ET
E. Taskesen
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3 records found
1
Erratum to
Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics (Scientific Reports, (2016), 6, 1, (24949), 10.1038/srep24949)
Journal article
(2018)
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Erdogan Taskesen, Sjoerd M.H. Huisman, Ahmed Mahfouz, Jesse H. Krijthe, Jeroen de Ridder, Anja van de Stolpe, Erik van den Akker, Wim Verhaegh, Marcel J.T. Reinders
This Article contains a typographical error in the spelling of the author Wim Verhaegh, which is incorrectly given as Wim Verheagh.
...
This Article contains a typographical error in the spelling of the author Wim Verhaegh, which is incorrectly given as Wim Verheagh.
Journal article
(2016)
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Erdogan Taskesen, Sjoerd Huisman, Ahmed Mahfouz, Jesse Krijthe, Jeroen de Ridder, A. van de Stolpe, Erik van den Akker, Wim Verhaegh, Marcel Reinders
The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.
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The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.
Poster
(2011)
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Erdogan Taskesen, Marije Havermans, Kirsten van Lom, Mathijs Sanders, Yvette van Norden, Eric Bindels, Remco Hoogenboezem, Marcel Reinders, Peter Valk, More Authors...
Acute Myeloid Leukemia is a highly diverse disease containing many cytogenetic and molecular abnormalities. We analyzed the DNA methylation
(DMP) and gene expression profiles (GEP) of 344 AML patients using an unsupervised and supervised approach. We hypothesized to better
characterize the disease phenotype by combing these features as these may result in specific patterns in cancer cells which reflect biological
differences. The unsupervised approach segregates patients into 18 clusters, among them six clusters that are defined by the World Health Organization, such as inv16, t(15;17), t(8;21) and CEBPA double mutants. In addition we identified four novel AML subtypes that could not be explained by the enrichment of any currently known recurrent cytogenetic, molecular, morphological or clinical feature. Two of these clusters are categorized with good stability. One of these cluster could be characterized with pathways that are involved in the accumulation of red blood cells and highly predictable using 21 GEP and 3 DMP features, whereas the other cluster is characterized with T-cell related pathways and highly predictable with 9 GEP and 4 DMP features. ...
(DMP) and gene expression profiles (GEP) of 344 AML patients using an unsupervised and supervised approach. We hypothesized to better
characterize the disease phenotype by combing these features as these may result in specific patterns in cancer cells which reflect biological
differences. The unsupervised approach segregates patients into 18 clusters, among them six clusters that are defined by the World Health Organization, such as inv16, t(15;17), t(8;21) and CEBPA double mutants. In addition we identified four novel AML subtypes that could not be explained by the enrichment of any currently known recurrent cytogenetic, molecular, morphological or clinical feature. Two of these clusters are categorized with good stability. One of these cluster could be characterized with pathways that are involved in the accumulation of red blood cells and highly predictable using 21 GEP and 3 DMP features, whereas the other cluster is characterized with T-cell related pathways and highly predictable with 9 GEP and 4 DMP features. ...
Acute Myeloid Leukemia is a highly diverse disease containing many cytogenetic and molecular abnormalities. We analyzed the DNA methylation
(DMP) and gene expression profiles (GEP) of 344 AML patients using an unsupervised and supervised approach. We hypothesized to better
characterize the disease phenotype by combing these features as these may result in specific patterns in cancer cells which reflect biological
differences. The unsupervised approach segregates patients into 18 clusters, among them six clusters that are defined by the World Health Organization, such as inv16, t(15;17), t(8;21) and CEBPA double mutants. In addition we identified four novel AML subtypes that could not be explained by the enrichment of any currently known recurrent cytogenetic, molecular, morphological or clinical feature. Two of these clusters are categorized with good stability. One of these cluster could be characterized with pathways that are involved in the accumulation of red blood cells and highly predictable using 21 GEP and 3 DMP features, whereas the other cluster is characterized with T-cell related pathways and highly predictable with 9 GEP and 4 DMP features.
(DMP) and gene expression profiles (GEP) of 344 AML patients using an unsupervised and supervised approach. We hypothesized to better
characterize the disease phenotype by combing these features as these may result in specific patterns in cancer cells which reflect biological
differences. The unsupervised approach segregates patients into 18 clusters, among them six clusters that are defined by the World Health Organization, such as inv16, t(15;17), t(8;21) and CEBPA double mutants. In addition we identified four novel AML subtypes that could not be explained by the enrichment of any currently known recurrent cytogenetic, molecular, morphological or clinical feature. Two of these clusters are categorized with good stability. One of these cluster could be characterized with pathways that are involved in the accumulation of red blood cells and highly predictable using 21 GEP and 3 DMP features, whereas the other cluster is characterized with T-cell related pathways and highly predictable with 9 GEP and 4 DMP features.