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STING agonist inflames the cervical cancer immune microenvironment and overcomes anti-PD-1 therapy resistance.
BACKGROUND: Cervical cancer poses a significant global threat to women's health. However, current therapeutic interventions, such as radiotherapy, chemotherapy, surgical resection, and immune checkpoint inhibitors, face limitations in the advanced stages of the disease. Given the immunosuppressive microenvironment in cervical cancer, it is imperative to explore novel perspectives. In this regard, STING agonists have emerged as promising candidates. METHODS: The expression profiles and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Prognostic analysis of STING downstream genes (CCL5, CXCL9, CXCL10) and immune infiltration analysis were conducted using Kaplan-Meier Plotter, ESTIMATE, and deconvo_CIBERSOR. Single-cell RNA-seq (scRNA-seq) analysis was conducted to evaluate the potential of MSA-2 in cervical cancer treatment employing SingleR, chi-squared test, and Gene Set Enrichment Analysis (GSEA). Cellular interaction analysis utilized the CellChat package to assess the potentiation of cellular interaction following MSA-2 administration. Murine tumor models involving U14 and TC-1, were conducted, and the IF of tissue was subsequently conducted to assess the tumor microenvironment status after treatment. RESULTS: Prognosis in cervical cancer correlated with elevated expression of STING downstream genes, indicating prolonged survival and reduced recurrence. These genes positively correlated with immune infiltration, influencing stromal scores, immune scores, and estimate scores. Specific immune cell populations, including CD8+ T cells, M1-type macrophages, NK cells, and T follicular helper cells, were associated with STING downstream genes. scRNA-seq in a classic immune-excluded model revealed that MSA-2 exerts priming and activating functions on vital components within TME, and intensifies their intercellular communications. The in vivo assay ultimately demonstrated that MSA-2, either as a standalone treatment or in combination with anti-PD-1, effectively suppressed the growth of subcutaneous cervical tumors. Moreover, the combination strategy significantly augmented efficacy compared to anti-PD-1 monotherapy by eliciting a robust antitumor immune response. CONCLUSION: This study highlights the pivotal role of the STING pathway and the potential of MSA-2 in reshaping the immune microenvironment in cervical cancer. Combining MSA-2 with immune checkpoint inhibitors presents a transformative approach, holding promise for improved prognosis. Further investigations are warranted to explore the broader immune landscape and potential long-term effects of MSA-2 in cervical cancer treatment.
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Pan-cancer profiling links C1orf50 to DNA repair and immune modulation in ovarian cancer.
BACKGROUND: C1orf50 encodes a small, evolutionarily conserved protein, the function of which remains unclear. Its significance across various human cancers, particularly its specific role in ovarian cancer within an immunogenomic context, is not yet fully understood. Utilizing The Cancer Genome Atlas and single-cell RNA sequencing (scRNA-seq) public datasets, we conducted a comprehensive profiling of C1orf50 across multiple cancer types, with a particular focus on ovarian cancer, to investigate its associations with copy-number status, genomic instability, tumor programs, and the immune microenvironment. RESULTS: Across cancer types, copy-number gain or amplification of C1orf50 was most frequent in ovarian cancer and closely tracked with higher messenger RNA levels. Higher C1orf50 expression was associated with a greater tumor mutational burden and homologous recombination deficiency, as indicated by gene-set patterns that suggested heightened cell-cycle and cellular stress responses accompanied by reduced oxidative phosphorylation, enrichment of regulatory T cells, and depletion of resting memory CD4 T cells. In ovarian cancer, focal events at chromosome 1p34.2 were accompanied by stepwise increases in C1orf50 expression by clinical stage and were linked to higher tumor mutational burden, homologous recombination deficiency, and greater loss of heterozygosity, together with more frequent gene alterations in BRCA1 or BRCA2. Immune composition clustered into profiles consistent with an immunosuppressive context in tumors with higher C1orf50 expression. The scRNA-seq data further revealed that cancer cells enhanced immune-suppressive interactions with various immune cell populations and diminished antigen-presentation signals. Analyses of genomic instability in ovarian cancer suggested mutational processes compatible with base-substitution patterns associated with cytidine deaminase activity and with insertion-deletion patterns characteristic of homologous recombination failure, while transcript-level patterns pointed to a broad downshift of canonical DNA repair activity with apparent compensatory adjustments in related pathways rather than a uniform change in any single pathway. CONCLUSIONS: The overexpression of C1orf50 characterizes an aggressive immunogenomic phenotype in ovarian cancer, distinguished by genomic instability, impaired DNA repair mechanisms, and extensive immunosuppression. These findings indicate that C1orf50 warrants consideration as a potential biomarker and a prospective target for therapeutic investigation. Furthermore, they advocate for the progression to prospective validation and functional studies to ascertain its clinical significance.
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Cell type matching in single-cell RNA-sequencing data using FR-Match.
Reference cell atlases powered by single cell and spatial transcriptomics technologies are becoming available to study healthy and diseased tissue at single cell resolution. One important use of these data resources is to compare cell types from new dataset with cell types in the reference atlases to evaluate their phenotypic similarities and differences, for example, for identifying novel cell types under disease conditions. For this purpose, rigorously-validated computational algorithms are needed to perform these cell type matching tasks that can compare datasets from different experiment platforms and sample types. Here, we present significant enhancements to FR-Match (v2.0)-a multivariate nonparametric statistical testing approach for matching cell types in query datasets to reference atlases. FR-Match v2.0 includes a normalization procedure to facilitate cross-platform cluster-level comparisons (e.g., plate-based SMART-seq and droplet-based 10X Chromium single cell and single nucleus RNA-seq and spatial transcriptomics) and extends the pipeline to also allow cell-level matching. In the use cases evaluated, FR-Match showed robust and accurate performance for identifying common and novel cell types across tissue regions, for discovering sub-optimally clustered cell types, and for cross-platform and cross-sample cell type matching.
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DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.
Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor microenvironments. Despite challenges due to heterogeneity within and among tumors, our innovative deep learning-based approach, DeSide, shows exceptional accuracy in estimating the proportions of 16 distinct cell types and subtypes within solid tumors. DeSide integrates biological pathways and assesses noncancerous cell types first, effectively sidestepping the issue of highly variable gene expression profiles (GEPs) associated with cancer cells. By leveraging scRNA-seq data from six cancer types and 185 cancer cell lines across 22 cancer types as references, our method introduces distinctive sampling and filtering techniques to generate a high-quality training set that closely replicates real tumor GEPs, based on The Cancer Genome Atlas (TCGA) bulk RNA-seq data. With this model and high-quality training set, DeSide outperforms existing methods in estimating tumor purity and the proportions of noncancerous cells within solid tumors. Our model precisely predicts cellular compositions across 19 cancer types from TCGA and proves its effectiveness with multiple additional external datasets. Crucially, DeSide enables the identification and analysis of combinatorial cell type pairs, facilitating the stratification of cancer patients into prognostically significant groups. This approach not only provides deeper insights into the dynamics of tumor biology but also highlights potential therapeutic targets by underscoring the importance of specific cell type or subtype interactions.
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Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.
Transcriptomic differences between individuals and sexes are well-documented across tissues, affecting cell-type identity. Single-cell atlases often have skewed sex ratios or limited donor diversity, potentially leading to sex- or donor-biased annotations using automatic classification methods. This might cause models to exacerbate existing biases from their training data. We investigated this by varying the sex ratio in a training set and assessing a cell-type classifier's performance on fixed single-sex test sets. To separate sex and donor effects, we ran the experiment with and without donor information. We found that differences between donors negatively impact classification, as evidenced by poorer performance on unseen donors. However, this is not primarily due to sex bias; the model classified male and female cells similarly well, even when trained on highly sex-skewed data. We also found that large sex-based abundance differences between cell types can confound performance interpretation, creating apparent sex-biased patterns. Our findings suggest that atlas creators and classifier developers must carefully consider donor-specific biases in scRNA-seq data.
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Loss of Glutathione-S-Transferase Theta 2 (GSTT2) Modulates the Tumor Microenvironment and Response to BCG Immunotherapy in a Murine Orthotopic Model of Bladder Cancer.
Loss of the glutathione-S-transferases Theta 2 (Gstt2) expression is associated with an improved response to intravesical Mycobacterium bovis, Bacillus Calmette-Guérin (BCG) immunotherapy for non-muscle-invasive bladder cancer (NMIBC) patients who receive fewer BCG instillations. To delineate the cause, Gstt2 knockout (KO) and wildtype (WT) C57Bl/6J mice were implanted with tumors before treatment with BCG or saline. RNA was analyzed via single-cell RNA sequencing (scRNA-seq) and real-time polymerase chain reaction (RT-PCR). BCG induced PD-L1 expression in WT mice bladders, while pro-inflammatory TNF-α was upregulated in KO bladders. ScRNA-seq analysis showed that Gstt2 WT mice bladders had a higher proportion of matrix remodeling fibroblasts, M2 macrophages, and neuronal cells. In KO mice, distinct tumor cell types, activated fibroblasts, and M1 macrophages were enriched in the bladders. In WT bladders, the genes expressed supported tumorigenesis and immunosuppressive PD-L1 expression. In contrast, Gstt2 KO bladders expressed genes involved in inflammation, immune activation, and tumor suppression. An 11-gene signature (Hmga2, Peak 1, Kras, Slc2a1, Ankfn1, Ahnak, Cmss1, Fmo5, Gphn, Plec, Gstt2), derived from the scRNA-seq analysis predicted response in NMIBC patients (The Cancer Genome Atlas (TCGA) database). In conclusion, our results indicate that patients with WT Gstt2 may benefit from anti-PD-L1 checkpoint inhibition therapy.
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A comprehensive analysis of the potential role of necroptosis in hepatocellular carcinoma using single-cell RNA Seq and bulk RNA Seq.
PURPOSE: Necroptosis plays an essential role in oncogenesis and tumor progression in hepatocellular carcinoma (HCC). This study aimed to investigate the role of necroptosis in the development and progression of HCC. Specifically, we constructed a prognostic prediction model using necroptosis-associated genes (NAGs) to predict patient outcomes. METHODS: Using data from The Cancer Genome Atlas (TCGA) database, we analyzed gene expression and clinical data. We identified a 5-gene model associated with NAGs and explored genetic features and immune cell infiltration using the CIBERSORT algorithm. In addition, we conducted single-cell RNA sequencing to investigate the potential role of necroptosis in HCC. RESULTS: We constructed a 5-gene prognostic model based on NAGs that demonstrated excellent predictive accuracy in both training and validation sets. Using multifactorial cox regression analysis, we confirmed the risk score derived from the model as an independent predictor of prognosis, surpassing other clinical characteristics. Patients with high risk scores had significantly worse prognosis than those with low risk scores. To enhance the clinical utility of the necroptosis score, we constructed an accurate nomogram. Additionally, we compared metabolic pathway and immune microenvironment differences between HCC tumors with high and low risk scores. Our single-cell RNA sequencing analyses revealed that necroptosis in HCC was primarily associated with a specific subset of macrophages. CONCLUSIONS: Our study revealed the presence of two distinct necroptosis subtypes in HCC and developed a robust prognostic model with exceptional predictive accuracy. We observed significantly higher infiltration of M0 macrophages in the high-risk group. We propose that rescuing cytochrome c metabolism in HCC could serve as a potential therapeutic strategy. Furthermore, at a single-cell resolution, our analysis identified myeloid cells as the primary cells exhibiting necroptosis. Specifically, macrophages expressing CD5L, CETP, and MARCO, which may belong to a subset of tissue-resident macrophages, were found to be highly susceptible to necroptosis. These findings suggest the involvement of this specific macrophage subset in potential antitumor therapies. Our study provides novel insights into predicting patient prognosis and developing personalized therapeutic approaches for HCC.
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Single-cell transcriptomics of the ocular anterior segment: a comprehensive review.
Elucidating the cellular and genetic composition of ocular tissues is essential for uncovering the pathophysiology of ocular diseases. Since the introduction of single-cell RNA sequencing (scRNA-seq) in 2009, vision researchers have performed extensive single-cell analyses to better understand transcriptome complexity and heterogeneity of ocular structures. This technology has revolutionized our ability to identify rare cell populations and to make cross-species comparisons of gene expression in both steady state and disease conditions. Importantly, single-cell transcriptomic analyses have enabled the identification of cell-type specific gene markers and signalling pathways between ocular cell populations. While most scRNA-seq studies have been conducted on retinal tissues, large-scale transcriptomic atlases pertaining to the ocular anterior segment have also been constructed in the past three years. This timely review provides vision researchers with an overview of scRNA-seq experimental design, technical limitations, and clinical applications in a variety of anterior segment-related ocular pathologies. We review open-access anterior segment-related scRNA-seq datasets and illustrate how scRNA-seq can be an indispensable tool for the development of targeted therapeutics.
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Ensemble learning for classifying single-cell data and projection across reference atlases.
SUMMARY: Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. AVAILABILITY AND IMPLEMENTATION: https://github.com/diazlab/ELSA. CONTACT: aaron.diaz@ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Rediscovering publicly available single-cell data with the DISCO platform.
Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis.