16461 results for "sec+and+unspecified+malignant+neoplasm"

in 34.59 milliseconds.

• NKI-AvL OpACIN DNA-seq of stage III melanoma patients

  The dataset “NKI-AvL OpACIN DNA-seq of stage III melanoma patients" includes 2 x 18 normal and 2 x 18 tumor FASTQ files from paired-end whole exome sequencing on Illumina HiSeq2500 for 18 stage III melanoma patients.

  Dataset EGAD00001004217

• ICGC-LIRI-JP Release 15

  RIKEN collection WGS reads for 321 HCC and blood matched samples from 158 donors submitted to ICGC for release 15

  Dataset EGAD00001000808

• Metastatic breast cancer targeted gene screen (2014-09-24)

  The samples will be sequenced for a targeted panel of cancer relevant genes (n ~ 370) and analysed for somatic mutations. This dataset contains all the data available for this study on 2014-09-24

  Dataset EGAD00001001018

• IBDCA_Edinburgh

  In this experiment we have sequenced tumour normal pairs from patients presenting with CRC who have a prior history of inflammatory bowel disease. The idea is to identify driver mutations, new genes and novel pathways associated with the development of these malignancies.

  Dataset EGAD00001001330

• WGS on patients 5-7, study of metastatic prostate cancer

  Data from the study of subclonal metastatic expansion in prostate cancer. Whole genome shotgun sequencing of six samples, tumour and whole blood, from the three additional patients whose somatic variants were examined in depth.

  Dataset EGAD00001001344

• Metastatic Breast Cancer Whole Genome

  We propose to definitively characterise the somatic genetics of Metastatic breast cancer through generation of comprehensive catalogues of somatic mutations in Metastatic breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001000899

• Metastatic breast cancer targeted gene screen (2017-05-11)

  The samples will be sequenced for a targeted panel of cancer relevant genes (n ~ 370) and analysed for somatic mutations. This dataset contains all the data available for this study on 2017-05-11.

  Dataset EGAD00001003330

• Genomic Landscape of Chordoid Glioma

  This data set consist genomic information of 10 Chordoid Glioma samples: - Exome sequencing: 10 tumors and matched normal DNA for four of them (BAM files) - RNAseq : 10 tumors (fastq files) - CNV array: 9 tumors (IDAT files)

  Dataset EGAD00001004112

• Transposome Bisulfite Sequencing

  DNA belonging to 16 tumour/normal samples were treated with bisulfite, then up to 5 different bisulfite PCRs were performed in each one of the samples. Amplicons form the same sample were pooled and submitted to sequencing on a MiSeq platform.

  Dataset EGAD00001001028

• Genomic alteration in Korean Young Age Diffuse Gastric Cancers

  To identify recurrent somatic alterations in this unique subset of gastric cancers, whole exome and SNP6 analyses were performed using frozen cancer tissue. The somatic mutation analyses were also performed using blood of the same patients.

  Dataset EGAD00001001984

• Raw BulkRNA sequencing data

  BulkRNA sequencing Human Strand Specific mRNA (WOBI) 40M reads was performed by Novogene (Netherlands) using the NovaSeq X Plus Series (PE150, Q30≥85%) sequencing platform and strategy. 24 samples were included in the study, requiring 12 G of raw data per sample.

  Dataset EGAD50000001848

• Analysis of mutational and proteomic heterogeneity of gastric cancer to monitor post-treatment tumor burden using circulating tumor DNA

  It remains unclear whether complete spectrum of tumor heterogeneity is reflected in circulating tumor DNA (ctDNA). Here, we performed multi-regional target sequencing of primary tumors from 10 patients who underwent surgical resection for gastric cancer. A total of 126 non-synonymous mutations were identified from 30 samples from 10 tumors. Of these, 16 (12.7%) were likely mutations that occurred early in tumor development (i.e., present in all three regions) and were designated founder mutations. Among those validated with highly sensitive digital PCR (dPCR), 93% of non-founder mutations were found in all three regions. Variant allele frequencies (VAFs) of founder mutations were significantly higher than those of non-founder mutations. Phylogenetic analysis demonstrated that five of eight tumors (62.5%, excluding two hypermutator) had at least one identifiable truncal/founder mutation. These findings led us to prioritize founder mutations for ctDNA detection with individually-designed primer/probe sets for dPCR. Marked proteomic diversity was present across the tumor regions both within and between patients independent of mutational status. In preoperative plasma, the average ctDNA VAF of founder mutations was significantly higher than that of non-founder mutations (p = 0.039). Our present data demonstrated that high VAF mutations in the tumor likely represent founder or truncal mutations. However, mutation patterns alone appear insufficient to predict protein heterogeneity. These results suggest that, in practice, mutations having high VAF identified without multi-regional sequencing may be useful for ctDNA monitoring but are inadequate to fully predict proteomic heterogeneity of the tumor.

  Study JGAS000231

• Data Use Ontology (DUO)

  Data Use Ontology Data Use Ontology at EGA The EGA is committed to its involvement in the work of GA4GH. In an effort to enhance data discoverability and streamline data access, EGA have implemented the use of the Data Use Ontology (DUO), based on consent codes as described in Dyke et al. 2017. The Data Use Ontology codes will be displayed on the live dataset page of your submission to advise any would be requestor on how the data can be used and also to enhance data discoverability as users will be able to search on these codes to find applicable datasets. DUO can be browsed online via the Ontology Lookup Service Learn more reading the Data Use Ontology publication and the GA4GH Machine-Readable Consent Guidance! Check our DAC Portal Take The Tour and learn how to add DUO codes to your policy! Term Shorthand Label Description DUO:0000004 NRES no restriction This data use permission indicates there is no restriction on use. DUO:0000042 GRU general research use This data use permission indicates that use is allowed for general research use for any research purpose. DUO:0000006 HMB health or medical or biomedical research This data use permission indicates that use is allowed for health/medical/biomedical purposes; does not include the study of population origins or ancestry. DUO:0000007 DS disease specific research This data use permission indicates that use is allowed provided it is related to the specified disease. DUO:0000011 POA population origins or ancestry research only This data use permission indicates that use of the data is limited to the study of population origins or ancestry. DUO:0000012 RS research specific restrictions This data use modifier indicates that use is limited to studies of a certain research type. DUO:0000015 NMDS no general methods research This data use modifier indicates that use does not allow methods development research (e.g., development of software or algorithms). DUO:0000016 GSO genetic studies only This data use modifier indicates that use is limited to genetic studies only (i.e., studies that include genotype research alone or both genotype and phenotype research, but not phenotype research exclusively) DUO:0000018 NPUNCU not for profit, non commercial use only This data use modifier indicates that use of the data is limited to not-for-profit organizations and not-for-profit use, non-commercial use. DUO:0000019 PUB publication required This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community. DUO:0000020 COL collaboration required This data use modifier indicates that the requestor must agree to collaboration with the primary study investigator(s). DUO:0000021 IRB ethics approval required This data use modifier indicates that the requestor must provide documentation of local IRB/ERB approval. DUO:0000022 GS geographical restriction This data use modifier indicates that use is limited to within a specific geographic region. DUO:0000024 MOR publication moratorium This data use modifier indicates that requestor agrees not to publish results of studies until a specific date. DUO:0000025 TS time limit on use This data use modifier indicates that use is approved for a specific number of months. DUO:0000026 US user specific restriction This data use modifier indicates that use is limited to use by approved users. DUO:0000027 PS project specific restriction This data use modifier indicates that use is limited to use within an approved project. DUO:0000028 IS institution specific restriction This data use modifier indicates that use is limited to use within an approved institution. DUO:0000029 RTN return to database or resource This data use modifier indicates that the requestor must return derived/enriched data to the database/resource. DUO:0000043 CC clinical care use This data use modifier indicates that use is allowed for clinical use and care. DUO:0000044 NPOA population origins or ancestry research prohibited This data use modifier indicates use for purposes of population, origin, or ancestry research is prohibited. DUO:0000045 NPU not for profit organisation use only This data use modifier indicates that use of the data is limited to not-for-profit organisations. DUO:0000046 NCU non-commercial use only This data use modifier indicates that use of the data is limited to not-for-profit use. Point to Notice: For the consent code DUO:0000007 where data is restricted to use on a specific disease, please accompany it with an appropriate ontology from MONDO e.g., If the data is restricted to the use of research into juvenile idiopathic arthritis the code should be displayed as DUO:0000007; MONDO:0011429.

  Documentation access/data-access-committee/data-use-ontology

• Creatine in Huntington's Disease (HD) (CREST-E)

  This is a multi-center, randomized, double-blind, placebo-controlled trial of creatine monohydrate involving 553 ambulatory subjects with HD who are not requiring skilled care or institutionalization. Eligible subjects were randomized to receive up to 40 grams daily of creatine monohydrate or matching placebo. Randomization will be followed by a Titration Phase, a Maintenance Phase, an Optional Extended Maintenance Phase, and Washout. During the Titration Phase, which may last up to six months, study drug will be initially escalated in 5-gram increments starting at 5 g/day until the 40 g/day target dose is reached. Should subjects be unable to reach or stay at the maximum dose during the Titration Phase, they will remain on the highest tolerated dose (HTD) for which they have no significant side effects. During the Maintenance Phase, subjects will be followed prospectively and systematically. Subjects completing 36 months of follow-up on study drug will be eligible to continue in the study for an additional twelve months of follow-up in the Extended Maintenance Phase. The primary measure of efficacy is the annualized rate of change in TFC. The primary hypothesis of the study is that chronic treatment of early-stage HD subjects with creatine monohydrate will slow the progressive functional decline of HD.

  Study phs001488

• Ethiopia_Genome_Project__high_coverage_

  The Ethiopian area stands among the most ancient ones ever occupied by human populations and their ancestors. Particularly, according to archaeological evidences, it is possible to trace back the presence of Hominids up to at least 3 million years ago. Furthermore, the present day human populations show a great cultural, linguistic and historic diversity which makes them essential candidate to investigate a considerable part of the African variability. Following the typing of 300 Ethiopian samples on Illumina Omni 1M (see Human Variability in Ethiopia project, previously approved by the Genotyping committee) we now have a clearer idea on which populations living in the area include the most of the diversity.This project therefore aims to sequence the whole genome of 300 individuals at low (4-8x) depth belonging to the six most representative populations of the Ethiopian area to produce a unique catalogue of variants peculiar of the North East Africa. Furthermore 6 samples (one from each population) will also be sequenced at high (30x) depth to ensure full coverage of the diversity spectrum.The retrieved variants will be of great help in evaluating the demographic dynamics of those populations as well as shedding light on the migrations out of Africa.

  Study EGAS00001000237

• Ethiopia_Genome_Project__low_coverage_

  The Ethiopian area stands among the most ancient ones ever occupied by human populations and their ancestors. Particularly, according to archaeological evidences, it is possible to trace back the presence of Hominids up to at least 3 million years ago. Furthermore, the present day human populations show a great cultural, linguistic and historic diversity which makes them essential candidate to investigate a considerable part of the African variability. Following the typing of 300 Ethiopian samples on Illumina Omni 1M (see Human Variability in Ethiopia project, previously approved by the Genotyping committee) we now have a clearer idea on which populations living in the area include the most of the diversity.This project therefore aims to sequence the whole genome of 300 individuals at low (4-8x) depth belonging to the six most representative populations of the Ethiopian area to produce a unique catalogue of variants peculiar of the North East Africa. Furthermore 6 samples (one from each population) will also be sequenced at high (30x) depth to ensure full coverage of the diversity spectrum.The retrieved variants will be of great help in evaluating the demographic dynamics of those populations as well as shedding light on the migrations out of Africa.

  Study EGAS00001000238

• Detection of cancers three years prior to diagnosis using plasma cell-free DNA

  A major goal of early cancer detection is to identify subclinical disease when the tumor burden is low, so that treatments are more effective. But how early can cancers be detected prior to clinical signs or symptoms? This question can be answered only through the evaluation of participants whose clinical course has not been altered by the study itself. We here describe such an evaluation, performed on prospectively collected plasma samples from the Atherosclerosis Risk in Communities (ARIC) study, including 26 participants diagnosed with cancer and 26 matched controls. At the index time point, eight of these 52 participants scored positively with a multicancer early detection (MCED) blood test. All eight of these participants were diagnosed with cancer within 4 months after blood collection. In six of these 8 participants, we were able to assess an earlier plasma sample collected 3.1 to 3.5 years prior to clinical diagnosis. In four of these six participants, the same mutations detected by the MCED test could be identified, but at 8.6 to 79-fold lower levels. These results demonstrate that it is possible to detect circulating tumor DNA (ctDNA) more than three years prior to clinical diagnosis, and provide benchmark sensitivities required for the success of ctDNA-based tests for this purpose.

  Study EGAS00001008068

• Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

  Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

  Dataset EGAD00001006231

• Identification of biomarkers of response to preoperative talazoparib monotherapy in treatment naïve gBRCA+ breast cancers

  Germline mutations in BRCA1 or BRCA2 exist in ~2-7% of breast cancer patients, which has led to the approval of PARP inhibitors in the advanced setting. We have previously reported a phase II neoadjuvant trial of single agent talazoparib for patients with germline BRCA pathogenic variants with a pathologic complete response (pCR) rate of 53%. As nearly half of the patients treated did not have pCR, better strategies are needed to overcome treatment resistance. To this end, we conducted multi-omic analysis of 13 treatment naïve breast cancer tumors from patients that went on to receive single-agent neoadjuvant talazoparib. We looked for biomarkers that were predictive of response (assessed by residual cancer burden) after 6 months of therapy. We found that all resistant tumors exhibited either the loss of SHLD2, expression of a hypoxia signature, or expression of a stem cell signature. These results indicate that the deep analysis of pre-treatment tumors can identify biomarkers that are predictive of response to talazoparib and potentially other PARP inhibitors, and provides a framework that will allow for better selection of patients for treatment, as well as a roadmap for the development of novel combination therapies to prevent emergence of resistance.

  Study EGAS00001005676

• PML complete dataset

  We generated single-cell transcriptomics, single-nuclei chromatin accessibility, CITE-seq and Multiomics of patients with PML. Pools are genetically multiplexed across donors, genotypic variation is included in this dataset to enable demultiplexing.

  Dataset EGAD50000000197

• Netherlands Cancer Institute (NKI-AVL) general DAC

  Data is available under reasonable request. All data requests will be reviewed by the NKI IRB and must be supported by the Principal Investigator of the study. The researcher will need to sign a data access agreement with the NKI after approval. Go to https://ega.nki.nl to fill out the form to request access.

  Dac EGAC50000000055

• Single-cell BCR sequencing data of patients with T follicular helper cell lymphomas

  This dataset contains single-cell BCR sequencing data generated by the Cellranger pipeline (v3.1.0, 10X Genomics) from 5 LN and 1 PB samples of 5 patients with T follicular helper cell lymphomas (TFHLs) as well as 7 homeostatic LN (HLN) samples.

  Dataset EGAD50000000400

• Single-cell RNA sequencing data of patients with T follicular helper cell lymphomas

  This dataset contains single-cell count data generated by the Cellranger pipeline (v3.1.0, 10X Genomics) from 9 LN and 16 PB samples of 14 patients with T follicular helper cell lymphomas (TFHLs) as well as 7 homeostatic LN (HLN) samples.

  Dataset EGAD50000000402

• Single-cell TCR sequencing data of patients with T follicular helper cell lymphomas

  This dataset contains single-cell TCR sequencing data generated by the Cellranger pipeline (v3.1.0, 10X Genomics) from 9 LN and 16 PB samples of 14 patients with T follicular helper cell lymphomas (TFHLs) as well as 7 homeostatic LN (HLN) samples.

  Dataset EGAD50000000403

• Exome sequencing analysis of BCP-LBL patients samples

  In order to perform comprehensive SNV and CNA analyses of a cohort of BCP-LBL patients, we performed whole exome sequencing of 41 tissue samples from BCP-LBL patients. Because the material was available as FFPE, the target coverage of the samples was >200x.

  Dataset EGAD50000000418