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  1. Article ; Online: MADGiC: a model-based approach for identifying driver genes in cancer.

    Korthauer, Keegan D / Kendziorski, Christina

    Bioinformatics (Oxford, England)

    2015  Volume 31, Issue 10, Page(s) 1526–1535

    Abstract: Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely ...

    Abstract Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely primarily on frequency-based criteria, where a gene is identified as having a driver mutation if it is altered in significantly more samples than expected according to a background model. Although useful, frequency-based methods are limited in that all mutations are treated equally. It is well known, however, that some mutations have no functional consequence, while others may have a major deleterious impact. The spatial pattern of mutations within a gene provides further insight into their functional consequence. Properly accounting for these factors improves both the power and accuracy of inference. Also important is an accurate background model.
    Results: Here, we develop a Model-based Approach for identifying Driver Genes in Cancer (termed MADGiC) that incorporates both frequency and functional impact criteria and accommodates a number of factors to improve the background model. Simulation studies demonstrate advantages of the approach, including a substantial increase in power over competing methods. Further advantages are illustrated in an analysis of ovarian and lung cancer data from The Cancer Genome Atlas (TCGA) project.
    MeSH term(s) Carcinoma, Squamous Cell/genetics ; Computational Biology/methods ; Computer Simulation ; DNA Mutational Analysis/methods ; Data Interpretation, Statistical ; Female ; Genome, Human ; Humans ; Lung Neoplasms/genetics ; Models, Statistical ; Mutation/genetics ; Neoplasm Proteins/genetics ; Ovarian Neoplasms/genetics
    Chemical Substances Neoplasm Proteins
    Language English
    Publishing date 2015-05-15
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btu858
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  2. Article ; Online: Methods for collapsing multiple rare variants in whole-genome sequence data.

    Sung, Yun Ju / Korthauer, Keegan D / Swartz, Michael D / Engelman, Corinne D

    Genetic epidemiology

    2014  Volume 38 Suppl 1, Page(s) S13–20

    Abstract: Genetic Analysis Workshop 18 provided whole-genome sequence data in a pedigree-based sample and longitudinal phenotype data for hypertension and related traits, presenting an excellent opportunity for evaluating analysis choices. We summarize the nine ... ...

    Abstract Genetic Analysis Workshop 18 provided whole-genome sequence data in a pedigree-based sample and longitudinal phenotype data for hypertension and related traits, presenting an excellent opportunity for evaluating analysis choices. We summarize the nine contributions to the working group on collapsing methods, which evaluated various approaches for the analysis of multiple rare variants. One contributor defined a variant prioritization scheme, whereas the remaining eight contributors evaluated statistical methods for association analysis. Six contributors chose the gene as the genomic region for collapsing variants, whereas three contributors chose nonoverlapping sliding windows across the entire genome. Statistical methods spanned most of the published methods, including well-established burden tests, variance-components-type tests, and recently developed hybrid approaches. Lesser known methods, such as functional principal components analysis, higher criticism, and homozygosity association, and some newly introduced methods were also used. We found that performance of these methods depended on the characteristics of the genomic region, such as effect size and direction of variants under consideration. Except for MAP4 and FLT3, the performance of all statistical methods to identify rare casual variants was disappointingly poor, providing overall power almost identical to the type I error. This poor performance may have arisen from a combination of (1) small sample size, (2) small effects of most of the causal variants, explaining a small fraction of variance, (3) use of incomplete annotation information, and (4) linkage disequilibrium between causal variants in a gene and noncausal variants in nearby genes. Our findings demonstrate challenges in analyzing rare variants identified from sequence data.
    MeSH term(s) Genetic Variation ; Genotype ; High-Throughput Nucleotide Sequencing ; Homozygote ; Humans ; Hypertension/genetics ; Hypertension/pathology ; Linkage Disequilibrium ; Pedigree ; Phenotype ; Polymorphism, Single Nucleotide ; Sequence Analysis, DNA/methods
    Language English
    Publishing date 2014-08-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 605785-8
    ISSN 1098-2272 ; 0741-0395
    ISSN (online) 1098-2272
    ISSN 0741-0395
    DOI 10.1002/gepi.21820
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  3. Article ; Online: Differential substrate use in EGF- and oncogenic KRAS-stimulated human mammary epithelial cells.

    Keibler, Mark A / Dong, Wentao / Korthauer, Keegan D / Hosios, Aaron M / Moon, Sun Jin / Sullivan, Lucas B / Liu, Nian / Abbott, Keene L / Arevalo, Orlando D / Ho, Kailing / Lee, Jennifer / Phanse, Aasavari S / Kelleher, Joanne K / Iliopoulos, Othon / Coloff, Jonathan L / Vander Heiden, Matthew G / Stephanopoulos, Gregory

    The FEBS journal

    2021  Volume 288, Issue 19, Page(s) 5629–5649

    Abstract: Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are ...

    Abstract Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are limited. Here, we examined the extent to which metabolic changes corresponding to oncogenic KRAS expression differed from those corresponding to epidermal growth factor (EGF)-driven proliferation in human mammary epithelial cells (HMECs). Removal of EGF from culture medium reduced growth rates and glucose/glutamine consumption in control HMECs despite limited changes in respiration and fatty acid synthesis, while the relative contribution of branched-chain amino acids to the TCA cycle and lipogenesis increased in the near-quiescent conditions. Most metabolic phenotypes measured in HMECs expressing mutant KRAS were similar to those observed in EGF-stimulated control HMECs that were growing at comparable rates. However, glucose and glutamine consumption as well as lactate and glutamate production were lower in KRAS-expressing cells cultured in media without added EGF, and these changes correlated with reduced sensitivity to GLUT1 inhibitor and phenformin treatment. Our results demonstrate the strong dependence of metabolic behavior on growth rate and provide a model to distinguish the metabolic influences of oncogenic mutations and nononcogenic growth.
    MeSH term(s) Animals ; Breast/growth & development ; Breast/pathology ; Breast Neoplasms/genetics ; Breast Neoplasms/metabolism ; Breast Neoplasms/pathology ; Carcinogenesis/genetics ; Cell Proliferation/genetics ; Epidermal Growth Factor/genetics ; Epithelial Cells/metabolism ; Epithelial Cells/pathology ; Female ; Gene Expression Regulation, Neoplastic/genetics ; Glucose/metabolism ; Glucose Transporter Type 1/antagonists & inhibitors ; Glucose Transporter Type 1/genetics ; Glutamic Acid/metabolism ; Glutamine/metabolism ; Humans ; Lactic Acid/metabolism ; Mammary Glands, Human/growth & development ; Mammary Glands, Human/pathology ; Proto-Oncogene Proteins p21(ras)/genetics ; Tumor Cells, Cultured
    Chemical Substances Glucose Transporter Type 1 ; KRAS protein, human ; SLC2A1 protein, human ; Glutamine (0RH81L854J) ; Lactic Acid (33X04XA5AT) ; Glutamic Acid (3KX376GY7L) ; Epidermal Growth Factor (62229-50-9) ; Proto-Oncogene Proteins p21(ras) (EC 3.6.5.2) ; Glucose (IY9XDZ35W2)
    Language English
    Publishing date 2021-05-13
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2173655-8
    ISSN 1742-4658 ; 1742-464X
    ISSN (online) 1742-4658
    ISSN 1742-464X
    DOI 10.1111/febs.15858
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  4. Article: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

    Korthauer, Keegan D / Chu, Li-Fang / Newton, Michael A / Li, Yuan / Thomson, James / Stewart, Ron / Kendziorski, Christina

    Genome biology. 2016 Dec., v. 17, no. 1

    2016  

    Abstract: The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the ... ...

    Abstract The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.
    Keywords gene expression ; gene expression regulation ; sequence analysis ; statistical analysis
    Language English
    Dates of publication 2016-12
    Size p. 222.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-016-1077-y
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  5. Article ; Online: High-throughput identification of RNA nuclear enrichment sequences.

    Shukla, Chinmay J / McCorkindale, Alexandra L / Gerhardinger, Chiara / Korthauer, Keegan D / Cabili, Moran N / Shechner, David M / Irizarry, Rafael A / Maass, Philipp G / Rinn, John L

    The EMBO journal

    2018  Volume 37, Issue 6

    Abstract: In the post-genomic era, thousands of putative noncoding regulatory regions have been identified, such as enhancers, promoters, long noncoding RNAs (lncRNAs), and a cadre of small peptides. These ever-growing catalogs require high-throughput assays to ... ...

    Abstract In the post-genomic era, thousands of putative noncoding regulatory regions have been identified, such as enhancers, promoters, long noncoding RNAs (lncRNAs), and a cadre of small peptides. These ever-growing catalogs require high-throughput assays to test their functionality at scale. Massively parallel reporter assays have greatly enhanced the understanding of noncoding DNA elements
    MeSH term(s) Cell Nucleus/genetics ; HeLa Cells ; High-Throughput Nucleotide Sequencing ; Humans ; In Situ Hybridization, Fluorescence ; RNA, Long Noncoding/genetics ; Sequence Analysis, RNA
    Chemical Substances RNA, Long Noncoding
    Language English
    Publishing date 2018-01-15
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 586044-1
    ISSN 1460-2075 ; 0261-4189
    ISSN (online) 1460-2075
    ISSN 0261-4189
    DOI 10.15252/embj.201798452
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  6. Article ; Online: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.

    Korthauer, Keegan D / Chu, Li-Fang / Newton, Michael A / Li, Yuan / Thomson, James / Stewart, Ron / Kendziorski, Christina

    Genome biology

    2016  Volume 17, Issue 1, Page(s) 222

    Abstract: The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the ... ...

    Abstract The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.
    MeSH term(s) Algorithms ; Computational Biology ; Gene Expression Profiling ; High-Throughput Nucleotide Sequencing/methods ; High-Throughput Nucleotide Sequencing/statistics & numerical data ; Humans ; RNA/genetics ; Sequence Analysis, RNA ; Single-Cell Analysis/methods ; Single-Cell Analysis/statistics & numerical data ; Software
    Chemical Substances RNA (63231-63-0)
    Language English
    Publishing date 2016-10-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6914 ; 1465-6906
    ISSN (online) 1474-760X ; 1465-6914
    ISSN 1465-6906
    DOI 10.1186/s13059-016-1077-y
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  7. Article: Differential substrate use in EGF‐ and oncogenic KRAS‐stimulated human mammary epithelial cells

    Keibler, Mark A. / Dong, Wentao / Korthauer, Keegan D. / Hosios, Aaron M. / Moon, Sun Jin / Sullivan, Lucas B. / Liu, Nian / Abbott, Keene L. / Arevalo, Orlando D. / Ho, Kailing / Lee, Jennifer / Phanse, Aasavari S. / Kelleher, Joanne K. / Iliopoulos, Othon / Coloff, Jonathan L. / Vander Heiden, Matthew G. / Stephanopoulos, Gregory

    FEBS journal. 2021 Oct., v. 288, no. 19

    2021  

    Abstract: Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are ...

    Abstract Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are limited. Here, we examined the extent to which metabolic changes corresponding to oncogenic KRAS expression differed from those corresponding to epidermal growth factor (EGF)‐driven proliferation in human mammary epithelial cells (HMECs). Removal of EGF from culture medium reduced growth rates and glucose/glutamine consumption in control HMECs despite limited changes in respiration and fatty acid synthesis, while the relative contribution of branched‐chain amino acids to the TCA cycle and lipogenesis increased in the near‐quiescent conditions. Most metabolic phenotypes measured in HMECs expressing mutant KRAS were similar to those observed in EGF‐stimulated control HMECs that were growing at comparable rates. However, glucose and glutamine consumption as well as lactate and glutamate production were lower in KRAS‐expressing cells cultured in media without added EGF, and these changes correlated with reduced sensitivity to GLUT1 inhibitor and phenformin treatment. Our results demonstrate the strong dependence of metabolic behavior on growth rate and provide a model to distinguish the metabolic influences of oncogenic mutations and nononcogenic growth.
    Keywords culture media ; epidermal growth factor ; epithelium ; fatty acids ; glucose ; glutamic acid ; glutamine ; humans ; lactic acid ; lipogenesis ; mutants ; tricarboxylic acid cycle
    Language English
    Dates of publication 2021-10
    Size p. 5629-5649.
    Publishing place John Wiley & Sons, Ltd
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 2173655-8
    ISSN 1742-4658 ; 1742-464X
    ISSN (online) 1742-4658
    ISSN 1742-464X
    DOI 10.1111/febs.15858
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  8. Article ; Online: Author Correction: Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

    Nuzzo, Pier Vitale / Berchuck, Jacob E / Korthauer, Keegan / Spisak, Sandor / Nassar, Amin H / Alaiwi, Sarah Abou / Chakravarthy, Ankur / Shen, Shu Yi / Bakouny, Ziad / Boccardo, Francesco / Steinharter, John / Bouchard, Gabrielle / Curran, Catherine R / Pan, Wenting / Baca, Sylvan C / Seo, Ji-Heui / Lee, Gwo-Shu Mary / Michaelson, M Dror / Chang, Steven L /
    Waikar, Sushrut S / Sonpavde, Guru / Irizarry, Rafael A / Pomerantz, Mark / De Carvalho, Daniel D / Choueiri, Toni K / Freedman, Matthew L

    Nature medicine

    2020  Volume 26, Issue 10, Page(s) 1663

    Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

    Abstract An amendment to this paper has been published and can be accessed via a link at the top of the paper.
    Language English
    Publishing date 2020-09-06
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-020-1078-y
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  9. Article ; Online: Reversal of viral and epigenetic HLA class I repression in Merkel cell carcinoma.

    Lee, Patrick C / Klaeger, Susan / Le, Phuong M / Korthauer, Keegan / Cheng, Jingwei / Ananthapadmanabhan, Varsha / Frost, Thomas C / Stevens, Jonathan D / Wong, Alan Yl / Iorgulescu, J Bryan / Tarren, Anna Y / Chea, Vipheaviny A / Carulli, Isabel P / Lemvigh, Camilla K / Pedersen, Christina B / Gartin, Ashley K / Sarkizova, Siranush / Wright, Kyle T / Li, Letitia W /
    Nomburg, Jason / Li, Shuqiang / Huang, Teddy / Liu, Xiaoxi / Pomerance, Lucas / Doherty, Laura M / Apffel, Annie M / Wallace, Luke J / Rachimi, Suzanna / Felt, Kristen D / Wolff, Jacquelyn O / Witten, Elizabeth / Zhang, Wandi / Neuberg, Donna / Lane, William J / Zhang, Guanglan / Olsen, Lars R / Thakuria, Manisha / Rodig, Scott J / Clauser, Karl R / Starrett, Gabriel J / Doench, John G / Buhrlage, Sara J / Carr, Steven A / DeCaprio, James A / Wu, Catherine J / Keskin, Derin B

    The Journal of clinical investigation

    2022  Volume 132, Issue 13

    Abstract: Cancers avoid immune surveillance through an array of mechanisms, including perturbation of HLA class I antigen presentation. Merkel cell carcinoma (MCC) is an aggressive, HLA-I-low, neuroendocrine carcinoma of the skin often caused by the Merkel cell ... ...

    Abstract Cancers avoid immune surveillance through an array of mechanisms, including perturbation of HLA class I antigen presentation. Merkel cell carcinoma (MCC) is an aggressive, HLA-I-low, neuroendocrine carcinoma of the skin often caused by the Merkel cell polyomavirus (MCPyV). Through the characterization of 11 newly generated MCC patient-derived cell lines, we identified transcriptional suppression of several class I antigen presentation genes. To systematically identify regulators of HLA-I loss in MCC, we performed parallel, genome-scale, gain- and loss-of-function screens in a patient-derived MCPyV-positive cell line and identified MYCL and the non-canonical Polycomb repressive complex 1.1 (PRC1.1) as HLA-I repressors. We observed physical interaction of MYCL with the MCPyV small T viral antigen, supporting a mechanism of virally mediated HLA-I suppression. We further identify the PRC1.1 component USP7 as a pharmacologic target to restore HLA-I expression in MCC.
    MeSH term(s) Antigens, Viral, Tumor/genetics ; Antigens, Viral, Tumor/metabolism ; Carcinoma, Merkel Cell/genetics ; Carcinoma, Merkel Cell/pathology ; Epigenesis, Genetic ; Humans ; Merkel cell polyomavirus/genetics ; Merkel cell polyomavirus/metabolism ; Polyomavirus Infections/genetics ; Skin Neoplasms/pathology ; Ubiquitin-Specific Peptidase 7/metabolism
    Chemical Substances Antigens, Viral, Tumor ; USP7 protein, human (EC 3.4.19.12) ; Ubiquitin-Specific Peptidase 7 (EC 3.4.19.12)
    Language English
    Publishing date 2022-06-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 3067-3
    ISSN 1558-8238 ; 0021-9738
    ISSN (online) 1558-8238
    ISSN 0021-9738
    DOI 10.1172/JCI151666
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  10. Article ; Online: Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

    Nuzzo, Pier Vitale / Berchuck, Jacob E / Korthauer, Keegan / Spisak, Sandor / Nassar, Amin H / Abou Alaiwi, Sarah / Chakravarthy, Ankur / Shen, Shu Yi / Bakouny, Ziad / Boccardo, Francesco / Steinharter, John / Bouchard, Gabrielle / Curran, Catherine R / Pan, Wenting / Baca, Sylvan C / Seo, Ji-Heui / Lee, Gwo-Shu Mary / Michaelson, M Dror / Chang, Steven L /
    Waikar, Sushrut S / Sonpavde, Guru / Irizarry, Rafael A / Pomerantz, Mark / De Carvalho, Daniel D / Choueiri, Toni K / Freedman, Matthew L

    Nature medicine

    2020  Volume 26, Issue 7, Page(s) 1041–1043

    Abstract: Improving early cancer detection has the potential to substantially reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a highly sensitive assay capable of detecting early-stage ... ...

    Abstract Improving early cancer detection has the potential to substantially reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a highly sensitive assay capable of detecting early-stage tumors. We report accurate classification of patients across all stages of renal cell carcinoma (RCC) in plasma (area under the receiver operating characteristic (AUROC) curve of 0.99) and demonstrate the validity of this assay to identify patients with RCC using urine cell-free DNA (cfDNA; AUROC of 0.86).
    MeSH term(s) Biomarkers, Tumor/genetics ; Carcinoma, Renal Cell/blood ; Carcinoma, Renal Cell/diagnosis ; Carcinoma, Renal Cell/genetics ; Carcinoma, Renal Cell/urine ; Cell-Free Nucleic Acids/blood ; Cell-Free Nucleic Acids/genetics ; Cell-Free Nucleic Acids/urine ; DNA Methylation/genetics ; Early Detection of Cancer ; Epigenome/genetics ; High-Throughput Nucleotide Sequencing ; Humans
    Chemical Substances Biomarkers, Tumor ; Cell-Free Nucleic Acids
    Language English
    Publishing date 2020-06-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-020-0933-1
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