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5,287 results for “Colorectal Cancer”
100,000 histological images of human colorectal cancer and healthy tissue
<p><strong>Data Description "NCT-CRC-HE-100K"</strong></p> <ul> <li>This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.</li> <li>All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abstract/document/5193250/, DOI <a href="https://doi.org/10.1109/ISBI.2009.5193250">10.1109/ISBI.2009.5193250</a>).</li> <li>Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).</li> <li>These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.</li> </ul> <p><strong>Ethics statement "NCT-CRC-HE-100K"</strong></p> <p>All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).</p> <p><strong>Data set "CRC-VAL-HE-7K"</strong></p> <p>This is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.</p> <p><strong>Data set "NCT-CRC-HE-100K-NONORM"</strong></p> <p>This is a slightly different version of the "NCT-CRC-HE-100K" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as "NCT-CRC-HE-100K". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as "NCT-CRC-HE-100K", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.</p> <p><strong>General comments</strong></p> <p>Please note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended.</p>
Structural modelling results to accompany the paper "Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing: an update"
<p>This repository contains the results of modelling missense mutants in KRAS, NRAS and BRAF observed in our study in the corresponding protein structures. Modelling was performed using FoldX.</p>
Scripts and data attached to colorectal cancer study by Purcell, 2017
<p>Scripts and data attached to colorectal cancer study by Purcell, 2017.</p> <p>Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer.<br> Purcell RV, Visnovska M, Biggs PJ, Schmeier S, Frizelle FA.<br> Scientific Reports, 2017, doi: <a href="http://doi.org/10.1038/s41598-017-11237-6">10.1038/s41598-017-11237-6</a></p> <p><br> Pubmed: <a href="https://www.ncbi.nlm.nih.gov/pubmed/28912574">https://www.ncbi.nlm.nih.gov/pubmed/28912574</a></p>
MinION sequence data: MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing
<p>MinION sequencing data that was unmapped by minimap2 for the 11 samples using in the "MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing" paper.</p>
Molecular portraits of colorectal cancer morphological regions
<p>Gene expression data (and associated annotation) corresponding to distinct morphological regions of colorectal cancer cases. This archive contains the data as an R package (MPM) and as tab-delimited files (*.tsv).</p>
Spatially Resolved Transcriptomics Deconvolutes Prognostic Histological Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases
<p>Spatial transcriptomic data (counts.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse matched colonic primary and liver metastases from 4 patients with metastatic colorectal cancer. 48 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation. In addition, normalised data (Counts - ncounter.csv) from ncounter bulk experiment comparing matched colonic primary and liver metastases</p>
VISION Invited lecture - Genomic instability, microenvironment and telomere homeostasis in colorectal cancer
<p>Recording and presentation of the invited lecture that took place online on 4 November 2021 - <strong>Pavel Vodička, MD, Ph.D. - Genomic instability, microenvironment and telomere homeostasis in colorectal cancer.</strong></p> <p>Pavel Vodicka<sup>1,2,3</sup>, Sona Vodenkova<sup>1</sup>, Michal Kroupa<sup>1,3</sup>, Alena Opattova<sup>1,2,3</sup>, Kristyna Tomasova<sup>1,3</sup>, Ludmila Vodickova<sup>1,2,3</sup></p> <p><sup>1</sup> Institute of Experimental Medicine, Czech Acad. Sci., Videnska 1083, Prague 4, Czech Rep.</p> <p><sup>2</sup> Inst. Biology and Med. Genet., 1st Faculty of Medicine, Charles University, Albertov 6, Prague 2, Czech Rep.</p> <p><sup>3</sup> Biomedical Center, Faculty of Medicine in Pilsen, Charles University Prague, Pilsen, 30100, Czech Rep.</p> <p>Colorectal cancer (CRC) continues to be one of the leading malignancies and causes of tumour-related deaths worldwide. Both impaired DNA repair mechanisms and disrupted telomere length homeostasis represent potential culprits in CRC onset, its dissemination in the body and prognosis. Above parameters are becoming critical as prognostic markers, since CRC therapy is based on compounds interacting with DNA. DNA repair capacity in CRC patients have recently been studied in order to address prediction of therapy response. Due to the substantial interindividual variations in DNA repair capacities and relative telomere length, these markers may pose a possible contribution in individualized therapeutical regimen of CRC patients. Telomere attrition, responsible for replicative senescence in healthy cells, may become a hallmark of malignant transformation of the cell due to by-passing cell cycle checkpoints. Telomerase – a key enzyme keeping homeostasis of telomere - is almost ubiquitous in advanced solid cancers, including CRC, and its expression is fundamental to cell immortalization.<br> Here we present our data based on the investigation of base excision repair capacities and relative telomere length in tumor tissues and adjacent non-malignant mucosa of sporadic CRC patients. The relative gene expression of telomerases is monitored as well. Particular attention will be dedicated to the CRC phenotypes and clinicopathological characteristics. We also addressed telomere homeostasis in peripheral blood lymphocytes of CRC patients in several consecutive samplings (at diagnosis, immediately after treatment and in additional follow-up intervals), which could provide us the insight into the treatment response. This aspect is of particular relevance, since there is currently a persistent effort to develop therapeutics, which are telomerase-specific and gentle to non-malignant tissue. However, in practice, we are at the dawn of clinical trials. Additionally, emerging crosstalks between DNA repair and telomere length homeostasis may cast some lights on a dynamic of genomic instability, a fundamental hallmark (or cause) of cancer.</p> <p>Acknowledgement: GACR 21-04607X, 19-10543S, AZV NV18/03/00199</p>
Oncogenic signalling is coupled to colorectal cancer cell differentiation state
<p>Mass cytometry and single-cell RNA-sequencing data as well as R Markdown reports to reproduce the figures of our publication.</p> <p>Raw MC data were saved post de-convolution, spillover-compensation, and removal of calibration bead events. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage.</p> <p>As we performed random sampling to equalise cell numbers across conditions, batch normalisation, and used non-linear dimensionality reduction techniques (UMAP and Diffusion Maps), resulting plots may differ slightly from the published figures, yet still support the drawn conclusions. Already normalised and/or sampled data as well as pre-computed UMAP and Diffusion Map coordinates are included in this data set to reproduce the manuscript figures exactly, as shown in the included report “figures_only”. For all details on the batch normalisation and data analysis steps performed, please consult the report “data_analysis” instead.</p>
Transcriptome Analysis of Cisplatin, Cannabidiol, and Intermittent Serum Starvation Alone and in Various Combinations on Colorectal Cancer Cells
<p>* See README file for the description of data files available in this repository</p> <p>1. Study Description:</p> <p>Platinum-derived chemotherapy medications are often combined with other conventional therapies for treating different tumours, including colorectal cancer. However, the development of drug resistance and multiple adverse effects remain common in clinical settings. Thus, there is a necessity to find novel treatments and drug combinations that could effectively target colorectal cancer cells and lower the probability of disease relapse. To find potential synergistic interaction, we designed multiple different combinations between cisplatin, cannabidiol, and intermittent serum starvation on colorectal cancer cell lines. Based on the cell viability assay, we found that combinations between cannabidiol and intermittent serum starvation, cisplatin, and intermittent serum starvation, as well as cisplatin, cannabidiol and intermittent serum starvation can work in a synergistic fashion on different colorectal cancer cell lines. Furthermore, we analyzed differentially expressed genes and affected pathways in colorectal cancer cell lines to understand further the potential molecular mechanisms behind the treatments and their interactions. We found that synergistic interaction between cannabidiol and intermittent serum starvation can be related to changes in the transcription of genes responsible for cell metabolism and cancer’s stress pathways. Moreover, when we added cisplatin to the treatments, there was a strong enrichment of genes taking part in G2/M cell cycle arrest and apoptosis.</p> <p> </p> <p>2. Bioinformatics workflow:</p> <p>Initial quality control was conducted using FastQC v0.11.9 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Sequencing reads were trimmed of adapter sequences and low-quality bases using Trimmomatic. Trimmed sequence files were examined with FastQC to verify the trimming results. Trimmed sequencing reads were mapped to Human genome (GRCh37, Ensembl) downloaded from Illumina iGenome website (<a href="https://support.illumina.com/sequencing/sequencing_software/igenome.html">https://support.illumina.com/sequencing/sequencing_software/igenome.html</a>). Mapping was done using splice aware aligner HISAT2 2.1.0. Alignment files in SAM format were converted to BAM, sorted and indexed with samtools v.1.3.1. Mapping quality and statistics were collected with QualiMap software package v.2.2.2 <a href="http://qualimap.conesalab.org/">http://qualimap.conesalab.org/</a> The counts if reads mapping to features (genes) were counted using FeatureCounts v.2.0.1 software.</p> <p>Data exploration, visualization and statistical comparisons were conducted using R language version 4.2.2. Pair-wise comparisons between experimental groups were done with DESeq2 v.2.1.36 as described in the package manual. To decrease computational time, only the genes with at least 5 reads across 3 samples were kept in the analysis. In addition to hard threshold filtering mentioned above, DESeq2 implements independent filtering based on mean of normalized count as a filter statistic.</p> <p>We used hierarchical clustering (HC) and principal components analysis (PCA) to investigate the relationship between samples and detect potential outliers. Prior to HC and PCA analysis, DESeq2 normalized values underwent variance stabilizing transformation with using vst() function from DESeq2. HC was done using hclust() function implemented in R, with the clustering method set as “complete” for the matrices of sample-to-sample distances, and “Ward.D2” in case of the sample and gene clustering based on top 500 most variable genes. The distance measure in HC analysis was set to “euclidean”. Principal components analysis (PCA), applied to top 500 highly variable genes, was conducted using prcomp() function implemented in R with default options.</p> <p>Differentially expressed genes (DEGs) were detected with DESeq2 function results() with default options. DESeq2 uses Wald test to determine significantly changed genes between groups. The independent filtering option was set to TRUE with alpha threshold (adjusted p-value) kept at 0.1. Multiple comparison adjustment was done using Bejamini-Hochberg procedure.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
An Evaluation of a Multi-target Stool DNA (Mt-sDNA) Test, Cologuard, for CRC Screening in Individuals Aged 45-49 and at Average Risk for Development of Colorectal Cancer: Act Now
ClinicalTrials.gov study NCT03728348. IPD Sharing: YES. Countries: 1. Publications: 1.
PRevention Using EPA Against coloREctal Cancer
ClinicalTrials.gov study NCT04216251. IPD Sharing: YES. Countries: 1. Publications: 0.
Trastuzumab Deruxtecan in Participants With HER2-overexpressing Advanced or Metastatic Colorectal Cancer
ClinicalTrials.gov study NCT04744831. IPD Sharing: YES. Countries: 10. Publications: 2.
DS-8201a in Human Epidermal Growth Factor Receptor2 (HER2)-Expressing Colorectal Cancer (DESTINY-CRC01)
ClinicalTrials.gov study NCT03384940. IPD Sharing: YES. Countries: 5. Publications: 3.
Helping Patients and Providers Make Better Decisions About Colorectal Cancer Screening
ClinicalTrials.gov study NCT04683731. IPD Sharing: YES. Countries: 1. Publications: 1.
Paired Promotion of Colorectal Cancer and Social Determinants of Health Screening
ClinicalTrials.gov study NCT04585919. IPD Sharing: YES. Countries: 1. Publications: 2.
Lee2020 GSE132465 Primary Colorectal Cancer Dataset for Besca
<p>The gene expression matrix was downloaded from GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465</a>), originally published by Lee HO, Hong Y, Etlioglu HE, et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. <em>Nat Genet</em>. 2020;52(6):594-603. doi:10.1038/s41588-020-0636-z. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).</p>
Colorectal cancer scRNA-seq 10xG-format data matrix
<p>Metastatic colorectal cancer (CRC) is a major cause of cancer-related death and incidence is rising in the younger population (<50 years). Current chemotherapies can achieve response rates above 50%, but immunotherapies have limited value for patients with microsatellite-stable (MSS) cancers. The present study investigates the impact of chemotherapy on the tumor immune microenvironment. We treat human liver metastases slices with 5-Fluorouracil (5FU) plus either irinotecan or oxaliplatin, then perform single-cell transcriptome analyses. Results from eight cases reveal two cellular subtypes with divergent responses to chemotherapy. Susceptible tumors are characterized by a stemness signature, an activated interferon pathway, and suppression of PD-1 ligands in response to 5FU+irinotecan. Conversely, immune checkpoint TIM-3 ligands are maintained or up-regulated by chemotherapy in CRC with an enterocyte-like signature, and combining chemotherapy with TIM-3 blockade leads to synergistic tumor killing. Together, our analyses highlight chemo-modulation of the immune microenvironment and provide a framework for combined chemo-immunotherapies. </p>
Collection of textures in colorectal cancer histology
<p><strong>Content</strong></p> <p>This data set represents a collection of textures in histological images of human colorectal cancer. It contains two files:</p> <ol> <li>"Kather_texture_2016_image_tiles_5000.zip": a zipped folder containing 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories (specified by the folder name). </li> <li>"Kather_texture_2016_larger_images_10.zip": a zipped folder containing 10 larger histological images of 5000 x 5000 px each. These images contain more than one tissue type. </li> </ol> <p><strong>Image format</strong></p> <p>All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the approved guidelines and with the Declaration of Helsinki.</p> <p><strong>More information / data usage</strong></p> <p>For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong></p> <p>Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F: Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br /> Dr. Jakob Nikolas Kather<br /> http://orcid.org/0000-0002-3730-5348<br /> ResearcherID: D-4279-2015</p>
Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Colorectal Cancer Detection
<p>Processed data (feature-by-sample matrices) of non-disruptive bisulfite-free methylation sequencing for cfDNA samples from 4 clinical cohorts (Cohort 1, Cohort 2, Cohort 3, and cfTAPS dataset). Codes used to repeat the results in our paper can be found https://rpubs.com/LiYumei/926228 and https://github.com/ChaorongC/MESA. </p>
Search Strategies for the Cost Effectiveness of Colorectal Cancer Screening
<p><span>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question:</span></p> <p><span>For the Irish population at average risk of colorectal cancer, is biennial FIT-based colorectal cancer screening at a FIT threshold of 45 ug/g, in persons aged from age 50 to 74 years, cost effective compared to screening in persons aged 55 to 74 years</span></p>
ScienceDex guides
Understand access before you commit
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.