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66 results for “tumor-infiltrating lymphocytes”
Prognostic Significance of Deep Learning-Based Tumor-Infiltrating Lymphocyte Assessment for Triple-Negative Breast Cancer
<p>The quantified manual and auto sTILs scores for the TNBC patiens from TCGA-BRCA cohort presented in the article "Prognostic Significance of Deep Learning-Based Tumor-Infiltrating Lymphocyte Assessment for Triple-Negative Breast Cancer" which is currently under review. </p>
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>
Assessing Tumor-Infiltrating Lymphocytes in Breast Cancer: A Proposal for Combining Immunohistochemistry and Gene Expression Analysis to Refine Scoring
<p>Whole tissue scans of histochemistry (H&E) and immunohistochemistry (CD3, CD4, CD8 andCD45) images that have been used to calculate TIL scores.</p> <p>All stainings are numbers for each patient..</p>
Phenotypic Characterization Tumor-infiltrating Lymphocytes at Diagnosis and After Chemotherapy in Ovarian Cancer
ClinicalTrials.gov study NCT03922776. IPD Sharing: NO. Countries: 1. Publications: 24.
Tumor-Infiltrating Lymphocytes and Programmed Cell Death - Ligand 1 in Breast Cancer
ClinicalTrials.gov study NCT05250336. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Tumor-Infiltrating Lymphocytes And Low-Dose Interleukin-2 Therapy Following Cyclophosphamide And Fludarabine In Patients With Melanoma
ClinicalTrials.gov study NCT01883323. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Adoptive Tumor-infiltrating Lymphocyte Transfer With Nivolumab for Melanoma
ClinicalTrials.gov study NCT04165967. IPD Sharing: Not stated. Countries: 1. Publications: 33.
Stromal Tumor-Infiltrating Lymphocyte Levels Are Associated With Immune Checkpoint Proteins In Triple Negative Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
ClinicalTrials.gov study NCT06965361. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Comprehensive Evaluation of Tumor-Infiltrating Lymphocytes across 28 Cancer Types Using Deep Learning
<p><span><span>Training, validation and independent datasets related to model training and evaluation.</span></span></p>
Tumor-Infiltrating Lymphocytes After Combination Chemotherapy in Treating Patients With Metastatic Melanoma
ClinicalTrials.gov study NCT01807182. IPD Sharing: Not stated. 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.
RNASeq of PD-1loCTLA-4lo versus PD-1hiCTLA-4hi CD8 tumor-infiltrating lymphocytes from human melanoma
GEO Series GSE147620. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
CD8+ T cells in peripheral blood lymphocytes and tumor-infiltrating lymphocytes of melanoma patients: additional validation data
GEO Series GSE153098. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Tumor-infiltrating lymphocytes-derived CD8+ clonotypes infiltrate the tumor tissue and mediate tumor regression in glioblastoma [RNA-Seq]
GEO Series GSE285281. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
IL-7-primed bystander CD8 tumor-infiltrating lymphocytes optimize the antitumor efficacy of T-cell engager immunotherapy in solid tumors
GEO Series GSE237266. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
β3-adrenergic receptor on tumor-infiltrating lymphocytes sustains IFN-γ-dependent PD-L1 expression and impairs anti-tumor immunity in a murine model of neuroblastoma
GEO Series GSE209634. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Momordicine-I suppresses head and neck cancer growth by reprogrammimg immunosuppressive effect of the tumor-infiltrating macrophages and B lymphocytes
GEO Series GSE254011. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
CD8+ T cells in peripheral blood lymphocytes and tumor-infiltrating lymphocytes of melanoma patients
GEO Series GSE138720. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Colon cancer cells acquire immune regulatory molecules from tumor-infiltrating lymphocytes by trogocytosis
GEO Series GSE186692. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Impact of Sirt2 deficiency on downstream TCR signaling in mouse melanoma tumor-infiltrating T lymphocytes
GEO Series GSE265880. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.