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2,756 results for “Head and neck”
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients
<p><span>Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients.</span></p> <p><span>File <strong>data_os_gs.csv</strong> : data linking overall survival and gene signature scores</span></p> <p><span>File <strong>data_dfs_gs.csv</strong> : data linking disease-free survival and gene signature scores</span></p> <p><span><strong>Variables</strong>:</span></p> <ul> <li><span><em>supertreat_id</em>: patient ID</span></li> <li><span><em>GS_score_172GS</em>: gene signature score for the <em>172-GS</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_3clustersHPV</em>: gene signature score for the <em>3 clusters HPV</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_RSI</em>: gene signature score for the <em>radiosenstivity index (RSI) </em>signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_pancancerCisplatin</em>: gene signature score for the <em>pancancer-cisplatin</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_cl3Hypoxia</em>: gene signature score for the <em>Cl3-hypoxia</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span>Variables only available in <strong>data_os_gs.csv: </strong></span> <ul> <li><span><em>overall_survival_days_2years</em>: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_days_5years</em>: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_status_2years</em>: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> <li><span><em>overall_survival_status_5years</em>: Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> </ul> </li> </ul> <ul> <li><span>Variables only available in <strong>data_dfs_gs.csv:</strong></span> <ul> <li><span><em>disease_free_survival_days_2years</em>: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_days_5years</em>: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_status_2years</em>: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> <li><span><em>disease_free_survival_status_5years</em>: Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> </ul> </li> </ul>
ASPH and Hypoxia Marker Expression in Head and Neck Carcinomas: Implications for HPV-Associated Tumours
<p><strong>Data open:</strong> file with parametres used for the multivariate evaluation. </p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Benchmarking eliminative radiomic feature selection for head and neck lymph node classification - Supplemental data
<p>Supplementary files for the publication "Benchmarking eliminative radiomic feature selection for head and neck lymph node classification"</p>
Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing
<p>Circulating tumor DNA (ctDNA) in blood may become a generic biomarker for non-invasive cancer diagnosis and monitoring. However, detection of ctDNA is challenged by the presence of many circulating DNA molecules from healthy cells. We found that single ctDNA molecules can be sequenced with high accuracy by a three-step process consisting of capturing, copying and concatenation of the original double-stranded ctDNA molecules. This innovative approach - called CyclomicsSeq - is unparalleled by any other method in terms of cost-efficiency and speed, allowing point-of-care cancer diagnostics.</p> <p>Within this CPOC, subsidized by the Oncode institute, we have applied our CyclomicsSeq ctDNA test in patients with advanced head and neck cancer squamous cell carcinoma (HNSCC). Head and neck cancer (HNSCC) accounts for 380,000 cancer-related deaths worldwide. For these patients, determining whether a patient responds to the primary chemoradiation treatment is challenging, and non-responders are sometimes identified when other treatment options are no longer possible. By measuring the ctDNA levels in the blood of these patients prior to and during treatment, we aim to identify non-responders at an earlier stage.</p> <p>This dataset contains base calls of TP53 of 47 nanopore sequencing runs. We included 10 patients and 7 controls. For the patients, we have samples of multiple time points (0 = prior to treatment, 1 = 1 week after treatment initiation, etc).</p>
Fig. 4 in Head and neck posture in sauropod dinosaurs inferred from extant animals
Fig. 4. Range of possible habitual head angles in the basal sauropodomorph Massospondylus (A) and the sauropods: Camarasaurus (B) and Diplodocus (C). Heads shown with HSSC oriented horizontally, and tilted 30° upwards and 20° downwards, the range of habitual orientations found for birds by Duijm (1951). Black bars indicate the angles of the anterior necks in neutral position relative to heads with HSCCs held horizontal. Massospondylus BP/1/4376 after Sues et al. (2004: fig. 1A), Camarasaurus CM 11338 after Gilmore (1925: pl. 16), Diplodocus USNM 2672 after Hatcher (1901: pl. 2).
Fig. 1. Recent Cape hare Lepus capensis Linnaeus, 1758 RAM R2 in Head and neck posture in sauropod dinosaurs inferred from extant animals
Fig. 1. Recent Cape hare Lepus capensis Linnaeus, 1758 RAM R2 in right lateral view, illustrating maximally extended pose (A) and ONP (B): skull, cervical vertebrae 1–7 and dorsal vertebrae 1–2. Note the very weak dorsal deflection of the base of the neck in ONP, contrasting with the much stronger deflection illustrated in a live rabbit by Vidal et al. (1986: fig. 4).
Fig. 3 in Head and neck posture in sauropod dinosaurs inferred from extant animals
Fig. 3. Phylogeny indicating high−level relationships between tetrapod groups, habitual neck posture in extant groups, and inferred posture in sauropods. Cervical vertebrae shaded dark grey. Lissamphibia: Ambystoma tigrinum, after Simons et al. (2000: fig. 4); Mammalia: domestic cat Felis catus Linnaeus, 1758, after Vidal et al. (1986: fig. 3B); Testudines: box turtle Terrapene carolina (Linnaeus, 1758), after Landberg et al. (2003: fig. 8); Squamata: Savannah monitor Varanus exanthematicus (Bosc, 1792), after Owerkowicz et al. (1999: fig. 2A); Crocodylia: alligator Alligator mississippiensis (Daudin 1801), after unpublished photograph; Aves: chicken Gallus gallus (Linnaeus, 1758), after Vidal et al. (1986: fig. 7); Sauropoda: Diplodocus carnegii, modelled after vertebrae in Hatcher (1901: fig. 4, pl. 3).
Fig. 5 in Head and neck posture in sauropod dinosaurs inferred from extant animals
Fig. 5. Sauropod Brachiosaurus brancai reconstructions with low and high torso positions. Neck in ONP, in a drinking posture (A), and in a browsing posture (B) attained by deflecting the neck dorsally by the same amount as it is deflected ventrally to reach the ground. Torso, appendicular skeleton and ONP neck from Stevens and Parrish (2005b: fig. 6.8). Cervical joints deflected by 8° from ONP. See text for full details.
Fig. 2. Recent chicken Gallus domesticus Linnaeus, 1758 RAM R1 in Head and neck posture in sauropod dinosaurs inferred from extant animals
Fig. 2. Recent chicken Gallus domesticus Linnaeus, 1758 RAM R1 in right lateral view, illustrating maximally extended pose (A) and ONP (B): last four cervical and first four dorsal vertebrae. Note the strong ventral deflection of the base of the neck in ONP, contrasting with the very strong dorsal deflection illustrated in a live chicken by Vidal et al. (1986: fig. 7).
Valproic Acid Synergizes With Cisplatin and Cetuximab in vitro and in vivo in Head and Neck Cancer by Targeting the Mechanisms of Resistance - Unpublished data
<p>Antitumor effects of valproic acid (VPA) in combination with Cisplatin/Cetuximab doublet in head and neck squamous cell carcinoma (HNSCC) models. We reported unpublished data of the effects of this combination on cell cycle and 3D cell cultures</p>
Text-fig. 8. Middle-ear ossicles of Metacheiromys marshi, USNM-P 452349. a – left malleus (partial), incus, and stapes in ventral view; b – right malleus in oblique anterior view (left) and oblique posterior view (right). Abbreviations: acr – anterior crus, cb – crus breve, cl – crus longum, fp – footplate, iaf – inferior articular facet, ib – incudal body, lp – lateral process, mh – mallear head, mn – manubrium, mp – muscular process, n – neck, ol – osseous lamina (broken), pcr – posterior crus, sh – stapedial head, stf – stapedial foramen, suaf – superior articular facet. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 8. Middle-ear ossicles of Metacheiromys marshi, USNM-P 452349. a – left malleus (partial), incus, and stapes in ventral view; b – right malleus in oblique anterior view (left) and oblique posterior view (right). Abbreviations: acr – anterior crus, cb – crus breve, cl – crus longum, fp – footplate, iaf – inferior articular facet, ib – incudal body, lp – lateral process, mh – mallear head, mn – manubrium, mp – muscular process, n – neck, ol – osseous lamina (broken), pcr – posterior crus, sh – stapedial head, stf – stapedial foramen, suaf – superior articular facet.
AGMT (Arbeitsgemeinschaft medikamentöse Tumortherapie): Head and neck tumor registry Austria
<p>A metadataset describing the patient registry: Head and neck tumor registry Austria. </p>
Patritumab With Cetuximab and a Platinum Agent for Squamous Cell Carcinoma (Cancer) of the Head and Neck (SCCHN )
ClinicalTrials.gov study NCT02633800. IPD Sharing: YES. Countries: 7. Publications: 2.
Study of Efficacy and Safety of Buparlisib (BKM120) Plus Paclitaxel Versus Placebo Plus Paclitaxel in Recurrent or Metastatic Head and Neck Cancer Previously Pre-treated With a Platinum Therapy
ClinicalTrials.gov study NCT01852292. IPD Sharing: UNDECIDED. Countries: 18. Publications: 2.
F-18 Fluorothymidine PET Imaging for Early Evaluation of Response to Therapy in Head & Neck Cancer Patients
ClinicalTrials.gov study NCT00721799. IPD Sharing: NO. Countries: 1. Publications: 3.
Vertebral Artery and Cerebral Hemodynamics After Various Head Positions & Manipulation in Patients With Neck Pain
ClinicalTrials.gov study NCT02667821. IPD Sharing: YES. Countries: 1. Publications: 13.
Head-Neck-Radiomics-HN1 data processed with Pyradiomics ready to use for data analysis
<p>We downloaded <a href="https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-Radiomics-HN1">Head-Neck-Radiomics-HN1, </a>align CT and segmentation images and use PyRadiomics to extract all kind of features for data analysis.</p> <p>CSV contains all the features. Some of them are settings information, setting information should be remove before any data analysis. By using feature extraction algorithm or... by checking values, all the settings have the same values in the columns. Each row is a patient.</p> <p> </p> <p> </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.