ABSTRACT The diverse cellular milieu of the gastric tissue microenvironment plays a critical role in normal tissue homeostasis and tumor development. However, few cell culture model can recapitulate the tissue microenvironment and intercellular signaling in vitro . Here we applied an air-liquid interface method to culture primary gastric organoids that contains epithelium with endogenous stroma. To characterize the microenvironment and intercellular signaling in this model, we analyzed the transcriptomes of over 5,000 individual cells from primary gastric organoids cultured at different time points. We identified epithelial cells, fibroblasts and macrophages at the early stage of organoid formation, and revealed that macrophages were polarized towards wound healing and tumor promotion. The organoids maintained both epithelial and fibroblast lineages during the course of time, and a subset of cells in both lineages expressed the stem cell marker Lgr5 . We identified that Rspo3 was specifically expressed in the fibroblast lineage, providing an endogenous source of the R-spondin to activate Wnt signaling. Our studies demonstrate that air-liquid-interface-derived organoids provide a novel platform to study intercellular signaling and immune response in vitro .
Cancer research experiments often require the dissociation of cells from their native tissue before molecular profiling, leading to the loss of spatial tissue context. The cancer genomics research has shifted from mostly profiling tumour DNA mutations towards the current frontier of investigating individual genes and gene products in single cells and their immediate microenvironments. Information at this level with the spatial context enables us to link cancer–causing mutations and environmental factors to outcomes in cell signalling, responses and survival that will lead to solutions for diagnosing, predicting progression and treating cancers in different individuals. In this project we aim to capture tissue morphology, cancer cell types, multi-parameter protein contents of single cells in within morphologically intact tissue sections of colorectal tumours from 52 patients.
Methods
Using Hyperion Imaging Mass Cytometry (IMC), we simultaneously profiled 16 protein markers for each tissue section, capturing molecular signatures of tissue architecture, cancer cells, and immune cells. IMC uses laser beam to accurately ablate every 1µm2 of tissue region, generating data at subcellular resolution for FFPE tissue sections on a glass microscopy slide. We selected 2–8 regions of interest (ROI), each containing approximately 2098 cells. The ROI sizes range from 141µm x 500µm to 1121µm x 1309µm. We developed an analysis pipeline to process raw Hyperion imaging data (IMCtools), define cellular masks with information about nuclei, membrane, cytoplasm (using CellProfiler and Ilastick), and analyses cellular communities (HistoCAT). We also generated whole exome sequencing data and histopathological mages from sections of the same tissue blocks.
Results
By measuring 16 multiplexed proteins, for each tissue region we were able to identify up to seven cell types and preserved their spatial location within the tissue (figure 1A). Through the spatial map of the cell types to the tissue, we showed the heterogeneity of the tumour microenvironment, such as the infiltration of macrophages and B-cells to the cancer regions (figure 1A). We found cancer cells consistently marked as positive for p53 and Ki67 proteins. Moreover, we could measure the level of p53 in every individual cell within each tissue section (figure 1B). The quantitative measurement of p53 by imaging mass cytometry was correlated with the result from traditional genomic sequencing of p53 mutations and with the histopathological annotation.
Conclusions
Applying the Hyperion technology, we could acquire rich information from each of the precious cancer samples. The spatial data at single-cell resolution enabled us to assess the heterogeneity of tumour tissue by defining cell types, immune infiltration, and cancer-immune cell interaction within an undissociated tissue section. Future analysis and application of Hyperion data would allow us to find better predictors for colorectal cancer tissue with more accurate diagnosis and prognosis.
Ethics Approval
This study was approved by the Institutional Review Board (#1050191) at Intermountain Healthcare (Salt Lake City, UT USA)
Shotgun short-read sequencing methods facilitate study of the genomic content and strain-level architecture of complex microbial communities. However, existing methodologies do not capture structural differences between closely related co-occurring strains such as those arising from horizontal gene transfer and insertion sequence mobilization. Recent techniques that partition large DNA molecules, then barcode short fragments derived from them, produce short-read sequences containing long-range information. Here, we present a novel application of these short-read barcoding techniques to metagenomic samples, as well as Athena, an assembler that uses these barcodes to produce improved metagenomic assemblies. We apply our approach to longitudinal samples from the gut microbiome of a patient with a hematological malignancy. This patient underwent an intensive regimen of multiple antibiotics, chemotherapeutics and immunosuppressants, resulting in profound disruption of the microbial gut community and eventual domination by Bacteroides caccae. We significantly improve draft completeness over conventional techniques, uncover strains of B. caccae differing in the positions of transposon integration, and find the abundance of individual strains to fluctuate widely over the course of treatment. In addition, we perform RNA sequencing to investigate relative transcription of genes in B. caccae, and find overexpression of antibiotic resistance genes in our de novo assembled draft genome of B. caccae coinciding with both antibiotic administration and the appearance of proximal transposons harboring a putative bacterial promoter region. Our approach produces overall improvements in contiguity of metagenomic assembly and enables assembly of whole classes of genomic elements inaccessible to existing short-read approaches.
Recent advancements in immunotherapy are revolutionizing the landscape of clinical immuno-oncology and have significantly increased patient survival in a range of cancers. Notably, immune checkpoint blockade therapies have induced durable responses and provided tremendous clinical benefits to previously untreatable patients. However, unleashing immune system against cancer also disrupts the immunologic homeostasis and induce inflammatory responses, resulting immune-related adverse events. The precise mechanisms underlying immune-related adverse events (irAEs) remain unknown. Furthermore, it is unclear why immune checkpoint blockade therapies only induce irAEs in some patients but not the others. In this study, we systematically characterize the functional impacts of immune checkpoint blockade on the patient immune system at single-cell resolution.
Methods
The peripheral blood mononuclear cells (PBMCs) from seven cancer patients with melanoma, non-small cell lung cancer, or colon cancer (MSI-H) receiving immune checkpoint inhibitors (CPIs), i.e. anti–PD-1+anti-CTLA4 combo or anti-PD-1 single agent, were collected at three serial time points (T1, T2, and T3). During the immunotherapy, four patients developed irAEs, including colitis (2X), pneumonitis (1), hyper/hypothyroidism (1), while three patients showed no signs of irAEs. In total, we generated and characterized single cell gene expression profiles for more than 65,000 cells from 21 PBMC libraries. Furthermore, we simultaneously measured TCR and BCR from nine selected samples, thus generating a comprehensive profile of Immune repertoire upon CPIs.
Results
We systematically characterized T cells, B cells, monocytes, NK cells, and platelets from PBMCs. Both checkpoint blockade and patient comorbidity affect PBMC populations. We found that irAEs are often associated with an acute increase in monocytes and decrease in T cells. After repeated CPI treatment, PBMC populations remained relatively stable. We characterized specific subsets within each cell type that are associated with CPI treatment as well as patient clinical conditions, and identified signature genes for each subset. For example, Mucosal-Associated Invariant CD8 T cells were strongly enriched in the PBMC population of the colon cancer patient. In the melanoma patient who received anti–PD-1+anti-CTLA4 combo but didn't develop colitis, we found enriched NK cell subsets expressing chemokine such as XCL1 and CCL4. Furthermore, we found prominent T cell clonal expansion in this patient compared to the two melanoma patients who developed colitis. The administration of steroids after irAEs led to massive anti-inflammatory responses in PMBCs, often characterized by the prominent expression of AREG.
Conclusions
Our study characterized the functional impact of CPIs on patient PBMCs. Our data demonstrated that single cell RNA sequencing provides a powerful tool to dissect and identify clinically actionable biomarkers for response prediction and side effects alleviation in patients receiving immunotherapy in the era of precision medicine.
Ethics Approval
This study was approved by the Institutional Review Board (#1050678) at Intermountain Healthcare (Salt Lake City, UT USA)
Abstract The Stanford-TCGA Portal (http://genomeportal.stanford.edu/pan-tcga) enables users to easily navigate cancer genomic/proteomic data of cancer patients with their clinical information from the Cancer Genome Atlas (TCGA) project. As of 2014 January, the TCGA processed thousands of samples from over 20 cancer types and the data is publically available. This huge data set can offer many valuable insights about cancer research. However, exploring data from TCGA remains a challenge, particularly for researchers and others who lack formal bioinformatics training. With the Stanford-TCGA Portal, anyone can use a personal device such as laptops or smart phones to explore the TCGA data in regards to clinically relevant questions. For example, “What genes are associated with advanced breast cancer?”, “What is the difference in the frequency of TP53 mutations between diffuse and intestinal gastric cancers?”, or “What is the frequency of copy number deletion of APC for samples with/without PIK3CA mutations?”. While several websites such as cBio portal or UCSC cancer genomics browser was developed in order to make TCGA data more accessible to a community, there are few interactive features that enable querying of clinically-relevant phenotypic associations with drivers and modifier genes. The Stanford-TCGA portal allows users to navigate TCGA data in three different ways; i) search for clinically relevant genes/micro RNAs (miRs)/proteins by names, cancer types or clinical parameters, ii) profile genomic/proteomic changes by clinical parameters in a cancer type, or iii) test two-hit hypothesis. Thus, one can address specific questions regarding the association of cancers drivers with clinical parameters and outcome. We identify clinically relevant genes/miRs/proteins by our rigorous statistical analysis, which has been previously published, from 17 cancer types with 19 clinical parameters such as clinical stage or smoking history. Basically, elastic-net method estimates an optimal multiple linear regularized regression of the clinical parameters on the space of genomic/proteomic features. This analysis identified the set of top gene predictors of each clinical parameter in each cancer type respectively. Users can easily access the lists of these identified genes through the Stanford-TCGA Portal. In summary, the Stanford-TCGA Portal enables the cancer research community and others to fully utilize TCGA data. It provides simple yet clinically relevant information for web and mobile interface, thus one can examine queries and test hypothesis regarding genomic/proteomic alterations in cancers from any time and place. This will be an important step towards translation of genomic/proteomic data into clinics. Citation Format: HoJoon Lee, Jennifer Palm, Hanlee Ji. The Stanford-TCGA portal: An interactive web/mobile interface for exploring the clinical phenotypic relevance of specific cancer drivers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-32.
<p>(A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all tumor epithelial cells annotated by sample. (B) Heatmap depicting expression of five highest significantly expressed genes (adjusted p-value <0.05) per patient. (C) Heatmap representation of inferred single-cell CNV profiles of all tumors and reference cells. (A-C) Data from seven mCRCs.</p>
<p>(A-C) Comparisons with Pearson correlation between (A) proportions of cell lineages in five samples with both scRNA-seq and CODEX data. (B) Average expression of LGALS3 and CD68 in macrophages across all 15 patients. (C) Average expression of COL4A1 and ACTA2 in CAFs across all 15 patients.</p>
Additional file 7. Table S9. Total reads, sequencing coverage, assembled genome draft size and N50, and taxonomic annotation for all 53 isolates from stool samples of time points A, C, and D.
Abstract The gastric carcinoma cells have complex interactions with diverse cell types in the associated tumor microenvironment. The tumor microenvironment plays important roles on gastric cancer development, progression and therapy response. In this study, we leveraged high-throughput single-cell RNA sequencing (scRNAseq) to characterize the diverse cell population in gastric tumors. In total, we analyzed more than 30,000 single cells from eight gastric tumors and matched normal tissues. Cells were sequenced at an average depth of 70000 reads/cell to guarantee sufficient coverage for downstream analysis. Using graph-based cell clustering methods and known cell markers, we identified heterogenous epithelial cells (PGC+, MUC5AC+, TFF1+, EPCAM+, CDH1+), fibroblasts (ACTA2+, THY1+, COLA1+), endothelial cells (VWF+, PECAM1+), and a variety of immune cells including M1 and M2 macrophages (IL1A+, IL1B+, TNF+, MARCO+, MSR1+, CD68+), CD4 T cells (CD3D+, CD4+), CD8 T cells (CD8A+), Gamma Delta T cells (TRGC2+, TRDC+), B cells (CD79+), and plasma cells (CD19+, CD20+, IgG+). Specifically, gastric epithelial cancer cells demonstrated distinct CNVs inferred from single cell transcriptome files. We established and characterized patient derived gastric cancer organoids from single tumor cells. The organoids recapitulate CNVs found in the original gastric tumors and displayed varied degree of drug responses. We identified specific molecular features of the tumor microenvironment that modulate cell growth and immune responses. For example, we identified heterogenous myofibroblast populations with distinct chemokine secretory profiles compared to normal gastric tissue. We characterized gene signatures associated with immune checkpoints on T cells and targetable molecules in innate cells. Using cell type specific expression profiles, we were able to establish a comprehensive signaling network of tumor-tumor, tumor-stromal, stromal-stromal and stromal-tumor signaling via receptor-ligand pairing. Citation Format: Jiamin Chen, Anuja Sathe, Sue Grimes, Stephanie Greer, Billy Lau, Ann Renschler, George Poultsides, Carlos Suarez, Hanlee Ji. Comprehensive characterization of gastric cancer at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 151.
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