Identification of the prognostic immune subtype in copy-number high endometrial cancer.

OBJECTIVE: The TCGA molecular subtype of endometrial cancer (EC) is widely applied, among which the copy-number high (CNH) subtype has the poorest prognosis. However, the heterogeneity of this subtype remains elusive. In this study, we aimed to identify heterogeneous immune subtypes in CNH EC and explore their prognostic significance. METHODS: We collected 60 CNH EC cases in the TCGA database and performed unsupervised cluster analysis based on the enrichment scores of immune-related gene signatures to identify immune subtypes. We described their immune characteristics and prognoses and conducted differential gene analysis and lasso regression to identify a prognostic biomarker, GZMM. For experimental validation, we performed immunohistochemical staining of GZMM in 39 p53-positive EC surgical samples. RESULTS: We defined two immune subtypes, immune-hot (IH) and immune-cold (IC), which differed in immune cell infiltration, cytokine and chemokine expression and prognosis. The IH subtype has significantly stronger immune activation than the IC subtype, showing a significant infiltration of immune effector cells and high expression of relevant chemokines, with better prognosis. Moreover, the immunohistochemical staining of GZMM in a cohort of 39 p53-positive EC surgical samples confirmed GZMM as a unique prognostic biomarker, with high expression in both tumor cells and lymphocytes predicting a better prognosis. CONCLUSION: Our study revealed heterogeneous immune subtypes in CNH EC and identified GZMM as a prognostic biomarker. The stratified classification strategy combining molecular and immune subtypes provides valuable insights for future clinical practice.

PD-1 and TIGIT coexpressing CD8 + CD103 + tissue-resident memory cells in endometrial cancer as potential targets for immunotherapy.

BACKGROUND: Immunotherapy has shown promise in treating various cancers; however, its efficacy in endometrial cancer (EC) remains suboptimal owing to the complex dynamics of the tumour immune microenvironment. This study focuses on exploring the potential of targeting the programmed cell death protein 1 gene (PD-1) and the T cell Immunoreceptor with Ig and ITIM domains gene (TIGIT) coexpressing tissue-resident memory cells in EC. METHODS: A comprehensive approach, utilizing RNA sequencing, single-cell RNA sequencing, mass cytometry, and flow cytometry, was employed to analyse the expression patterns of PD-1 and TIGIT in the EC tumor environment and to characterize the phenotypic properties of tumor-infiltrating lymphocytes (TILs), particularly tissue-resident memory (TRM) cells. Additionally, in vitro cell experiments were conducted to assess the functional impact of PD-1 and TIGIT blockade on T-cell activity. RESULTS: Our analysis identified a significant co-expression of PD-1 and TIGIT in TRM cells within the EC tumor microenvironment. These TRM cells displayed an exhausted phenotype with impaired cytotoxicity, enhanced proliferative capacity, and diminished cytotoxic activity. In vitro T-cell assays showed that a dual blockade of PD-1 and TIGIT more effectively restored T-cell functionality compared to single blockade, suggesting enhanced therapeutic potential. CONCLUSIONS: TRM cells co-expressing PD-1 and TIGIT represent potential targets for EC immunotherapy. Dual immune checkpoint blockade targeting PD-1 and TIGIT may offer an effective therapeutic strategy for EC, providing valuable insights for the development of immunotherapeutic approaches.

Comparing biweekly single-dose actinomycin D with multiday methotrexate therapy for low-risk gestational trophoblastic neoplasia (FIGO Score 0-4): study protocol for a prospective, multicentre, randomized trial.

BACKGROUND: Single-agent chemotherapy using methotrexate or actinomycin D is the first-line treatment for patients with low-risk gestational trophoblastic neoplasia. Various methotrexate-based and actinomycin D-based single-agent regimens can be used. However, there is insufficient evidence to determine the superior regimen. To guide doctors in selecting a single-agent chemotherapy regimen for patients with low-risk gestational trophoblastic neoplasia, we will compare two regimens. METHODS: We will conduct a multicentre, randomized, prospective clinical trial. Selected low-risk gestational trophoblastic neoplasia patients (FIGO score 0-4) will be randomized 1:1 to a biweekly single-dose actinomycin D group or a multiday methotrexate therapy group. The actinomycin D group will receive IV pulse actinomycin D (1.25 mg/m2) every 14 days, and the methotrexate group will receive methotrexate (50 mg) intramuscularly on days 1, 3, 5, and 7 (4 doses per cycle) and leucovorin (15 mg) intramuscularly on days 2, 4, 6, and 8. This process will be repeated every 14 days. The primary endpoints will include the complete remission rate by single-agent therapy and the overall complete remission rate. The secondary endpoints will include the duration needed to achieve complete remission after single-agent chemotherapy, number of courses needed to achieve complete remission after single-agent chemotherapy, incidence and severity of adverse effects, effects on menstrual conditions and ovarian function based on the anti-Mullerian hormone level, and patient-reported quality of life. DISCUSSION: Previous clinical trials comparing biweekly single-dose actinomycin D with multiday methotrexate therapy for treating low-risk gestational trophoblastic neoplasia patients failed to meet the expected case number. Through this multicentre study, the complete remission ratio and efficacy difference between biweekly single-dose actinomycin D and multiday methotrexate therapy will be obtained. This study will also provide the basis for formulating a preferred regimen for treating patients with low-risk gestational trophoblastic neoplasia. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04562558, Registered on 13 September 2020 (Protocol version 2020-9-24, version 1.0).

Trehalose promotes functional recovery of keratinocytes under oxidative stress and wound healing via ATG5/ATG7.

Wounds are in a stressed state, which precludes healing. Trehalose is a stress metabolite that protects cells under stress. Here, we explored whether trehalose reduces stress-induced wound tissue damage. A stress model was prepared by exposing human keratinocytes to hydrogen peroxide (H2O2), followed by trehalose treatment. Trehalose effects on expression of the autophagy-related proteins ATG5 and ATG7 and cell proliferation and migration were evaluated. For in vivo verification, a wound model was established in Sprague-Dawley rats, to measure the effects of trehalose wound-healing rate and reactive oxygen species (ROS) content. Histological changes during wound healing and trehalose's effects on ATG5 and ATG7 expression, necrosis, and apoptosis were examined·H2O2 stress increased ATG5 and ATG7 expression in vitro, but this was insufficient to prevent stress-induced damage. Trehalose further increased ATG5/ATG7 levels, which restored proliferation and increased migration by depolymerizing the cytoskeleton. However, trehalose did not exert these effects after ATG5 and ATG7 knockout. In vivo, the ROS content was higher in the wound tissue than in normal skin. Trehalose increased ATG5/ATG7 expression in wound tissue keratinocytes, reduced necrosis, depolymerized the cytoskeleton, and promoted cell migration, thereby promoting wound healing.

Immunologic Signatures across Molecular Subtypes and Potential Biomarkers for Sub-Stratification in Endometrial Cancer.

Current molecular classification approaches for endometrial cancer (EC) often employ multiple testing platforms. Some subtypes still lack univocal prognostic significance, highlighting the need for risk sub-stratification. The tumor immune microenvironment (TIME) is associated with tumor progression and prognosis. We sought to investigate the feasibility of classifying EC via DNA sequencing and interrogate immunologic signatures and prognostic markers across and within subtypes, respectively. Formalin-fixed paraffin-embedding (FFPE) samples from 50 EC patients underwent targeted DNA and RNA sequencing, and multiplex immunofluorescence assay for TIME. DNA sequencing classified 10%, 20%, 52%, and 18% of patients into the subtype of POLE-mutant, microsatellite instability-high (MSI-H), TP53-wt, and TP53-mutant. POLE-mutant tumors expressed the highest T-effector and IFN-γ signature and the lowest innate anti-PD-1 resistance signature among subtypes. TP53-wt revealed a converse enrichment trend for these immunologic signatures. Survival analyses using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) dataset identified associations of CCR5 (hazard ratio (HR) = 0.71, p = 0.035), TNFRSF14 (HR = 0.58, p = 0.028), and IL-10 (HR = 2.5, p = 0.012) with overall survival within MSI-H, TP53-mutant, and TP53-wt subtype, respectively. A TIME comparison between the sub-stratified subgroups of our cohort revealed upregulated tumor infiltration of immune cells in the low-risk subgroups. Our study demonstrates that targeted DNA sequencing is an effective one-stop strategy to classify EC. Immunomodulatory genes may serve as prognostic markers within subtypes.

Single-Cell Profiling of the Immune Atlas of Tumor-Infiltrating Lymphocytes in Endometrial Carcinoma.

Endometrial carcinoma (EC) is a gynecological malignancy with a high incidence; however, thorough studies on tumor-infiltrating lymphocyte (TIL) populations in EC are lacking. We aimed to map the immune atlas of TILs in type I EC via single-cell RNA sequencing (scRNA-seq), mass cytometry and flow cytometry analysis. We found that natural killer (NK) cells and CD8+ T lymphocytes were the major components of TILs in EC patients. We first identified three transcriptionally distinct NK cell subsets, which are likely to possess diverse anti-tumor functions. Additionally, CD103+ cells substantially contributed to the CD8+ T cell population. The signature gene expression of CD103+ CD8+ T cells indicated the tissue residency, immunological memory, and exhaustion properties of this cell subset, which were defined as tissue-resident memory T cells (TRM cells). Moreover, based on scRNA-seq and mass cytometry analysis, we first identified the intrinsic heterogeneity of CD103+ CD8+ T cells that were thought to have a distinct cytotoxicity, cell adhesion and exhaustion status functions. Collectively, distinct subsets of NK cells were found and might shed light on future investigations. CD103+ CD8+ T cell population may be an important immunotherapeutic target in EC and targeting this cell population with combined immunosuppressive therapy might improve the efficacy of immunotherapy for EC.

Auto-Metric Graph Neural Network Based on a Meta-Learning Strategy for the Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cognitive disorder. In recent years, many computer-aided diagnosis techniques have been proposed for AD diagnosis and progression predictions. Among them, graph neural networks (GNNs) have received extensive attention owing to their ability to effectively fuse multimodal features and model the correlation between samples. However, many GNNs for node classification use an entire dataset to construct a large fixed-graph structure, which cannot be used for independent testing. To overcome this limitation while maintaining the advantages of the GNN, we propose an auto-metric GNN (AMGNN) model for AD diagnosis. First, a metric-based meta-learning strategy is introduced to realize inductive learning for independent testing through multiple node classification tasks. In the meta-tasks, the small graphs help make the model insensitive to the sample size, thus improving the performance under small sample size conditions. Furthermore, an AMGNN layer with a probability constraint is designed to realize node similarity metric learning and effectively fuse multimodal data. We verified the model on two tasks based on the TADPOLE dataset: early AD diagnosis and mild cognitive impairment (MCI) conversion prediction. Our model provides excellent performance on both tasks with accuracies of 94.44% and 87.50% and median accuracies of 94.19% and 86.25%, respectively. These results show that our model improves flexibility while ensuring a good classification performance, thus promoting the development of graph-based deep learning algorithms for disease diagnosis.