Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,125
datasets available to search
ShareScore release 0.7.1
Dataset results
7,125 results for “prostate cancer”
Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
Data and code for paper "Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer."
<p>This repository contains raw data and code for the manuscript with DOI: 10.1038/s42003-020-01529-5.</p>
Isoform specific activities of androgen receptor and its splice variants in prostate cancer cells
<p>Androgen receptor (AR) signaling continues to drive castration resistant prostate cancer (CRPC) in spite of androgen deprivation therapy (ADT). Constitutively active shorter variants of AR, lacking the ligand binding domain, are frequently expressed in CRPC and have emerged as a potential mechanism for prostate cancer to escape ADT. ARv7 and AR<sup>v567es </sup>are two of the most commonly detected variants of AR in clinical samples of advanced, metastatic prostate cancer. It is not clear if variants of AR merely act as weaker substitutes for AR or can mediate unique isoform specific activities different from AR. In this study, we employed LNCaP prostate cancer cell lines with inducible expression of ARv7 or AR<sup>v567es </sup>to delineate similarities and differences in transcriptomics, metabolomics and lipidomics resulting from the activation of AR, ARv7 or AR<sup>v567es</sup>. While the majority of target genes were similarly regulated by the action of all three isoforms, we found a clear difference in transcriptomic activities of AR versus the variants, and a few differences between ARv7 and AR<sup>v567es</sup>. Some of the target gene regulation by AR isoforms was similar in the VCaP background as well. Differences in downstream activities of AR isoforms were also evident from comparison of the metabolome and lipidome in an LNCaP model. Overall our study implies that shorter variants of AR are capable of mediating unique downstream activities different from AR and some of these are isoform specific.</p>
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.
Data from: Oral contraceptive use is associated with prostate cancer: an ecologic study
INTRODUCTION: Recently there have been several studies suggesting that estrogen exposure may increase the risk of prostate cancer (PCa). In this report we examine associations between PCa incidence and mortality and population-based use of oral contraceptives (OC's). We hypothesized that OC's by-products may cause an environmental contamination leading to an increased low level estrogen exposure and therefore higher PCa incidence and mortality. METHODS: The hypothesis was studied in an ecologic study. We used data from the "international agency for research on cancer" (IACR) to retrieve age-standardized rates of prostate cancer in 2007 and the "United Nations 2007 use of contraceptive report" to retrieve data on contraceptive use. We subsequently used a Pearson correlation and a multivariable linear regression to associate the percentage of women using OC's, intrauterine devices, condoms or vaginal barriers to the age standardized prostate cancer incidence and mortality. We performed these analyses by individual nation and by continent worldwide. RESULTS: OC's use was significantly associated with prostate cancer incidence and mortality in the individual nation world wide (r=0.61 and r=0.53, respectively p<0.05 for all). PCa incidence was also associated with OC's use in Europe (r=0.545 p<0.05) and by continent (r=0.522 p<0.05). All other forms of contraceptives (i.e. intra-uterine devices, condoms or vaginal barriers) were not correlated with prostate cancer incidence or mortality. On multivariable analysis the correlation with OC was independent of nation's wealth. CONCLUSION: In this hypothesis generating ecologic study we have demonstrated a significant association between OC's and PCa. We hypothesize that oral contraceptive effect may be mediated through environmental estrogen levels; this novel concept is worth further investigation.
Prostate Cancer Metastasis Cardiometabolic Diseases Dataset, Meta-Analysis & Systematic Review
Open the record for dataset details and reuse information.
Automatic Segmentation of Prostate Cancer using Deep Learning
<p>Demo of Prostate Cancer Segmentation on 3D Magnetic Resonance Imaging using deep learning</p>
Large Language Models and prostate cancer
Open the record for dataset details and reuse information.
1H-NMR data for prostate cancer and benign prostatic hyperplasia
Open the record for dataset details and reuse information.
radiomics in prostate cancer
Open the record for dataset details and reuse information.
Microwell-Based Flow Culture Increases Viability and Restores Drug Response in Prostate Cancer Spheroids
<p>Data files for main manuscript figures.</p>
High Resolution MRI Study for Prostate Cancer
ClinicalTrials.gov study NCT03292874. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ABT-751 in Treating Patients With Metastatic Prostate Cancer That Did Not Respond to Hormone Therapy
ClinicalTrials.gov study NCT00471718. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Medical Nutrition Therapy or Standard Care in Treating Patients With Lung Cancer, Pancreatic Cancer, or Stage III or Stage IV Prostate Cancer
ClinicalTrials.gov study NCT00769652. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Genetic Testing for Breast, Ovarian, Pancreatic, and Prostate Cancers
ClinicalTrials.gov study NCT04330716. IPD Sharing: YES. Countries: 1. Publications: 0.
Study of SRF617 With AB928 (Etrumadenant) and AB122 (Zimberelimab) in Patients With Metastatic Castration Resistant Prostate Cancer
ClinicalTrials.gov study NCT05177770. IPD Sharing: NO. Countries: 2. Publications: 0.
One Month Degarelix/Comparator Treatment for Prostate Cancer in Chinese Population
ClinicalTrials.gov study NCT01744366. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hormone Therapy Plus Chemotherapy as Initial Treatment for Local Failures or Advanced Prostate Cancer
ClinicalTrials.gov study NCT02560051. IPD Sharing: NO. Countries: 1. Publications: 0.
Developing Inclusive Support and Intervention for Spanish-speaking Latiné Prostate Cancer Survivors
ClinicalTrials.gov study NCT06435871. IPD Sharing: YES. Countries: 1. Publications: 0.
Efficacy and Safety of Zactima™ in Patients With Castration-refractory Metastatic Prostate Cancer
ClinicalTrials.gov study NCT00659438. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.