Liver tumour immune microenvironment subtypes and neutrophil heterogeneity

Xue, Ruidong; Zhang, Qiming; Cao, Qi; Kong, Ruirui; Xiang, Xiao; Liu, Hengkang; Feng, Mei; Wang, Fangyanni; Cheng, Jinghui; Zhao, Li; Zhan, Qimin; Deng, Mi; Zhu, Jinmin; Zhang, Zemin; Zhang, Ning

doi:10.1038/s41586-022-05400-x

Cited by 313 publications

(274 citation statements)

References 55 publications

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“…Recently, with the advances of high‐resolution approaches, like scRNA‐seq, novel and crucial phenotypes of neutrophils were identified, contributing to better understanding their dual roles in TME. 43 , 59 …”

Section: Discussionmentioning

confidence: 99%

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Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment

Peng

Xiao-yue

Liu

et al. 2023

Clinical & Translational Med

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Background Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology. Methods Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks. Results Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells ( r 2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells ( r 2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p < .001). Conclusions We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, the conflicting results reported by different studies could be attributed to different phenotypes of neutrophils. Recently, with the advances of high‐resolution approaches, like scRNA‐seq, novel and crucial phenotypes of neutrophils were identified, contributing to better understanding their dual roles in TME 43,59 …”

Section: Discussionmentioning

confidence: 99%

Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment

Peng

Xiao-yue

Liu

et al. 2023

Clinical & Translational Med

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“…The analysis of both scRNAseq and spatial data involves several quality control steps, of which regression or complete removal of mitochondrial and/or ribosomal genes is common and is an inbuilt feature of the widely used pipelines Seurat (28) and Scanpy (29). The removal of these genes prior to clustering has enabled the detection of distinct cell subtypes (19) and may become a more common feature to identify sub-clusters from major cell types. As a large portion of scRNA-seq and spatial technologies centre around poly-A captured targets, detection of mitochondrial and ribosomal RNA is an integral part of the workflow (5).…”

Section: Discussionmentioning

confidence: 99%

“…For many downstream analyses such as cell type identification and cell-cell interaction analysis, only MRna information is used while reads corresponding to ribosomal and mitochondrial RNA are filtered out during quality control steps. This is exemplified by a recent study that was able to elucidate neutrophil heterogeneity in the liver tumor immune microenvironments by filtering out 1,514 genes associated with mitochondria, heat-shock protein, and ribosome prior to sub-clustering (19). While probe-based methods (7, 20) could improve the detection of MRna from scRNA-seq and spatial methods, it adds additional customisation costs to experiments (5).…”

Section: Introductionmentioning

confidence: 99%

STOmics-GenX: CRISPR based approach to improve cell identity specific gene detection from spatially resolved transcriptomics

Currenti

Qiao

Pai

et al. 2022

Preprint

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The spatial organisation of cells defines the biological functions of tissue ecosystems from development to disease. Recently, an array of technologies have been developed to query gene expression in a spatial context. These include techniques such as employing barcoded oligonucleotides, single-molecule fluorescence in situ hybridization (smFISH), and DNA nanoball (DNB)-patterned arrays. However, resolution and efficiency vary across platforms and technologies. To obtain spatially relevant biological information from spatially resolved transcriptomics, we combined the Stereo-seq workflow with CRISPRclean technology to develop the STOmics-GenX pipeline. STOmics-GenX not only allowed us to reduce genomic, mitochondrial, and ribosomal reads, but also lead to a ~2.1-fold increase in the number of detected genes when compared to conventional Stereo-seq (STOmics). Additionally, the STOmics-GenX pipeline resulted in an improved detection of cell type specific genes, thereby improving cellular annotations. Most importantly, STOmics-GenX allowed for enhanced detection of clinically relevant biomarkers such as Alpha-fetoprotein (AFP), enabling the identification of two spatially distinct subsets of hepatocytes in hepatocellular carcinoma tissue. Thereby, combining CRISPRclean technology with STOmics not only allowed improved gene detection but also paved the way for spatial precision oncology by improved detection of clinically relevant biomarkers.

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“…As the CXCR2 antagonist AZD5069 has been demonstrated to be safe in humans, this preclinical study lays the foundation for testing this novel immunotherapy-based combination in clinical trials in the well-defined NASH-HCC patient subgroup. 71 In this regard, a recent study 72 identified five tumor immune microenvironment (TIME) subtypes by performing a single-cell RNA-sequencing (scRNA-seq) analysis in samples collected from both liver cancer patients and mouse models (TP53KO C57BL/6 mice subjected to hydrodynamic tail vein injection of Myc-90ΔCtnnb1 or Myc-KRas G12D transposon vectors). These authors reported the association of TANs with poor prognosis of the myeloid-cell-enriched group.…”

Section: Immunotherapy-based Approaches In Hcc Animal Modelsmentioning

confidence: 99%

Animal Models of Hepatocellular Carcinoma: Current Applications in Clinical Research

Fornari¹,

Giovannini²,

Piscaglia³

et al. 2022

JHC

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In the last decade, relevant advances have occurred in the treatment of hepatocellular carcinoma (HCC), with novel drugs entering the clinical practice, among which tyrosine kinase inhibitors (TKIs) such as lenvatinib, cabozantinib and regorafenib, and immune checkpoint inhibitors (ICPIs) either alone or in combination with VEGF inhibitors. Clinical trials have driven the introduction of such novel molecules into the clinics but, at present, no biomarker drives the choice of first-line options, which relies only upon clinical and imaging assessment. Remarkably, clinical and imaging-based evaluations do not consider the huge heterogeneity of HCC and do not allow to realize the potential of personalized treatments. Preclinical research still does not inform the design of clinical trials, even though many animal models mimicking specific subgroups of HCC are available and might provide relevant information. Although animal models directly informing the clinical practice, such as patients-derived xenografts, are not used to help the choice of treatment in advanced HCC, however, the preclinical research can count on a wide range of valuable models. Here we will review some HCC models which might turn informative for specific questions in defined patient subgroups, and we will describe recent preclinical studies for the mechanistic evaluation of immunotherapy-based treatment approaches. To this aim, we will mainly focus on two issues: (i) HCC models informative on NAFLD-NASH HCC and (ii) HCC models helping to elucidate mechanisms underneath immunotherapy. We have chosen these two settings since they represent, respectively, the most rapidly arising cause of chronic liver disease (CLD) and HCC in western countries and the most promising therapeutic option for advanced HCC.

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Liver tumour immune microenvironment subtypes and neutrophil heterogeneity

Cited by 313 publications

References 55 publications

Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment

Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment

STOmics-GenX: CRISPR based approach to improve cell identity specific gene detection from spatially resolved transcriptomics

Animal Models of Hepatocellular Carcinoma: Current Applications in Clinical Research

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