single-cell RNA sequencing (scRNA-seq) to examine IE in Th3/+ ß-thalassaemic mice. The results showed that the erythroid group was remarkably expanded, and genes involved in biological processes such as iron metabolism, haeme synthesis, protein folding, and response to heat were significantly upregulated from erythroid progenitors to reticulocytes in ß-thalassaemic mice. In particular, we identified a unique cell population close to reticulocytes, named ThReticulocytes, characterised by a high level of heat shock protein 70 (Hsp70) expression and dysregulation of iron metabolism and haeme synthesis signalling. Treatment of ß-thalassaemic mice with the haeme oxygenase inhibitor tin-mesoporphyrin effectively improved the iron disorder and IE, and the ThReticulocyte population and Hsp70 expression were significantly suppressed. This study revealed in detail the progression of IE at the single-cell level and possibly provided clues to find therapeutic targets in thalassaemia.

Associations of combined lifestyle and genetic risks with incident psoriasis: A prospective cohort study among UK Biobank participants of European ancestry.

BACKGROUND: Whether the lifestyle is associated with the risk of psoriasis in the presence of different genetic risk levels remains unknown. OBJECTIVE: To examine the gene-behavior interaction in association with incident psoriasis. METHODS: This study is based on the data from the UK Biobank, which recruited 500,000 participants. Genetic risk was categorized into low, intermediate, and high groups. The lifestyle score comprised the body mass index, smoking, physical activity, and diet and was also categorized into the ideal, intermediate, and poor groups. Within each genetic risk group, the risks of incident psoriasis associated with each lifestyle level were investigated and compared with the low genetic risk and ideal lifestyle group. RESULTS: Compared with the low genetic risk and ideal lifestyle group, the poor lifestyle and high genetic risk group was associated with a hazard ratio of up to 4.625 (95% confidence interval [CI], 2.920-7.348) for psoriasis. There was no interaction between genetic risk and lifestyle. The population attributable fractions of lifestyle and genetic risk were 32.2% (95% CI, 25.1%-38.6%) and 13.0% (95% CI, 3.2%-21.8%), respectively. LIMITATIONS: No verification in other independently ascertained populations. CONCLUSION: Lifestyle factors are predictive of the risk of incident psoriasis independent of genetic risk, and the relative impact of lifestyle factors was greater than that of genetic risk.

Identification of immune checkpoint and cytokine signatures associated with the response to immune checkpoint blockade in gastrointestinal cancers.

Immune checkpoint blockade (ICB) of the programmed cell death 1/programmed cell death ligand 1 (PD-1/PD-L1) immune checkpoint pathway has led to unprecedented advances in cancer therapy. However, the overall response rate of anti-PD-1/PD-L1 monotherapy is still unpromising, underscoring the need for predictive biomarkers. In this retrospective study, we collected pretreatment plasma samples from two independent cohorts of patients receiving ICB. To determine whether a signature of plasma cytokines could be associated with therapeutic efficacy, we systemically profiled cytokine clusters and functional groups in the discovery and validation datasets by using 59 multiplexed bead immunoassays and bioinformatics analysis. We first attempted to functionally classify the 59 immunological factors according to their biological classification or functional roles in the cancer-immunity cycle. Surprisingly, we observed that two signatures, the "checkpoint signature" and "trafficking of T-cell signature", were higher in the response subgroup than in the nonresponse subgroup in both the discovery and validation cohorts. Moreover, enrichment of the "checkpoint signature" was correlated with improved overall survival and progression-free survival in both datasets. In addition, we demonstrated that increased baseline levels of three checkpoint molecules (PD-L1, T-cell immunoglobulin mucin receptor 3 and T-cell-specific surface glycoprotein CD28) were common peripheral responsive correlates in both cohorts, thus rendering this "refined checkpoint signature" an ideal candidate for future verification. In the peripheral blood system, the "refined checkpoint signature" may function as a potential biomarker for anti-PD-1/PD-L1 monotherapy in gastrointestinal (GI) cancers.

CancerSEA: a cancer single-cell state atlas.

High functional heterogeneity of cancer cells poses a major challenge for cancer research. Single-cell sequencing technology provides an unprecedented opportunity to decipher diverse functional states of cancer cells at single-cell resolution, and cancer scRNA-seq datasets have been largely accumulated. This emphasizes the urgent need to build a dedicated resource to decode the functional states of cancer single cells. Here, we developed CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/ or http://202.97.205.69/CancerSEA/), the first dedicated database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states (including stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of 41 900 cancer single cells from 25 cancer types. It allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. CancerSEA also provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. In summary, CancerSEA provides a user-friendly interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of tens of thousands of cancer single cells and the corresponding PCGs/lncRNAs expression profiles.

Comprehensive analysis of long noncoding RNA (lncRNA)-chromatin interactions reveals lncRNA functions dependent on binding diverse regulatory elements.

Over the past decade, thousands of long noncoding RNAs (lncRNAs) have been identified, many of which play crucial roles in normal physiology and human disease. LncRNAs can interact with chromatin and then recruit protein complexes to remodel chromatin states, thus regulating gene expression. However, how lncRNA-chromatin interactions contribute to their biological functions is largely unknown. Here, we collected and constructed an atlas of 188,647 lncRNA-chromatin interactions in human and mouse. All lncRNAs showed diverse epigenetic modification patterns at their binding sites, especially the marks of enhancer activity. Functional analysis of lncRNA target genes further revealed that lncRNAs could exert their functions by binding to both promoter and distal regulatory elements, especially the distal regulatory elements. Intriguingly, many important pathways were observed to be widely regulated by lncRNAs through distal binding. For example, NEAT1, a cancer lncRNA, controls 13.3% of genes in the PI3K-AKT signaling pathway by interacting with distal regulatory elements. In addition, "two-gene" signatures composed of a lncRNA and its distal target genes, such as HOTAIR-CRIM1, provided significant clinical benefits relative to the lncRNA alone. In summary, our findings underscored that lncRNA-distal interactions were essential for lncRNA functions, which would provide new clues to understand the molecular mechanisms of lncRNAs in complex disease.

DiseaseEnhancer: a resource of human disease-associated enhancer catalog.

Large-scale sequencing studies discovered substantial genetic variants occurring in enhancers which regulate genes via long range chromatin interactions. Importantly, such variants could affect enhancer regulation by changing transcription factor bindings or enhancer hijacking, and in turn, make an essential contribution to disease progression. To facilitate better usage of published data and exploring enhancer deregulation in various human diseases, we created DiseaseEnhancer (http://biocc.hrbmu.edu.cn/DiseaseEnhancer/), a manually curated database for disease-associated enhancers. As of July 2017, DiseaseEnhancer includes 847 disease-associated enhancers in 143 human diseases. Database features include basic enhancer information (i.e. genomic location and target genes); disease types; associated variants on the enhancer and their mediated phenotypes (i.e. gain/loss of enhancer and the alterations of transcription factor bindings). We also include a feature on our website to export any query results into a file and download the full database. DiseaseEnhancer provides a promising avenue for researchers to facilitate the understanding of enhancer deregulation in disease pathogenesis, and identify new biomarkers for disease diagnosis and therapy.

An ecological scenario prediction model for newly created wetlands caused by coal mine subsidence in the Yanzhou, China.

The ecological model we developed can simulate the state of wetlands and predict ecosystem development by varying both parameter settings and forcing functions. The newly created wetland resulting from large-scale coal mining is a distinct type of wetland, but existing ecological models for this wetland type are limited in number and scope. The Yanzhou coalfield, located in Shandong Province in China, contains a typical newly created wetland that came into being after coal mining subsidence. We developed an ecological model for this wetland that estimates values for four state variables: phytoplankton biomass (A), zooplankton biomass (Z), sediment biomass (D), and hydrophyte biomass (H). Analysis of the results showed that the model was sensitive to changes in nutrient loading. As nutrient loads increased, plankton biomass increased, and the ratio of zooplankton biomass to phytoplankton biomass (Z/A) decreased. We defined three prediction scenarios for the wetland and calculated their eco-exergies to compare the ecological effects for each scenario. The most effective measures to improve the state of the ecosystem are to reduce the subsidence depth and to decrease nutrient loading.

A comprehensive overview of lncRNA annotation resources.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

'Traffic light rules': Chromatin states direct miRNA-mediated network motifs running by integrating epigenome and regulatome.

BACKGROUND: Epigenetic marks can cooperatively regulate chromatin accessibility and in turn facilitate or impede the binding of regulatory factors to various elements, suggesting their important roles in regulatory circuits. However, it remains elusive as to how epigenetic marks cooperate in the operations of regulatory network. METHODS: Here, we systematically characterized chromatin states of 26 epigenetic marks on different elements of protein-coding genes and miRNAs. We comprehensively analyzed, by using an integrative regulatory network, how cooperation among epigenetic, transcriptional, and post-transcriptional regulations came about. RESULTS: We observed extensive cooperation of epigenetic marks on local functional elements and complex epigenetic patterns corresponding to different biological functions. By identifying the significantly epigenetic state-modified motifs, we found that multiple combinations of epigenetic states were associated with a specific type of motif. Interestingly, miRNA-mediated motifs were linked to stable epigenetic states of downstream targets. Changes in epigenetic states of downstream targets in miRNA-mediated motifs can buffer the effects of upstream regulator on target genes, suggesting that miRNA-mediated motifs require the cooperation of epigenetic marks. CONCLUSIONS: Overall, epigenetic marks are involved in the running of regulatory motifs in the way traffic lights control traffic flows and hence should be part of the architecture of complex regulatory circuits. GENERAL SIGNIFICANCE: We demonstrated a detailed analysis of the cooperation of multiple epigenetic marks and how epigenetic regulation was organized into a human regulatory network. The findings form a basis for further understanding of the complicated roles of epigenetic marks on regulatory circuits.

Molecular subtyping of skin cutaneous melanoma based on inflammatory response.

The inflammatory response plays a crucial role in determining the prognosis and therapeutic response of skin cutaneous melanoma (SKCM). However, the molecular subtypes based on the inflammatory response and their clinical significance in SKCM have not been extensively studied. Clustering analyses to identify inflammation subtypes of SKCM based on the expression levels of inflammation response gene. We identified three subtypes: Inflammation_H, Inflammation_M, and Inflammation_L, which offer a more nuanced understanding of the complex relationship between inflammation and SKCM. The Inflammation_H subtype is associated with the most favourable prognosis, and is characterised by high levels of immune infiltrates and PD-L1 expression, low levels of stemness, high differentiation, and high genomic stability. In contrast, the Inflammation_L subtype has the least favourable prognosis, with the lowest levels of immune infiltrates and PD-L1 expression, high levels of stemness, low differentiation, and low genomic stability. In addition, the Inf-score, which is a linear risk scoring model based on the expression levels of inflammatory response genes, can be a useful tool for clinicians to assess SKCM prognosis and guide therapeutic decisions. This scoring model shows promise for clinical use in predicting patient outcomes and helps clinicians tailor treatments for individual patients. In conclusion, these findings represent a significant contribution to our understanding of the molecular subtypes of SKCM based on the levels of inflammatory response genes and their potential clinical significance. However, further studies are necessary to validate these findings and explore the underlying mechanisms of different subtypes.

PTK6: An emerging biomarker for prognosis and immunotherapeutic response in clear cell renal carcinoma (KIRC).

Kidney renal clear cell carcinoma (KIRC), one of the most prevalent form of kidney carcinoma, is highly aggressive cancer known for significant immune infiltration and high mortality rates. The absence of sensitivity to traditional therapy has spurred the search for new treatments. Protein Tyrosine Kinase 6 (PTK6) is implicated in promoting cancer growth, spread, and metastasis. Our review of The Cancer Genome Atlas database revealed PTK6 overexpression in KIRC, though its specific role in this cancer type was unclear. We investigated PTK6's cancer-promoting roles in KIRC using the database and confirmed our findings with patient-derived tissues. Our analysis showed that elevated PTK6 expression is linked to worse outcomes and higher levels of immune infiltration. It also correlates positively with neo-antigens (NEO) and DNA ploidy changes in KIRC. This research delves into PTK6's role in KIRC development, suggesting PTK6 as a possible biomarker for prognosis and treatment in KIRC.

Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma.

PURPOSE: Melanoma is widely utilized as a prominent model for the development of immunotherapy, thought an inadequate immune response can occur. Moreover, the development of apoptosis-related therapies and combinations with other therapeutic strategies is impeded by the limited understanding of apoptosis's role within diverse tumor immune microenvironments (TMEs). METHODS: Here, we constructed an apoptosis-related tumor microenvironment signature (ATM) and employ multi-dimensional analysis to understand the roles of apoptosis in tumor microenvironment. We further assessed the clinical applications of ATM in nine independent cohorts, and anticipated the impact of ATM on cellular drug response in cultured cells. RESULTS: Our ATM model exhibits robust performance in survival prediction in multiple melanoma cohorts. Different ATM groups exhibited distinct molecular signatures and biological processes. The low ATM group exhibited significant enrichment in B cell activation-related pathways. What's more, plasma cells showed the lowest ATM score, highlighting their role as pivotal contributors in the ATM model. Mechanistically, the analysis of the interplay between plasma cells and other immune cells elucidated their crucial role in orchestrating an effective anti-tumor immune response. Significantly, the ATM signature exhibited associations with therapeutic efficacy of immune checkpoint blockade and the drug sensitivity of various agents, including FDA-approved and clinically utilized drugs targeting the VEGF signaling pathway. Finally, ATM was associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical "hot tumors". CONCLUSION: Our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma.

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma.

Background: Melanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment. Methods: An immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS. Results: The area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden. Conclusion: The IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis.

Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients' clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan-Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.

Exploring the association and causal effect between white blood cells and psoriasis using large-scale population data.

Introduction: Psoriasis is a chronic inflammatory disease of the skin. A few studies have shown that psoriasis is an immune-mediated disease in which multiple immune cells play crucial roles. However, the association between circulating immune cells and psoriasis remains elusive. Methods: To explore the role of circulating immune cells in psoriasis, 361,322 individuals from the UK Biobank (UKB) and 3,971 patients with psoriasis from China were included to investigate the association between white blood cells and psoriasis via an observational study. Genome-wide association studies (GWAS) and Mendelian randomization (MR) were used to evaluate the causal relationship between circulating leukocytes and psoriasis. Results: The risk of psoriasis increased with high levels of monocytes, neutrophils, and eosinophils (relative risks and 95% confidence intervals, respectively: 1.430 (1.291-1.584) for monocytes, 1.527 (1.379-1.692) for neutrophils, and 1.417 (1.294-1.551) for eosinophils). Upon further MR analysis, eosinophils showed a definite causal relationship with psoriasis (odds ratio of inverse-variance weighted: 1.386, 95% confidence intervals: 1.092-1.759) and a positive correlation with the psoriasis area and severity index (PASI) score (P = 6.6 × 10-5). The roles of the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) in psoriasis were also assessed. More than 20,000 genetic variations associated with NLR, PLR, and LMR were discovered in a GWAS analysis using the UKB data. Following adjustment for covariates in the observational study, NLR and PLR were shown to be risk factors for psoriasis, whereas LMR was a protective factor. MR results indicated that there was no causal relationship between these three indicators and psoriasis; however, NLR, PLR, and LMR correlated with the PASI score (NLR: rho = 0.244, P = 2.1 × 10-21; PLR: rho = 0.113, P = 1.4 × 10-5; LMR: rho = -0.242, P = 3.5×10-21). Discussion: Our findings revealed an important association between circulating leukocytes and psoriasis, which is instructive for the clinical practice of psoriasis treatment.

Sphingosine kinase 1 promotes tumor immune evasion by regulating the MTA3-PD-L1 axis.

Immune checkpoint blockade (ICB) exhibits considerable benefits in malignancies, but its overall response rate is limited. Previous studies have shown that sphingosine kinases (SPHKs) are critical in the tumor microenvironment (TME), but their role in immunotherapy is unclear. We performed integrative analyses including bioinformatics analysis, functional study, and clinical validation to investigate the role of SPHK1 in tumor immunity. Functionally, we demonstrated that the inhibition of SPHK1 significantly suppressed tumor growth by promoting antitumor immunity in immunocompetent melanoma mouse models and tumor T-cell cocultures. A mechanistic analysis revealed that MTA3 functions as the downstream target of SPHK1 in transcriptionally regulating tumor PD-L1. Preclinically, we found that anti-PD-1 monoclonal antibody (mAb) treatment significantly rescued tumor SPHK1 overexpression or tumor MTA3 overexpression-mediated immune evasion. Significantly, we identified SPHK1 and MTA3 as biological markers for predicting the efficacy of anti-PD-1 mAb therapy in melanoma patients. Our findings revealed a novel role for SPHK1 in tumor evasion mediated by regulating the MTA3-PD-L1 axis, identified SPHK1 and MTA3 as predictors for assessing the efficacy of PD-1 mAb treatment, and provided a therapeutic possibility for the treatment of melanoma patients.

Albendazole induces immunotherapy response by facilitating ubiquitin-mediated PD-L1 degradation.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have been increasingly used in patients with various cancers and have shown efficient therapeutic outcomes. However, fewer than 40% of cases across multiple cancer types show a response to ICIs. Therefore, developing more efficient combinational approaches with ICIs and revealing the underlying mechanisms are important goals for achieving rapid clinical transformation and application. METHODS: The effects on antitumor immunity activity of albendazole (ABZ) and the synergistic effects of ABZ with CD73 blockade were investigated in the melanoma B16F10 and the Lewis lung cancer tumor-bearing immune-competent mice models. The mechanism of ABZ reducing PD-L1 protein level through suppressing UBQLN4 was identified and validated through immunoprecipitation-mass spectrometry and molecular methods. Bioinformatics and anti-PD-1 therapy melanoma patients samples analysis were used to assess the level of UBQLN4/PD-L1 in the therapeutic efficacy of anti-PD-1 therapy. RESULTS: ABZ induces CD8+ T cell activity and subsequent immunotherapy response associated with suppression of PD-L1 protein level. Mechanistically, we revealed that ABZ promotes ubiquitin-mediated degradation of PD-L1 via suppressing UBQLN4, which was bound to PD-L1 and stabilized PD-L1 protein. Preclinically, genetic deletion or target inhibition of CD73 showed synergistic effects with ABZ treatment in the immune-competent mice models. Significantly, UBQLN4 and PD-L1 levels were higher in the tumor region of responders versus non-responders and correlated with better progression-free survival and overall survival in anti-PD-1 therapy melanoma patients. CONCLUSIONS: Our findings revealed a previously unappreciated role of ABZ in antitumor immunity by inducing ubiquitin-mediated PD-L1 protein degradation, identified predictors for assessing the therapeutic efficacy of anti-PD-1 therapy, and provided novel therapeutic possibility by combination treatment of ABZ and CD73 blockade in cancers.

Characterization of Immune-Related Alternative Polyadenylation Events in Cancer Immunotherapy.

Alternative polyadenylation (APA) is an important posttranscriptional modification commonly involved in tumor development. However, the functional roles of APA in tumor immunity remain largely unknown. Here, we performed an in-depth analysis of the 3'UTR usage of protein-coding genes and tumor immune response in 10,303 tumor samples across 31 cancer types to develop the immune-related APA event (ImmAPA) score pipeline, an integrated algorithm to characterize the regulatory landscape of APA events in cancer immunity-related pathways. Tumor-specific ImmAPAs that strongly correlate with immune cell infiltration and immune checkpoint blockade (ICB) treatment-related biomarkers were identified. Among these ImmAPAs, the top-ranking COL1A1 3'UTR usage was strongly associated with worse prognosis and tumor immune evasion. Furthermore, a machine learning approach to construct an ICB-related ImmAPA score model predicted immunotherapy efficacy. Overall, the characterization of immune-related APA that corresponds to tumor progression and tumor immunity highlights the clinical utility of APA events as potential biomarkers in cancer immunotherapy. SIGNIFICANCE: Elucidation of the landscape of immune-related alternative polyadenylation in cancer identifies alternative polyadenylation events that may play a role in immune modulation and immunotherapy efficacy.