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1,174 results for “cancer genomics”
Catalog of stool metagenome-assembled genomes from patients with different cancer types
<p><strong>A non-redundant catalog of 3,816 genomes with at least 75% completeness and no more than 15% contamination assembled from metagenomes. Samples of 976 metagenomes were obtained from patients receiving immunotherapy for the treatment of different types of cancers.</strong></p>
Joint AstraZeneca-Cancer Research Horizons Functional Genomics Centre's CRISPRn library benchmark screens: gRNA counts and associated metadata
<p>Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale. </p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Loss of multi-level 3D genome organization during breast cancer progression - Processed LAD files
<p>This entry contains the processed LAD files produced as part of the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p>
Loss of multi-level 3D genome organization during breast cancer progression - Third-party datasets
<p>This entry contains the following datasets:</p> <p>Datasets used by <a href="https://github.com/dixonlab/hic_breakfinder" target="_blank" rel="noopener">hic_breakfinder</a>:</p> <ul> <li>inter_expect_1Mb.hg38.txt</li> <li>intra_expect_100kb.hg38.txt</li> </ul> <p>FIles were originally downloaded from <a href="https://salkinstitute.box.com/s/m8oyv2ypf8o3kcdsybzcmrpg032xnrgx" target="_blank" rel="noopener">this</a> URL.</p> <p>Datasets used by <a href="https://github.com/parklab/HiNT" target="_blank" rel="noopener">HiNT</a>:</p> <ul> <li>backgroundMatrices_hg38.zip</li> <li>refData_hg38.zip</li> </ul> <p>Files were originally downloaded from the following URLs: <a href="http://compbio.med.harvard.edu/hint/refData/" target="_blank" rel="noopener">link1</a>, <a href="http://compbio.med.harvard.edu/hint/backgroundMatrices/" target="_blank" rel="noopener">link2</a>.</p> <p>The above datasets are used by the data analysis workflows hosted at <a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression" target="_blank" rel="noopener">paulsengroup/2022-mcf10a-cancer-progression.</a><br>The results produced by running the workflows from the above repository were used as part of the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p>
Supporting data for RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data.
<p>This is the data repository for <em>RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data</em>. To use this dataset, please refer to <a href="https://github.com/HowardGech/RCANE" target="_blank" rel="noopener">https://github.com/HowardGech/RCANE</a>.</p>
Dataset for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"
<p>Datasets for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"</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>
Study of DS-1062a in Advanced or Metastatic Non-small Cell Lung Cancer With Actionable Genomic Alterations (TROPION-Lung05)
ClinicalTrials.gov study NCT04484142. IPD Sharing: YES. Countries: 10. Publications: 1.
Study of DS-1062a Versus Docetaxel in Previously Treated Advanced or Metastatic Non-small Cell Lung Cancer With or Without Actionable Genomic Alterations (TROPION-LUNG01)
ClinicalTrials.gov study NCT04656652. IPD Sharing: YES. Countries: 26. Publications: 2.
Reproducible data, example subsets, and analysis pipeline for the extended TAaCGH study of breast cancer genomic and transcriptomic profiles
Open the record for dataset details and reuse information.
R data set: The Cancer Genome Atlas Gene Expression data
<p>This compound data set comprises the following information from the The Cancer Genome Atlas:</p> <ul> <li>RNA-Seq counts for 60483 genes across 11093 samples</li> <li>HuEx 1.0 ST gene expression data for 18632 genes across 1211 samples</li> <li>clinical indicators for 11160 patients</li> </ul> <p>All gene expression data is annotated across ENSEMBL, ENTREZ and symbols. Samples are annotated by TCGA barcodes.</p> <p>To read the data set into R (requires 6 GB of RAM) use:</p> <p>tcga <- readRDS("tcga.rds")</p>
de novo genome assembly of the LNCaP human prostate cancer cell line
<p>Whole-genome sequencing reads from the LNCaP human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p>
de novo genome assembly of the PC3 human prostate cancer cell line
<p>Whole-genome sequencing reads from the PC3 human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p> <p> </p> <p> </p> <p> </p>
Deciphering genomic evolution of metastatic organotropism with 535 paired primary lung cancers and metastases
<p>Scripts and processed data used in the analyses of the manuscript:</p> <p><em>Deciphering genomic evolution of metastatic organotropism with 535 paired primary lung cancers and metastases.</em></p>
The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome
<p>Data associated with my PhD thesis titled "The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome".</p>
Loss of multi-level 3D genome organization during breast cancer progression - FISH dataset
<p>This entry contains the raw and processed FISH images produced by the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p> <p>The raw images contained in file 2022-mcf10a-cancer-progression-fish-db.tar.gz were processed using fish data analysis workflow (<a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression/blob/main/run_fish.sh" target="_blank" rel="noopener">link</a>) hosted at <a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression" target="_blank" rel="noopener">paulsengroup/2022-mcf10a-cancer-progression</a>.<br>The resulting files have been archived in file 2022-mcf10a-cancer-progression-fish-processed-data.tar.gz.</p>
Metagenome-assembled genomes obtained from fecal and salivary microbiomes of pancreatic cancer patients and controls
<p>7,546 MAGs obtained from fecal and salivary metagenomes of pancreatic cancer patients and controls</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.