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282 results for “Tumor infiltrating cells”
Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data
<p>Processed Datasets for:</p> <p>EGA Study ID: EGAS00001006141</p> <p>EGA Dataset ID: EGAD00001008662</p> <p> </p> <p>Find processed files and arrow files</p> <p> </p> <p>Abstract:</p> <p>Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8<sup>+</sup> TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.</p>
Pan-cancer analyses refine the single-cell portrait of tumor-infiltrating dendritic cells
<p>This is the dataset for "Pan-cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells".</p> <p>File "panDC_all_h5ad.gz" contains processed expression .h5ad data.</p> <p>File "panDC_metadata.csv" contains the meta data for this study.</p>
Response to primary chemoradiotherapy of locally advanced oropharyngeal carcinoma is determined by the degree of cytotoxic T cell infiltration within tumor cell aggregates
<p><strong><span>Background</span></strong><span>: Effective anti-tumor immune responses are mediated by T cells and require organized, spatially coordinated interactions within the tumor microenvironment (TME). Understanding coordinated T-cell behavior and deciphering mechanisms of radiotherapy resistance mediated by tumor stem cells will advance risk stratification of oropharyngeal cancer (OPSCC) patients treated with primary chemoradiotherapy (RCTx). </span></p> <p><span><strong>Methods</strong>:</span> <span>To determine the role of CD8 T cells (CTL) and tumor stem cells in response to RCTx, we employed multiplex immunofluorescence stains on pre-treatment biopsy specimens from 86 advanced OPSCC patients and correlated these quantitative data with clinical parameters. Multiplex stains were analyzed at the single-cell level using QuPath and spatial coordination of immune cells within the TME was explored using the R-package Spatstat. </span></p> <p><span><strong>Results</strong>:</span><span> Our observations demonstrate that a strong CTL-infiltration into the epithelial tumor compartment (HR for overall survival, OS: 0.35; p<0.001) and the expression of PD-L1 on CTL (HR: 0.36; p<0.001) were both associated with a significantly better response and survival upon RCTx. As expected, p16 expression was a strong predictor of improved OS (HR: 0.38; p=0.002) and correlated with overall CTL infiltration (</span><span>r: 0.358, p<0.001). By contrast, tumor cell proliferative activity, expression of the tumor stem cell marker CD271 and overall CTL infiltration, regardless of the affected compartment, were not associated with response or survival. </span></p> <p><span><strong>Conclusion</strong>: </span><span>In this study, we could demonstrate the clinical relevance of the spatial organization and the phenotype of CD8 T cells within the TME. In particular, we found that the infiltration of CD8 T cells specifically into the tumor cell compartment was an independent predictive marker for response to chemoradiotherapy, which was strongly associated with p16 expression. Meanwhile, tumor cell proliferation and the expression of stem cell markers showed no independent predictive effect in response to RCTx and require further study.</span></p>
Prospective Randomized Study of Cell Transfer Therapy for Metastatic Melanoma Using Tumor Infiltrating Lymphocytes Plus IL-2 Following Non-Myeloablative Lymphocyte Depleting Chemo Regimen Alone or in
ClinicalTrials.gov study NCT01319565. IPD Sharing: YES. Countries: 1. Publications: 6.
Nivolumab and Tumor Infiltrating Lymphocytes (TIL) in Advanced Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT03215810. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Circulating T Cells and Tumor Infiltrating Lymphocytes (TILs) During / After Pre-Surgery Chemotherapy in Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT01820754. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of LN-145/LN-145-S1 Autologous Tumor Infiltrating Lymphocytes in the Treatment of Squamous Cell Carcinoma of the Head & Neck
ClinicalTrials.gov study NCT03083873. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cell Therapy for Metastatic Melanoma Using CD8 Enriched Tumor Infiltrating Lymphocytes
ClinicalTrials.gov study NCT01236573. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Response to primary chemoradiotherapy of locally advanced oropharyngeal carcinoma is determined by the degree of cytotoxic T cell infiltration within tumor cell aggregates
Open the record for dataset details and reuse information.
utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR
<p><strong>Introduction</strong></p> <p>The original intent of assembling a data set of publicly-available tumor-infiltrating T cells (TILs) with paired TCR sequencing was to expand and improve the <a href="https://github.com/ncborcherding/scRepertoire">scRepertoire</a> R package. However, after some discussion, we decided to release the data set for everyone, a complete summary of the sequencing runs and the sample information can be found in the meta data of the Seurat object. This repository is the 4th version of the data, with addition of cells and changes to the workflow. </p> <p><strong>Methods</strong></p> <p><em>Single-Cell Data Processing</em></p> <p>The filtered gene matrices output from Cell Ranger align function from individual sequencing runs (10x Genomics, Pleasanton, CA) loaded into the R global environment. For each sequencing run cell barcodes were appended to contain a unique prefix to prevent issues with duplicate barcodes. The results were then ported into individual Seurat objects (<a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a>), where the cells with > 10% mitochondrial genes and/or 2.5x natural log distribution of counts were excluded for quality control purposes. At the individual sequencing run level, doublets were estimated using the scDblFinder (v1.4.0) R package.</p> <p><em>Annotation of Cells</em></p> <p>Automatic annotation was performed using the singler (v1.4.1) R package (<a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a>) with the HPCA (<a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a>) and Monaco (<a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a>) data sets as references and the fine label discriminators. Individual sequencing runs were subsetted to run through the singleR algorithm in order to reduce memory demands. The output of all the singleR analyses were collated and appended to the meta data of the seurat object. Likewise, the ProjecTILs (v0.4.1) R Package (<a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a>) was used for automatic annotation as a partially orthogonal approach. </p> <p><em>Addition of TCR data</em></p> <p>The filtered contig annotation T cell receptor (TCR) data for available sequencing runs were loaded into the R global environment. Individual contigs were combined using the combineTCR() function of scRepertoire (v1.3.5) R Package (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a>). Clonotypes were assigned to barcodes and were multiple duplicate chains for individual cells were filtered to select for the top expressing contig by read count. The clonotype data was then added to the Seurat Object with proportion across individual patients being used to calculate frequency.</p> <p><strong>Citations</strong></p> <p>As of right now, there is no citation associated with the assembled data set. However if using the data, please find the corresponding manuscript for each data set in the meta.data of the single-cell object. In addition, if using the processed data, feel free to modify the language in the methods section (above) and please cite the appropriate manuscripts of the software or references that were used.</p> <p><em>Itemized List of the Software Used</em></p> <ul> <li>Seurat v4.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a></li> <li>harmony v1.0 - <a href="https://pubmed.ncbi.nlm.nih.gov/31740819/">citation</a></li> <li>singler v1.4.1 - <a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a></li> <li>ProjecTILs v2.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a></li> <li>UCell v1.0.0 - <a href="https://www.biorxiv.org/content/10.1101/2021.04.13.439670v1">citation</a></li> <li>scRepertoire v1.3.5 - <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a></li> </ul> <p><em>Itemized List of Reference Data Used</em></p> <ul> <li>Human Primary Cell Atlas (HPCA) - <a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a></li> <li>Monaco Data Set - <a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a></li> </ul> <p><strong>Future Directions</strong></p> <ul> <li>Data Hosting for Interactive Analysis</li> <li>Easy Submission Portal for Researchers to Add Data</li> <li>Using the Data to Build a Reference Atlas</li> </ul> <p>There are areas in which we are actively hoping to develop to further facilitate the usage of the data set - if you have other suggestions, please reach out using the contact information below.</p> <p><strong>Contact</strong></p> <p>Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, <a href="mailto:ncborch@gmail.com">email</a>, or using <a href="https://twitter.com/theHumanBorch">twitter</a>.</p>
Pan-Cancer T cell atlas from "The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells" (https://doi.org/10.1371/journal.pone.0315980)
<p>The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.<br>Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0).</p> <p>To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction.<br>T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.</p>
Characterization of Circulating and Tumor-infiltrating Immune Cells in Malignant Brain Tumors
ClinicalTrials.gov study NCT05831631. IPD Sharing: NO. Countries: 1. Publications: 14.
Tumor-Infiltrating Lymphocytes and Programmed Cell Death - Ligand 1 in Breast Cancer
ClinicalTrials.gov study NCT05250336. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Effect of Neoadjuvant Radiation on Tumor Infiltrating T-cells by Low Dose Radiation in Colorectal Liver Metastases
ClinicalTrials.gov study NCT01191632. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Phase 2 Trial for Metastatic Melanoma Using Adoptive Cell Therapy With Tumor Infiltrating Lymphocytes Plus IL-2 Either Alone or Following the Administration of Pembrolizumab
ClinicalTrials.gov study NCT02621021. IPD Sharing: YES. Countries: 1. Publications: 3.
A PET Imaging Agent to Assess the Level of Tumor Tissue-infiltrating CD8 + T Cells in Patients With Solid Tumors
ClinicalTrials.gov study NCT05126927. IPD Sharing: NO. Countries: 1. Publications: 2.
T-cell Therapy with CRISPR PD1-edited Tumor Infiltrating Lymphocytes for Patients with Metastatic Melanoma
ClinicalTrials.gov study NCT06783270. IPD Sharing: NO. Countries: 1. Publications: 1.
Adoptive Cell Transfer of Autologous Tumor Infiltrating Lymphocytes and High-Dose Interleukin 2 in Select Solid Tumors
ClinicalTrials.gov study NCT03991741. IPD Sharing: NO. Countries: 1. Publications: 0.
RELB Reprograms Exhausted Tumor-Infiltrating Lymphocytes for Improved Adoptive Cell Therapy [TCR-Seq]
GEO Series GSE303438. Homo sapiens. 38 samples. Type: Other.
Single-cell RNAseq analysis of peripheral blood and tumor infiltration immune cells in glioblastoma
GEO Series GSE247824. Homo sapiens. 37 samples. Type: Expression profiling by high throughput sequencing.
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