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Accurately assessing and quantifying immune competence in cancer patients remains a major challenge in tumor immunology. Traditional intratumoral immune profiling, such as tissue pathology and tissue-based cytometry techniques, faces significant challenges due to difficulties in tissue sampling, spatial heterogeneity, and technical limitations. In contrast, peripheral blood immune profiling is a more practical and reproducible approach, providing valuable insights into systemic immune status. This article introduces a novel immune structural model, inspired by protein structural hierarchy, to classify immune components into three hierarchical levels: primary, secondary, and tertiary immune structures. We hypothesize that this model can provide a systematic framework for constructing an immune scoring system (ISS) that integrates multi-dimensional immune information from flow cytometry, cytokine profiling, and immune checkpoint molecule assessments. The proposed model offers a new way to assess immune status and could serve as a valuable tool for clinical personalized treatment and prognostic evaluation.

Background Previous studies have reported that the tumor immune microenvironment (TIME) was associated with the prognosis of lung cancer patients and the efficacy of immunotherapy. However, given the significant challenges in obtaining specimens of brain metastases (BrMs), few studies explored the correlation between the TIME and the prognosis in patients with BrMs from lung adenocarcinoma (LUAD). Methods Transcript profiling of archival formalin-fixed and paraffin-embedded specimens of BrMs from 70 LUAD patients with surgically resected BrMs was carried out using RNA sequencing. An immune scoring system, the green-yellow module score (GYMS), was developed to predict prognosis and immune characteristics in both BrMs and primary LUAD using Weighted Correlation Network analysis (WGCNA) and GSVA analysis. We comprehensively evaluated the immunological role of GYMS based on gene expression profile of LUAD BrMs by systematically correlating GYMS with immunological characteristics and immunotherapy responsiveness in the BrMs. Immunohistochemistry was applied for validation. Results We found that the high-GYMS group had better clinical prognosis and inflamed immune landscape including high infiltrations of various immune cells, increased immunomodulatory expression, and enriched immune-related pathways by using RNA-seq and immunohistochemical analysis. Low-GYMS group presented a lacked immune infiltration characteristic. Besides, the high-GYMS group had lower TIDE score and higher T-cell inflamed score than low-GYMS group. The GYMS has been validated in independent BrMs cohorts and primary NSCLC cohort treated with anti-PD-1/PD-L1, showing strong reproducibility and stability in both primary LUAD and BrMs. In addition, we construct a GYMS-related risk signature for patients with LUAD BrMs to predict prognosis. Conclusions We identified two immune-related subtypes which used to estimate prognosis and immune characteristics and developed a reliable GYMS-related risk signature in LUAD BrMs. These results will enhance the understanding of the immune microenvironment in LUAD BrMs and lay the theoretical foundation for the development of personalized therapies for LUAD patients with BrMs.

Background Determination of programmed death-ligand 1 (PD-L1) protein expression level in tumor cells and tumor-associated immune cells is critical for identifying patients eligible for immunotherapy. PD-L1 manual scoring algorithms can generally be divided into two categories: cell counting and visual estimation. Cell counting can be time-consuming and is not in sync with pathology practice, which classically uses a Gestalt approach based on pattern recognition and visual estimation. In this study, we introduce the Tumor Area Positivity (TAP) score, which is a novel, straightforward method for scoring tumor cells and immune cells together using visual estimation. Methods To demonstrate the reproducibility of TAP scoring among pathologists, between- and within-reader precision studies were performed both within (internal) and outside of (external) our organization. We also compared the TAP score to the Combined Positive Score (CPS), which is based on cell counting, for concordance and time efficacy. Results The average positive agreement, average negative agreement, and overall percent agreement between and within readers were all above 85% for both internal and combined external reader precision studies. TAP score had high concordance rate at 1% and 5% cutoff compared with CPS at cutoff 1: positive percent agreement, negative percent agreement, and overall percent agreement were all above 85%. Conclusions Our study showed the TAP scoring method to be straightforward, significantly less time-consuming, and highly reproducible with a high concordance rate between TAP score and CPS.

Background Immunogenic cell death (ICD) has been identified as regulated cell death, which is sufficient to activate the adaptive immune response. This study aimed to research ICD-related genes and create a gene model to predict pancreatic ductal adenocarcinoma (PAAD) patients’ prognosis. Methods The RNA sequencing and clinical data were downloaded from the TGCA and GEO databases. The PAAD samples were classified into two subtypes based on the expression levels of ICD-related genes using consensus clustering. Based on the differentially expressed genes (DEGs), a prognostic scoring model was constructed using LASSO regression and Cox regression, and the scoring model was used to predict the prognosis of PAAD patients. Moreover, colony formation assay was performed to confirm the prognostic value of those genes. Results We identified two ICD cluster by consensus clustering, and found that the the ICD-high group was closely associated with immune-hot phenotype, favorable clinical outcomes. We established an ICD-related prognostic model which can predict the prognosis of pancreatic ductal adenocarcinoma. Moreover, depletion of NT5E, ATG5, FOXP3, and IFNG inhibited the colony formation ability of pancreatic cancer cell. Conclusion We identified a novel classification for PAAD based on the expression of ICD-related genes, which may provide a potential strategy for therapeutics against PAAD.

The epithelial-to-mesenchymal transition (EMT) is a reversible process that may interact with tumour immunity through multiple approaches. There is increasing evidence demonstrating the interconnections among EMT-related processes, the tumour microenvironment, and immune activity, as well as its potential influence on the immunotherapy response. Long non-coding RNAs (lncRNAs) are emerging as critical modulators of gene expression. They play fundamental roles in tumour immunity and act as promising biomarkers of immunotherapy response. However, the potential roles of lncRNA in the crosstalk of EMT and tumour immunity are still unclear in sarcoma. We obtained multi-omics profiling of 1440 pan-sarcoma patients from 19 datasets. Through an unsupervised consensus clustering approach, we categorised EMT molecular subtypes. We subsequently identified 26 EMT molecular subtype and tumour immune-related lncRNAs (EILncRNA) across pan-sarcoma types and developed an EILncRNA signature-based weighted scoring model (EILncSig). The EILncSig exhibited favourable performance in predicting the prognosis of sarcoma, and a high-EILncSig was associated with exclusive tumour microenvironment (TME) characteristics with desert-like infiltration of immune cells. Multiple altered pathways, somatically-mutated genes and recurrent CNV regions associated with EILncSig were identified. Notably, the EILncSig was associated with the efficacy of immune checkpoint inhibition (ICI) therapy. Using a computational drug-genomic approach, we identified compounds, such as Irinotecan that may have the potential to convert the EILncSig phenotype. By integrative analysis on multi-omics profiling, our findings provide a comprehensive resource for understanding the functional role of lncRNA-mediated immune regulation in sarcomas, which may advance the understanding of tumour immune response and the development of lncRNA-based immunotherapeutic strategies for sarcoma.

Background The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. Methods The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. Results After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P < 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy. Conclusion A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

Background The adaptive CRISPR-Cas immune system stores sequences from past invaders as spacers in CRISPR arrays and thereby provides direct evidence that links invaders to hosts. Mapping CRISPR spacers has revealed many aspects of CRISPR-Cas biology, including target requirements such as the protospacer adjacent motif (PAM). However, studies have so far been limited by a low number of mapped spacers in the database. Results By using vast metagenomic sequence databases, we map approximately one-third of more than 200,000 unique CRISPR spacers from a variety of microbes and derive a catalog of more than two hundred unique PAM sequences associated with specific CRISPR-Cas subtypes. These PAMs are further used to correctly assign the orientation of CRISPR arrays, revealing conserved patterns between the last nucleotides of the CRISPR repeat and PAM. We could also deduce CRISPR-Cas subtype-specific preferences for targeting either template or coding strand of open reading frames. While some DNA-targeting systems (type I-E and type II systems) prefer the template strand and avoid mRNA, other DNA- and RNA-targeting systems (types I-A and I-B and type III systems) prefer the coding strand and mRNA. In addition, we find large-scale evidence that both CRISPR-Cas adaptation machinery and CRISPR arrays are shared between different CRISPR-Cas systems. This could lead to simultaneous DNA and RNA targeting of invaders, which may be effective at combating mobile genetic invaders. Conclusions This study has broad implications for our understanding of how CRISPR-Cas systems work in a wide range of organisms for which only the genome sequence is known.

Granulomatous lymphocytic interstitial lung disease (GLILD) is present in about 20% of patients with common variable immunodeficiency disorders (CVID). GLILD is characterized by nodules, reticulation, and ground-glass opacities on CT scans. To date, large cohort studies that include sensitive CT outcome measures are lacking, and severity of structural lung disease remains unknown. The aim of this study was to introduce and compare two scoring methods to phenotype CT scans of GLILD patients. Patients were enrolled in the \"Study of Interstitial Lung Disease in Primary Antibody Deficiency\" (STILPAD) international cohort. Inclusion criteria were diagnosis of both CVID and GLILD, as defined by the treating immunologist and radiologist. Retrospectively collected CT scans were scored systematically with the Baumann and Hartmann methods. In total, 356 CT scans from 138 patients were included. Cross-sectionally, 95% of patients met a radiological definition of GLILD using both methods. Bronchiectasis was present in 82% of patients. Inter-observer reproducibility (intraclass correlation coefficients) of GLILD and airway disease were 0.84 and 0.69 for the Hartmann method and 0.74 and 0.42 for the Baumann method. In both the Hartmann and Baumann scoring method, the composite score GLILD was reproducible and therefore might be a valuable outcome measure in future studies. Overall, the reproducibility of the Hartmann method appears to be slightly better than that of the Baumann method. With a systematic analysis, we showed that GLILD patients suffer from extensive lung disease, including airway disease. Further validation of these scoring methods should be performed in a prospective cohort study involving routine collection of standardized CT scans. https://www.drks.de, identifier DRKS00000799.

The Notch pathway is a highly conserved signaling pathway involved in the regulation of cell proliferation and differentiation. However, the relationships between Notch pathway-related genes (NPRGs), immunosuppression, and immunotherapy resistance of hepatocellular carcinoma (HCC) remain unclear. Gene expression data and clinical information were extracted from GSE14520, GSE36376, GSE76427, LIRI-JP, TCGA-LIHC, GSE20140, GSE27150, and IMvigor210 datasets. A consensus clustering analysis based on 10 NPRGs was performed to determine the molecular subtypes, and then a notchScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, a notchScore was constructed to quantify the Notch index of each patient with HCC. Next, we investigated the correlations between the clinical characteristics and the notchScore using logistic regression. Furthermore, multivariate Cox analysis showed that a high notchScore was an independent predictor of poor overall survival (OS) in the TCGA and LIRI-JP datasets and was associated with higher pathological stages. Additionally, a high notchScore was associated with higher immune cells, higher ESTIMATE score, higher immune score, higher stromal score, higher immune checkpoint, and lower tumor purity, which was consistent with the “immunity tidal model theory.” Importantly, a high notchScore was sensitive to immunotherapy. Additionally, GSEA indicated that several GO and KEGG items associated with apoptosis, immune-related pathways, and cell cycle signal pathways were significantly enriched in the high notchScore phenotype pathway. Our findings propose that a high notchScore is a prognostic biomarker and correlates with immune infiltration and sensitivity to immunotherapy in HCC.

Indoleamine 2,3-dioxygenase 1 (IDO1) facilitates tumor progression by catabolizing tryptophan into kynurenine (Kyn). While KHK2455, a selective IDO1 inhibitor, reduced Kyn levels in mouse tumors and plasma, it did not exert the expected antitumor activity in a mouse melanoma model. However, when combined with programmed death-ligand 1 (PD-L1) blockade, KHK2455 demonstrated enhanced antitumor effects compared with PD-L1 blockade alone. This study investigated the effects of IDO1 inhibition on the tumor microenvironment and mechanisms underlying the enhanced antitumor effects of combining IDO1 inhibition with PD-L1 blockade. PD-L1 blockade upregulated the pathways related to adaptive immunity including T-helper cells type 1 and 2 (Th1 and Th2) rather than innate immunity. On the other hand, IDO1 inhibition upregulated genes and pathways associated with innate immunity, such as natural killer cells, neutrophils, and macrophages. Furthermore, the combination of IDO1 inhibition and PD-L1 blockade upregulated both adaptive and innate responses more than PD-L1 blockade alone. These findings elucidate the differential effects of the two therapies on the immune system and provide valuable insights for future treatment strategies targeting IDO1.