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Abstract Background Multiple studies have shown that tumor-associated macrophages (TAMs) promote cancer initiation and progression. However, the reprogramming of macrophages in the tumor microenvironment (TME) and the cross-talk between TAMs and malignant subclones in intrahepatic cholangiocarcinoma (iCCA) has not been fully characterized, especially in a spatially resolved manner. Deciphering the spatial architecture of variable tissue cellular components in iCCA could contribute to the positional context of gene expression containing information pathological changes and cellular variability. Methods Here, we applied spatial transcriptomics (ST) and digital spatial profiler (DSP) technologies with tumor sections from patients with iCCA. Results The results reveal that spatial inter- and intra-tumor heterogeneities feature iCCA malignancy, and tumor subclones are mainly driven by physical proximity. Tumor cells with TME components shaped the intra-sectional heterogenetic spatial architecture. Macrophages are the most infiltrated TME component in iCCA. The protein trefoil factor 3 (TFF3) secreted by the malignant subclone can induce macrophages to reprogram to a tumor-promoting state, which in turn contributes to an immune-suppressive environment and boosts tumor progression. Conclusions In conclusion, our description of the iCCA ecosystem in a spatially resolved manner provides novel insights into the spatial features and the immune suppressive landscapes of TME for iCCA.

Background: Multiple studies have shown that tumor-associated macrophages (TAMs) promote cancer initiation and progression. However, the reprogramming of macrophages in the tumor microenvironment (TME) and the cross-talk between TAMs and malignant subclones in intrahepatic cholangiocarcinoma (iCCA) has not been fully characterized, especially in a spatially resolved manner. Deciphering the spatial architecture of variable tissue cellular components in iCCA could contribute to the positional context of gene expression containing information pathological changes and cellular variability. Methods: Here, we applied spatial transcriptomics (ST) and digital spatial profiler (DSP) technologies with tumor sections from patients with iCCA. Results: The results reveal that spatial inter- and intra-tumor heterogeneities feature iCCA malignancy, and tumor subclones are mainly driven by physical proximity. Tumor cells with TME components shaped the intra-sectional heterogenetic spatial architecture. Macrophages are the most infiltrated TME component in iCCA. The protein trefoil factor 3 (TFF3) secreted by the malignant subclone can induce macrophages to reprogram to a tumor-promoting state, which in turn contributes to an immune-suppressive environment and boosts tumor progression. Conclusions: In conclusion, our description of the iCCA ecosystem in a spatially resolved manner provides novel insights into the spatial features and the immune suppressive landscapes of TME for iCCA. [ABSTRACT FROM AUTHOR]

Background: Patients who possess various histological subtypes of early‐stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early‐stage LUAD due to intratumour intricacy. Methods: We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single‐cell RNA sequencing (scRNA‐seq) was carried out on matched tumour and normal tissue. Three formalin‐fixed and paraffin‐embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP). Results: Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA‐seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = –.886; p =.033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD‐L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region. Conclusions: These findings illustrate that assessing patient histological subtypes at the single‐cell level is feasible. Additionally, tumour endothelial cells that express PD‐L1 in stage IA LUAD suppress immune‐responsive CD8+ T cells. [ABSTRACT FROM AUTHOR]

Abstract Background Patients who possess various histological subtypes of early‐stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early‐stage LUAD due to intratumour intricacy. Methods We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single‐cell RNA sequencing (scRNA‐seq) was carried out on matched tumour and normal tissue. Three formalin‐fixed and paraffin‐embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP). Results Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA‐seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = –.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD‐L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region. Conclusions These findings illustrate that assessing patient histological subtypes at the single‐cell level is feasible. Additionally, tumour endothelial cells that express PD‐L1 in stage IA LUAD suppress immune‐responsive CD8+ T cells.

Photodynamic therapy (PDT) is an emerging clinical treatment that is expected to become an important adjuvant strategy for the immunotherapeutic cancer treatment. Recently, numerous works have reported combination strategies. However, clinical data showed that the anti-tumor immune response of PDT was not lasting though existing. The immune activation effect will eventually turn to immunosuppressive effect and get aggravated at the late stage post-PDT. So far, the mechanism is still unclear, which limits the design of specific correction strategies and further development of PDT. Several lines of evidence suggest a role for TGF-β1 in the immunosuppression associated with PDT. Herein, this study systematically illustrated the dynamic changes of immune states post-PDT within the tumor microenvironment. The results clearly demonstrated that high-light-dose PDT, as a therapeutic dose, induced early immune activation followed by late immunosuppression, which was mediated by the activated TGF-β1 upregulation. Then, the mechanism of PDT-induced TGF-β1 accumulation and immunosuppression was elucidated, including the ROS/TGF-β1/MMP-9 positive feedback loop and CD44-mediated local amplification, which was further confirmed by spatial transcriptomics, as well as by the extensive immune inhibitory effect of local high concentration of TGF-β1. Finally, a TGF-β blockade treatment strategy was presented as a promising combinational strategy to reverse high-light-dose PDT-associated immunosuppression. The results of this study provide new insights for the biology mechanism and smart improvement approaches to enhance tumor photodynamic immunotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: In breast cancer, there is a high degree of variability in tumors and the surrounding tissue called the tumor microenvironment (TME). To better understand tumor biology and metastasis, as well as to predict response to cancer treatments or the course of the disease, it is important to characterize molecular diversity in the breast TME. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially analyze proteins and RNA transcripts in tumors and surrounding tissues from patients or preclinical models. Using the GeoMx DSP, protein expression and RNA transcripts in the distinct regions of a tumor can be quantified up to and including the whole transcriptome level. Herein, the GeoMx Breast Cancer Consortium presents best practices for GeoMx spatial profiling of tumors to promote the collection of high-quality data, optimization of data analysis and integration of datasets to accelerate biomarker discovery. These best practices can also be applied to any tumor type to provide information about the tumor and the TME. Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity. [ABSTRACT FROM AUTHOR]