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5,287 results for “Colorectal cancer”
Dataset of "Assessment of transparency and selective reporting of interventional trials studying colorectal cancer"
<p>This is the dataset used for our work "Assessment of transparency and selective reporting of interventional trials studying colorectal cancer". </p> <p>Trials included were identified through ClinicalTrials.gov.</p> <p>This dataset is presented in the form of an excel sheet with each line corresponding to a different trial (NCT number).</p>
Immunosuppressive niche engineering at the onset of human colorectal cancer
<p>Dataset used in "Immunosuppressive niche engineering at the onset of human colorectal cancer" by Gatenbee et al. 2022. </p>
Differences in gut microbiome abundances and diversity by physical activity levels and BMI among patients with colorectal cancer
<p>We investigated associations of physical activity, BMI, and combinations of physical activity levels/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. Pre-surgery stool samples from 179 colorectal cancer patients were used to perform 16S rRNA gene sequencing.</p>
Sensitization of FOLFOX-resistant colorectal cancer cells via the modulation of a novel pathway involving protein phosphatase 2A
<p>The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed an increased phosphorylation of AKT1/mTOR/4EBP1 and levels of p21 in FOLFOX-resistant CRC cells. We have identified a small molecule, NSC49L, that stimulates protein phosphatase 2A (PP2A) activity, downregulates the AKT1/mTOR/4EBP1-axis, and inhibits p21 translation. We have provided evidence that NSC49L- and TRAIL-mediated sensitization is synergistically induced in p21-knockdown CRC cells, which is reversed in p21-overexpressing cells. p21 binds with procaspase 3 and prevents activation of caspase 3. We have shown that TRAIL induces apoptosis through the activation of caspase 3 by NSC49L-mediated downregulation of p21 translation, and thereby cleavage of procaspase 3 into caspase 3. NSC49L does not affect global protein synthesis. These studies provide a mechanistic understanding of NSC49L as a PP2A agonist, and how its combination with TRAIL sensitizes FOLFOX-resistant CRC cells.</p>
Colorectal cancer interleukin-10 blockade scRNA-seq
<p><em>Objective:</em> PD-1 checkpoint inhibition and adoptive cellular therapy have limited success in patients with microsatellite stable colorectal cancer liver metastases (CRLM). We demonstrate that interleukin-10 (IL-10) blockade enhances endogenous T cell and chimeric antigen receptor T (CAR-T) cell anti-tumor function in CRLM slice cultures.<br><br><em>Design:</em> We created organotypic slice cultures from human CRLM (n = 38) and tested the anti-tumor effects of a neutralizing antibody against IL-10 (αIL-10). We evaluated slice cultures with single and multiplex immunohistochemistry, in situ hybridization, single cell RNA sequencing, and time-lapse fluorescent microscopy. In addition, we studied the effects of αIL-10 on carcinoembryonic antigen (CEA)-specific CAR-T cells exogenously administered to both human CRLM slice cultures and a CRLM murine model. <br><br><em>Results: </em>There was little effect of PD-1 blockade in CRLM slice cultures. In contrast, αIL-10 generated 1.8-fold increase in T cell-mediated carcinoma cell death, and increased proportion of CD8+ T cells and inflammatory polarization of macrophages. In addition to effects on endogenous immune cells in human CRLM, αIL-10 also rescued murine CAR-T cell proliferation and cytotoxicity from myeloid cell-mediated immunosuppression. In human CRLM slices, αIL-10 dramatically improved CEA-specific CAR-T cell cytotoxicity, generating nearly 70% carcinoma apoptosis across multiple human tumors. We saw a less dramatic, but similar effect of pretreatment of CAR-T cells with an IL-10 receptor blocking antibody, demonstrating that IL-10 inhibits CAR-T function in the CRLM tumor microenvironment.</p> <p><em>Conclusion:</em> Neutralizing the effects of IL-10 in human CRLM has therapeutic potential as a stand-alone treatment and to augment the function of adoptively transferred CAR-T cells.</p>
Code and data for "Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer".
<p>Code and data for "<strong>Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer</strong>".</p> <ul> <li> <p>The publicly available datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) and UCSC Xena Browser (TCGA, <a href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</a>).</p> </li> </ul>
Metagenome-assembled genomes (MAGs), colorectal cancer (CRC)
<p>This archive contains (i) Metagenome assemblies of short-term enrichment cultures of CRC mucosal tissue microbiota, and (ii) Reconstructed metagenome-assembled genomes (MAGs) generated through binning of metagenome contigs.</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>
Visium HD Human Colorectal Cancer (FFPE) data release pathologist annotation
<p>10X Genomics released a spatial <a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">transcriptomic dataset of human colorectal cancer collected on the Visium HD platform. </a></p> <p>The dataset was divided into different spatial domains based on the accompanying HE stain and the expression of characteristic marker genes.</p> <p>The pathologist's annotation was added with the help of Napari and the Spatialdata python package.</p> <p>Every .csv file contains the Visium HD bin barcode and annotation for the respective level of binning.</p> <p>In addition the HE image was segmented and bins were assigned to Nuclei for a pseudo single cell resolution, as described <a href="https://www.10xgenomics.com/analysis-guides/segmentation-visium-hd">here.</a></p> <p>This work was carried out for the <a href="https://github.com/SpatialHackathon/SpaceHack2023">SpaceHack2023 project </a>and the data shared here is licensed CC0. </p> <p> </p>
Repurposing Sulfasalazine as a Radiosensitizer in Hypoxic Human Colorectal Cancer.
<p>This dataset contains the prism files, with all the individual data point that lie at the basis of the results discussed in the paper entitled: "Repurposing Sulfasalazine as a Radiosensitizer in Hypoxic Human Colorectal Cancer." </p>
Chemical perturbations impacting histone acetylation regulate colorectal cancer differentiation
<p>M60.count.Rdata: Rdata that includes the raw count, scaled CPM value and meta data of each sample. (Fig. 1 & 4)</p> <p>DAP.merged.Log2FC.csv: integrated DiffBind results that shows the log2FC and P-value/FDR of peaks. (Fig. 1 & 4)</p> <p>rawdata_scRNA-seq.zip: raw count data of scRNA-seq data from cellranger (labels: A: DMSO, B: MRK60, D: MRK60 + JQAD1). (Fig. 5)</p>
Retrotransposon insertions can initiate colorectal cancer and are associated with poor survival
<p>This dataset is related to "Retrotransposon insertions can initiate colorectal cancer and are associated with poor survival" (Cajuso et al.).</p> <p><strong>Abstract:</strong></p> <p>Genomic instability pathways in colorectal cancer (CRC) have been extensively studied, but the role of retrotransposition in colorectal carcinogenesis remains poorly understood. Although retrotransposons are usually repressed, they become active in several human cancers, in particular those of the gastrointestinal tract. Here we characterize retrotransposon insertions in 202 colorectal tumor whole genomes and investigate their associations with molecular and clinical characteristics. We find highly variable retrotransposon activity among tumors and identify recurrent insertions in 15 known cancer genes. In approximately 1% of the cases we identify insertions in <em>APC</em>,<em> </em>likely to be tumor-initiating events. Insertions are positively associated with the CpG island methylator phenotype and the genomic fraction of allelic imbalance. Clinically, high number of insertions is independently associated with poor disease-specific survival.</p> <p><strong>Sample description: </strong></p> <p>A signed informed consent was obtained for as many human participants as possible. In cases without a signed informed consent, an authorization from the National Supervisory Authority for Welfare and Health (Dnro 421/04/044/06, Dnro 8048/06.01.03.01/2014, Dnro 358/32/300/05, Dnro 1476/06.01.03.01/2012) was obtained as stated in Finnish law. The study has been reviewed by the Ethics Committee of the Hospital district of Helsinki and Uusima (Dnro 133/E8/03, 408/13/03/03/2009). Permission to use patient information was obtained from the National Institute for Health and Welfare (Dnro 53/07/2000, Dnro THL/1071/5.05.00/2011, Dnro THL/151/5.05.00/2017). </p> <p><strong>RNA sequencing:</strong></p> <p>Total RNA from consecutive cryosections was extracted using RNeasy Mini Kit (Qiagen) from 34 tumors that displayed more than 50% of cancer cell percentage (HE staining of cryosections) and RNA integrity>6 (Agilent RNA 6000, Agilent 2100 Bioanalyzer). Paired-end RNA sequencing was performed on the Illumina Hiseq 2000. RNA-seq data was processed using Kallisto (version 0.43.0) software. Kallisto quantification was executed in paired-end mode and aligned against the Ensembl Human reference transcriptome (GRCh37_79). Quantification results from Kallisto were normalized and aggregated to gene-level utilizing sleuth (version 0.28.1) R package with default filtering settings.</p> <p> </p>
Fragment coordinates from shallow WGS of colorectal cancer from patients in Pakistan
<p>BED files indicating fragment coordinates from whole genome sequencing of FFPE tumors and adjacent normal tissue samples. Sequencing data was aligned to hg19 using bwa-mem, prior to conversion to BED format. Tumor samples are indicated with suffix "_T" and normal samples are indicated with suffix "_N"</p>
Assessment of the expression level of selected genes and protein phosphorylation of the PI3K/Akt signaling pathway in patients with colorectal cancer
Open the record for dataset details and reuse information.
Genomic and functional characterization of a mucosal symbiont involved in early-stage colorectal cancer
<p>Files Uploaded</p> <p>1. 16S Phylum level LDA analysis of colonoscopy biopsies </p> <p>16S_LDA_analysis_upload_20210710.tar.gz </p> <p>2. 16S DNA fastq files</p> <p>FASTQ_Generation_2019-03-16_17_29_28Z-167483978.zip</p> <p>3. Whole genome sequence analysis of b fragilis isolates from colonoscopy isolates</p> <p>WGS_b_fragilis_analysis_20210709_upload.tar.gz</p> <p>4. Sample sheet describing the b fragilis sample fastq files.</p> <p>16S sequencing sample sheet.docx</p> <p>5. b fragilis whole genome sequence fastq files</p> <p>Sample-*.fastq.gz</p>
Radiomics in hepatic metastasis by colorectal cancer
<p>We uploaded the images of the manuscript Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer. 2021 Jun 2;16(1):39. doi: 10.1186/s13027-021-00379-y. PMID: 34078424; PMCID: PMC8173908.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Hepatocellular carcinoma (HCC) Tumor microenvironment is more suppressive than colorectal cancer liver metastasis (CRLM) Tumor microenvironment.
<p><strong>Background and purpose:</strong> While HCC is an inflammation-associated cancer, CRLM develop on permissive healthy liver microenvironment. To evaluate the immune aspects of these two different environments, peripheral blood-(PB), peritumoral-(PT) and tumoral tissues-(TT) from HCC and CRLM patients were evaluated.</p> <p><strong>Methods:</strong> 40 HCC and 34 CRLM were enrolled and freshly TT, PT and PB were collected at the surgery. PB-, PT- and TT-derived CD4<sup>+</sup>CD25<sup>+ </sup>Tregs, M/PMN-MDSC and PB-derived CD4<sup>+</sup>CD25<sup>− </sup>Teffector cells (Teffs) were isolated and characterized. Tregs function was also evaluated in the presence of the CXCR4 inhibitor, Peptide-R29, AMD3100 or anti-PD1. RNA was extracted from PB/PT/TT-tissues and tested for FOXP3, CXCL12, CXCR4, CCL5, IL-15, CXCL5, Arg-1, N-cad, Vim, CXCL8, TGFβ and VEGF-A expression.</p> <p><strong>Results:</strong> In HCC/CRLM-PB higher number of functional Tregs, CD4<sup>+</sup>CD25<sup>hi</sup>FOXP3<sup>+</sup> were detected, although PB-HCC Tregs exert a more suppressive function as compared to CRLM-Tregs. In HCC/CRLM-TT Tregs were highly represented with Activated/ENTPD-1<sup>+</sup>Tregs prevalent in HCC. As compared to CRLM, HCC overexpressed CXCR4 and N-cadherin/Vimentin in a contest rich of arginase and CCL5. Monocytic-MDSCs were highly represented in HCC/CRLM while high Polymorphonuclear-MDSCs were detected only in HCC. Interestingly, CXCR4-PB-Tregs function was impaired in HCC/CRLM by the CXCR4 inhibitor R29.</p> <p><strong>Conclusion:</strong> In HCC and CRLM, peripheral blood, peritumoral and tumoral tissues-Tregs are highly represented and functional. Nevertheless, HCC display a more immunosuppressive TME due to Tregs, MDSCs, intrinsic tumor features (CXCR4, CCL5, arginase) and the contest in which it develops. As CXCR4 is overexpressed in HCC/CRLM tumor/TME cells, CXCR4 inhibitors may be considered for double hits therapy in liver cancer patients.</p>
Transcriptomics and proteomics reveal distinct biology for lymph node metastases and tumor deposits in colorectal cancer
<p>Spatial transcriptomic data (counts_DSP_afterQC_normalisation.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse tumor deposits and lymph node metastases from 10 patients with colorectal cancer. 264 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation, for further information on methods see associated publication. </p>
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
<p><strong>Background</strong></p> <p>Understanding the use of invasive procedures (IPs) at the end-of-life (EoL) is important to avoid under- and overtreatment, but epidemiologic analysis is hampered by limited methods to define treatment intent and EoL phase. This study applied novel methods to report IPs at the EoL using a colorectal cancer (CRC) case study.</p> <p><strong>Methods</strong></p> <p>An English population-based cohort of adult patients diagnosed between 2013 and 2015 was used with follow-up to 2018. Procedure intent (curative, non-curative, diagnostic) by cancer site and stage at diagnosis was classified by two surgeons independently. Joinpoint regression modelled weekly rates of IPs for 36 sub-cohorts of patients with incremental survival of 0-36 months. EoL phase was defined by a significant IP rate change before death. Zero-inflated Poisson regression explored associations between IP rates and clinical/sociodemographic variables.</p> <p><strong>Results</strong></p> <p>Of 87,731 patients included, 41,972 (48%) died. 9,492 procedures were classified by intent (interrater agreement 99.8%). Patients received 502,895 IPs (1.39 and 3.36 per person year for survivors and decedents). Joinpoint regression identified significant increases in IPs four weeks before death in those living 3-6 months, and eight weeks before death in those living 7–36 months from diagnosis. 7,908 (18.8%) patients underwent IPs at the EoL, with stoma formation the most common major procedure. Younger age, early-stage disease, men, lower comorbidity, those receiving chemotherapy and living longer from diagnosis were associated with IPs.</p> <p><strong>Conclusions</strong></p> <p>Methods to identify and classify IPs at the EoL were developed and tested within a CRC population. This approach can be now extended and validated to identify potential under- and overtreatment. </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.