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57 results for “Precision Oncology”
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics
<p>In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment. </p>
Urine NMR metabolomics for precision oncology in colorectal cancer
<p>Tables summarizing the data used for the review. Up to 7 tables, and a list of the included studies is provided.</p>
Ex vivo modeling of precision immuno-oncology responses in lung cancer
<p>Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. </p> <p>The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.</p> <table> <tbody> <tr> <td><strong>Sample name</strong></td> <td><strong>Multiplexed experiment</strong></td> <td><strong>Donor<br></strong></td> <td><strong>Hashtag name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1-PBMCs_lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Untreated, baseline PBMCs for Lung-19</td> </tr> <tr> <td>2-PBMCs_lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Untreated, baseline PBMCs for Lung-35</td> </tr> <tr> <td>3-PBMCs_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Untreated, baseline PBMCs for Lung-25</td> </tr> <tr> <td>4-T_cells_Lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_4</td> <td>Tumor-stimulated immune cells for Lung-19</td> </tr> <tr> <td>5-T_cells_Lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_5</td> <td>Tumor-stimulated immune cells for Lung-35</td> </tr> <tr> <td>6-T_cells_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_6</td> <td>Tumor-stimulated immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Tumor cells alone for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Tumor cells alone for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Tumor cells alone for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-19</td> <td>Hashtag_7</td> <td>Tumor cells and ts-immune cells for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-35</td> <td>Hashtag_8</td> <td>Tumor cells and ts-immune cells for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-25</td> <td>Hashtag_9</td> <td>Tumor cells and ts-immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-19</td> <td>Hashtag_10</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-35</td> <td>Hashtag_12</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-25</td> <td>Hashtag_13</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-25</td> </tr> </tbody> </table> <p>This Zenodo repository provides:</p> <ul> <li>Processed RNA-seq and hashtag oligo sequencing (HTO-seq) data (<em>feature_bc_matrices.zip</em>)</li> <li>Hashtag names and sequences (<em>Custom_CMO_set.csv</em>), which are needed to rerun Cellranger</li> <li>Seurat v5 objects (<em>seurat_object_all_tumor_cells.rds, seurat_object_all_immune_cells.rds, seurat_object_PBMCs_demux.rds</em>)</li> </ul> <p>This Zenodo repository does <strong>not </strong>provide:</p> <ul> <li>Sensitive raw sequencing data</li> <li>Sensitive metadata</li> </ul> <p>The raw data generated from the scRNA sequencing is available at the European Genome-phenome Archive (EGA; <a href="https://ega-archive.org">https://ega-archive.org</a>) under accession number EGAD50000000845.</p> <div> <div> <p> </p> <p><strong>To cite our work</strong>:</p> </div> Bassel Alsaed <em>et al.</em> Ex vivo modeling of precision immuno-oncology responses in lung cancer.<em>Sci. Adv.</em><strong>10</strong>,eadq6830(2024).DOI:<a href="https://doi.org/10.1126/sciadv.adq6830">10.1126/sciadv.adq6830</a></div>
Clinical Research Platform on Decision Making and Clinical Impact of Biomarker-Driven Precision Oncology
ClinicalTrials.gov study NCT04389541. IPD Sharing: NO. Countries: 1. Publications: 1.
Liquid Biopsy-informed Precision Oncology Study to Evaluate Utility of Plasma Genomic Profiling for Therapy Selection
ClinicalTrials.gov study NCT05585684. IPD Sharing: NO. Countries: 1. Publications: 0.
Tumor-agnostic Precision Immuno-oncology and Somatic Targeting Rational for You (TAPISTRY) Platform Study
ClinicalTrials.gov study NCT04589845. IPD Sharing: YES. Countries: 24. Publications: 2.
Real-world Trial of Individualized Precision Oncology
ClinicalTrials.gov study NCT07346209. IPD Sharing: NO. Countries: 1. Publications: 3.
Research Collaboration for a Precision Oncology Program (POP)
ClinicalTrials.gov study NCT06680726. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: A virtual molecular tumor board platform to improve efficiency and scalability of delivering precision oncology to physicians and their patients
OBJECTIVES: Scalable informatics solutions that provide molecularly-tailored treatment recommendations to clinicians are needed to streamline Precision Oncology in care settings. MATERIALS AND METHODS: We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations. RESULTS: The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly twofold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support. DISCUSSION: VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand. CONCLUSION: Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real world evidence from electronic health records, and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.
RNA Precision Oncology in Advanced Pancreatic Cancer
ClinicalTrials.gov study NCT04476537. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Promoting INformed Approaches in Precision Oncology and ImmuNoTherapy
ClinicalTrials.gov study NCT05034289. IPD Sharing: NO. Countries: 1. Publications: 0.
Endoscopic Optical Imaging for Precision Oncology Treatment Applied to Colorectal Tumours (Elios-Color-on-Specimen)
ClinicalTrials.gov study NCT04101292. IPD Sharing: NO. Countries: 0. Publications: 24.
Precision Care Initiative: Integrating Precision Oncology Into Clinical Programs
ClinicalTrials.gov study NCT06077110. IPD Sharing: NO. Countries: 0. Publications: 1.
The PIONEER Initiative: Precision Insights On N-of-1 Ex Vivo Effectiveness Research Based on Individual Tumor Ownership (Precision Oncology)
ClinicalTrials.gov study NCT03896958. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: A virtual molecular tumor board platform to improve efficiency and scalability of delivering precision oncology to physicians and their patients
Open the record for dataset details and reuse information.
Phenotype-driven precision oncology in patient-derived tumor models predict therapeutic response in squamous cell carcinoma [expression]
GEO Series GSE100123. Homo sapiens. 9 samples. Type: Expression profiling by array.
Molecular and functional landscape of malignant serous effusions for precision oncology [MSE_baseline]
GEO Series GSE240952. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Patient-derived micro-organospheres (MOS) enable clinical precision oncology
GEO Series GSE184242. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Molecular and functional landscape of malignant serous effusions for precision oncology [MSE_drug_treated]
GEO Series GSE240951. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.