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1,615 results for “tumour”
The Biological Activity of Cediranib (AZD2171) in Gastro-Intestinal Stromal Tumours(GIST).
ClinicalTrials.gov study NCT00385203. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Weekly Intravenous Administrations of BI 836845 in Japanese Patients With Advanced Solid Tumours
ClinicalTrials.gov study NCT02145741. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of Preoperative Walking Exercises on the Prognosis of Supratentorial Brain Tumours Patients After Craniotomy
ClinicalTrials.gov study NCT05930288. IPD Sharing: NO. Countries: 1. Publications: 1.
Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
Open the record for dataset details and reuse information.
Data from: Sex bias in ability to cope with cancer: Tasmanian devils and facial tumour disease.
Open the record for dataset details and reuse information.
Supporting material for: A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study
<p>Underlying data, R scripts and subsequent figures are presented to deliver a replicable and transparent analysis for the manuscript "A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study"</p> <p>Published in Scientific Reports</p> <p>See: <a href="https://protect-de.mimecast.com/s/kCQLCgpRGySlzOwmCNma6d?domain=rdcu.be">https://rdcu.be/b3vj2</a> </p> <p> </p> <p>This is version 2 of this data. This differs from version 1 in the following</p> <p>1. The number of simulation has increased from 300 to 2000 in each simulation cycle</p> <p>2. Script has been added to generate publication level figures</p> <p> </p> <p> </p> <p> </p>
Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics - Dataset
<p>This is the dataset used in the manuscript "The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics" by Raatz et al. The code used to generate the data and figures can be found at https://doi.org/10.5281/zenodo.4461667.</p>
Data from: Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues
Cancer cells are known to exhibit unusual metabolic activity and yet, few metabolic cancer driver genes are known. Genetic alterations and epigenetic modi cations of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared to normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-speci fic cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top ranked one-hit dysregulations and 17 of the top ranked two-hit oncogenes with high similarity ratios to the templates. According literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.
Data from: Short-term and medium-term survival of critically ill patients with solid tumours admitted to the intensive care unit: a retrospective analysis
Objectives: Patients with cancer frequently require unplanned admission to the Intensive Care Unit (ICU). Our objectives were to assess hospital and 180-day mortality in patients with a non-haematological malignancy and unplanned ICU admission, and to identify which factors present on admission were the best predictors of mortality. Design: Retrospective review of all patients with a diagnosis of solid tumours following unplanned admission to the ICU between 1st August 2008 and 31st July 2012. Setting: Single centre tertiary care hospital in London (UK) Participants: 300 adult patients with non-haematological solid tumours requiring unplanned admission to the ICU. Interventions: None Primary and secondary outcomes: Hospital and 180-day survival Results: 300 patients were admitted to the ICU (median age 66.5 years; 61.7% male). Survival to hospital discharge and 180-days were 69% and 47.8%, respectively. Greater number of failed organ systems on admission was associated with significantly worse hospital survival (p<0.001) but not with 180-day survival (p=0.24). In multivariate analysis, predictors of hospital mortality were the presence of metastases [odds ratio (OR 1.97), 95% confidence interval (CI) 1.08-3.59], Acute Physiology and Chronic Health Evaluation II (APACHE II) score (OR 1.07, 95% CI 1.01-1.13) and a Glasgow Coma Scale score <7 on admission to ICU (OR 5.21, 95% CI 1.65-16.43). Predictors of worse 180-day survival were the presence of metastases (OR 2.82, 95% CI 1.57-5.06), APACHE II score (OR 1.07, 95% CI 1.01-1.13) and sepsis (OR 1.92, 95% CI 1.09-3.38). Conclusions: Short and medium-term survival in patients with solid tumours admitted to ICU is better than previously reported, suggesting that the presence of cancer alone should not be a barrier to ICU admission.
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
<p>This is the data repository for Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data.</p> <p>Catalog:</p> <ol> <li>Intermediate data used in plotting: CANVAS_source_data.zip <ol> <li>Single cell monocyte data: monocyte.h5ad</li> <li>qPCR table: qPCR_1013.csv</li> <li>NanoString GeoMx cell composition: fig6b.csv</li> </ol> </li> <li>Pretrained CANVAS model: checkpoint-1999.pth</li> </ol> <p>The source IMC data is avaiable at: https://zenodo.org/records/7760826</p>
Example data for "Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data"
<p>This repository includes two example dataset and configurations for running CANVAS (https://github.com/tanjimin/CANVAS).</p> <p>The repostory is structured as follows:</p> <p>├── Kim_2022<br>│ ├── configs<br>│ │ ├── config.yaml<br>│ │ └── preprocess<br>│ │ ├── channels_vis_strength.yaml<br>│ │ └── selected_channels_w_color.yaml<br>│ └── data<br>│ └── raw_data<br>│ ├── common_channels.txt<br>│ └── image_files<br>└── Sorin_2023<br> ├── configs<br> │ ├── config.yaml<br> │ └── preprocess<br> │ ├── channels_vis_strength.yaml<br> │ └── selected_channels_w_color.yaml<br> └── data<br> └── raw_data<br> ├── common_channels.txt<br> └── image_files</p> <p> </p> <p>The source IMC data from this repository are from Kim et al. 2022 (https://www.nature.com/articles/s41592-022-01657-2) and Sorin et al. 2023 (https://www.nature.com/articles/s41586-022-05672-3). They are avaiable at: https://zenodo.org/records/4110560 and https://zenodo.org/records/7760826.</p>
Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity
<p>We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell [6] (<a href="https://github.com/cnio-bu/beyondcell" target="_blank" rel="noopener">cnio-bu/beyondcell</a>), a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions.</p> <p>Here, you can find:</p> <p>In signatures folder:</p> <ul> <li><strong>SSc breast:</strong> Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.</li> <li><strong>Functional signatures:</strong> Collection of gene signatures used to compute enrichment in different biological pathways.</li> </ul> <p>In visium folder:</p> <ul> <li><strong>Visium objects:</strong> Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.</li> </ul> <p>In single-cell folder:</p> <ul> <li><strong>Single-cell objects:</strong> Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.</li> </ul> <p>In beyondcell folder:</p> <ul> <li><strong>Beyondcell </strong><strong>sensitivity </strong><strong>objects</strong> with prediction scores for all drug response signatures in SSc breast.</li> <li><strong>Beyondcell functional objects </strong>with enrichment scores for all functional signatures.</li> </ul>
Electrochemotherapy as Palliative Treatment in Patients with Recurrent and/or Metastatic Head and Neck Tumours: Features Analysis for an Early Determination of the Partial Responsive Patients
<p>We uploaded the data of enrolled patients in the manuscript "Electrochemotherapy as Palliative Treatment in Patients with Recurrent and/or Metastatic Head and Neck Tumours: Features Analysis for an Early Determination of the Partial Responsive Patients" </p>
Data from: Fusobacterium nucleatum and Bacteroides fragilis detection in colorectal tumours: optimal target site and correlation with total bacterial load
<p>These data were generated to investigate detection of <em>Fusobacterium nucleatum </em>(<em>F. nucleatum</em>) and <em>Bacteroides fragilis</em> (<em>B. fragili</em>s) across different regions of human colorectal tumours. Relative abundance of each species in DNA extracted for clinical molecular mutation testing from formalin-fixed, paraffin-embedded (FFPE) tumour samples from 42 patients was assessed using targeted real-time PCR quantitative (qPCR) (the screening cohort). DNA was then freshly extracted from specific regions of tumours testing positive for one or both species (n = 20) and from 31 additional patients, and relative abundance of each species assessed using qPCR (site investigation cohort). Total bacterial load at the tumour luminal surface (where <em>F. nucleatum</em> and <em>B. fragilis</em> were most frequently detected) was also assessed by qPCR using primers targeting amplification of 16S rRNA. 16S sequencing was performed on tumour luminal surface DNA samples from five patients as an orthogonal method to confirm the ability to detect the targeted species by qPCR.</p>
Computational validation of clonal and subclonal copy number alterations from bulk tumour sequencing
<p>Somatic variant identification from WGS data is a crucial step in the analysis of cancer genomes. Several tools are available to perform mutations calling, however, the noisiness of data requires appropriate quality control assessment. In the preprint work available at https://doi.org/10.1101/2021.02.13.429885 we present CNAqc, an R package devised to assess the quality of allele-specific Copy Number Alterations (CNA), somatic mutations, and tumor purity estimates. In order to test the model, we ran CNAqc on 2778 single-sample PCAWG whole-genomes and 48 TCGA whole-exomes. We uploaded the results of our analysis using the release of the tool available at https://github.com/caravagnalab/CNAqc/releases/tag/rr_22_0.1 in the form of .rds files. All the necessary details for the reproduction of our results are reported in the Supplementary Materials of the above-mentioned manuscript.</p>
Extricating human tumour immune alterations from tissue inflammation
<p>This is the pre-processed and cleaned RDS file that contains the main sc-RNAseq data (10x platform) from inflamed oral mucosal (OM) and head-and-neck squamous cell carcinoma (HNSCC) tissues used in this publication (https://www.nature.com/articles/s41586-022-04718-w). Script for processing and generating figures is uploaded on Github: https://github.com/MairFlo/Tumor_vs_Inflamed.</p> <p> </p> <p>Abstract:</p> <p>Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain<sup><a href="https://www.nature.com/articles/s41586-022-04718-w#ref-CR1">1</a>,<a href="https://www.nature.com/articles/s41586-022-04718-w#ref-CR2">2</a></sup>. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment<sup><a href="https://www.nature.com/articles/s41586-022-04718-w#ref-CR3">3</a>,<a href="https://www.nature.com/articles/s41586-022-04718-w#ref-CR4">4</a></sup>. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues. Here, in an effort to identify such tumour-enriched immune alterations, we used complementary single-cell analysis approaches to interrogate the immune infiltrate in human head and neck squamous cell carcinomas and site-matched non-malignant, inflamed tissues. Our analysis revealed a large overlap in the composition and phenotype of immune cells in tumour and inflamed tissues. Computational analysis identified tumour-enriched immune cell interactions, one of which yields a large population of regulatory T (T<sub>reg</sub>) cells that is highly enriched in the tumour and uniquely identified among all haematopoietically-derived cells in blood and tissue by co-expression of ICOS and IL-1 receptor type 1 (IL1R1). We provide evidence that these intratumoural IL1R1<sup>+</sup> T<sub>reg</sub> cells had responded to antigen recently and demonstrate that they are clonally expanded with superior suppressive function compared with IL1R1<sup>−</sup> T<sub>reg</sub> cells. In addition to identifying extensive immunological congruence between inflamed tissues and tumours as well as tumour-specific changes with direct disease relevance, our work also provides a blueprint for extricating disease-specific changes from general inflammation-associated patterns.</p>
Data for Eirew et al Accurate determination of CRISPR-mediated gene fitness in transplantable tumours
<p>Derived counts files and experimental metadata for Eirew et al Accurate determination of CRISPR-mediated gene fitness in transplantable tumours.</p>
Engineering Micro Oxygen Factories to Slow Tumour Progression via Hyperoxic Microenvironments
<p>Imaging data of the paper</p>
Supplement table for Sparano et al.: Classic and follicular variant of papillary thyroid microcarcinoma: two different phenotypes beyond tumour size. Journal of the Endocrine Society, 2022.
<p>Sparano <em>et al.</em> Supplement to: Classic and follicular variant of papillary thyroid microcarcinoma: two different phenotypes beyond tumour size. <em>Journal of the Endocrine Society</em>, 2022.</p>
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.