GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: JAMA, American Medical Association (AMA), Vol. 314, No. 8 ( 2015-08-25), p. 811-
    Type of Medium: Online Resource
    ISSN: 0098-7484
    RVK:
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2015
    detail.hit.zdb_id: 2958-0
    detail.hit.zdb_id: 2018410-4
    SSG: 5,21
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Cancer Cell, Elsevier BV, Vol. 25, No. 3 ( 2014-03), p. 379-392
    Type of Medium: Online Resource
    ISSN: 1535-6108
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2014
    detail.hit.zdb_id: 2074034-7
    detail.hit.zdb_id: 2078448-X
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: Blood, American Society of Hematology, Vol. 120, No. 21 ( 2012-11-16), p. 2527-2527
    Abstract: Abstract 2527 Acute myeloid leukemia (AML) is a hematopoietic neoplasm with high mortality that is typically treated with daunorubicin/cytarabine induction chemotherapy. Alternative therapies with cytosine analogs such as decitabine are also used in some cases with a variable clinical response that some have estimated to be as high as 25%. The mechanism of these agents is unclear, but at low doses they produce passive DNA hypomethylation by inhibiting DNMT1. Although the impact of these drugs on cell growth and DNA methylation in AML cell lines has been evaluated1, studies using primary cells are limited; importantly, most have involved extended drug treatments that may be confounded by the differentiation of the treated cells2. In addition, some evidence suggests that decitabine has a differential effect on methylation in patients who respond to treatment2, but the utility of this phenotype as an in vitro biomarker for decitabine responsiveness is unknown. In this study, we used a novel in vitro culture system for primary leukemia cells to explore the initial genomic effects of short-term low dose decitabine on primary samples from 22 AML patients. Primary bone marrow or blood samples from these patients were cultured on HS27 stromal cells in DMEM supplemented with beta-mercaptoethanol and 15% FBS along with hSCF, hIL3, hIL-6, hTPO and hFLT3L for an initial 4-day period prior to daily treatment for 3 days with either 100 nM decitabine, 100 nM cytarabine, or vehicle controls. Cells were then evaluated for growth, cell cycle effects, and differentiation (by flow cytometry and morphologic evaluation). DNA was prepared from all samples for 5-methylcytosine content measurements by mass spectrometry, and 8 samples were selected for genome-wide methylation and gene expression profiling with the Illumina Human Methylation 450 and Affymetrix Human Exon 1.0ST array platforms. Mass spectrometry revealed a mean decrease in 5-mdC of 29% (range: 13% to 62%) in the decitabine-treated samples; in comparison, cytarabine treatment resulted in a mean increase in 5-mdC of 5% (range: −10% to 37%). Methylation arrays also showed a modest shift toward lower methylation values, but unsupervised hierarchical clustering demonstrated that methylation patterns were driven by sample-specific differences and not drug treatment. Analysis of methylation changes showed the most pronounced hypomethylation at CpGs with high baseline methylation levels, irrespective of CpG island and gene-based annotation, suggesting that the initial methylation status of each CpG is responsible for preferential effects of decitabine, rather than its genomic context. Methylation at promoter-associated CpGs showed a small but statistically significant negative correlation with change in gene expression, but expression changes at individual genes were not consistent across the samples, including genes previously shown to be regulated by methylation-dependent mechanisms (eg. CDKN2B and CDx H1). In addition to these findings, we observed that a sample from a long-term decitabine responder had an exaggerated in vitro response to decitabine (58% decrease in 5-mdC after 6 days of treatment), compared to a cohort of decitabine non-responders; a sample from a second patient also showed marked hypomethylation by both mass spectrometry and methylation array, although this patient was not treated with decitabine. While more investigation is needed, this observation might suggest that extreme in vitro hypomethylation in response to decitabine could serve as a biomarker for a clinical response. In summary, our study showed that short-term low dose decitabine treatment has modest but detectable effects on DNA methylation and gene expression, but these changes did not result in activation of any canonical gene expression pathway at this early time point. We found that the baseline methylation status of a CpG appears to be the best predictor of decitabine-induced hypomethylation, with highly methylated CpGs showing the greatest change. We also observed that hypomethylation is highly variable across primary samples and at specific genes, implying that single gene approaches for measuring decitabine effect may be problematic. Finally, extreme in vitro decitabine-induced hypomethylation should be further investigated as a biomarker for decitabine responsiveness. Disclosures: No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2012
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    In: Blood, American Society of Hematology, Vol. 121, No. 9 ( 2013-02-28), p. 1633-1643
    Abstract: Decitabine treatment of in vitro expanded primary AML samples leads to global hypomethylation. Highly methylated CpGs are most affected by decitabine-induced hypomethylation, with little influence on transcriptional activity.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2013
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    Online Resource
    Online Resource
    American Society for Clinical Investigation ; 2015
    In:  Journal of Clinical Investigation Vol. 126, No. 1 ( 2015-11-23), p. 85-98
    In: Journal of Clinical Investigation, American Society for Clinical Investigation, Vol. 126, No. 1 ( 2015-11-23), p. 85-98
    Type of Medium: Online Resource
    ISSN: 0021-9738 , 1558-8238
    Language: English
    Publisher: American Society for Clinical Investigation
    Publication Date: 2015
    detail.hit.zdb_id: 2018375-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_2 ( 2015-11-15), p. PR03-PR03
    Abstract: Acute myeloid leukemia is heterogeneous with respect to clinical outcome and molecular pathogenesis. Approximately 20% of AML cases are refractory to induction chemotherapy, and about 50% of patients ultimately relapse within a time interval that ranges from months to years. At the molecular level, diverse chromosomal abnormalities and genetic mutations have been observed across patients1. Although several clinical factors (age, white blood cell count), cytogenetic aberrations (t[15;17] translocation, loss of chromosome 5) 2-4, and genetic mutations (DNMT3A, FLT3) have been associated with differences in survival 5,6, these factors are of limited prognostic utility. Moreover, few studies have integrated sequence data with clinical and cytogentic factors to build predictive models of patient outcome. Here, we sought to identify genomic predictors of refractory disease or early relapse. We used whole genome and exome sequencing to analyze the genomes of 71 adult de novo AML patients treated with anthracycline and cytarabine-based induction chemotherapy. Of these, 34 had refractory disease or relapsed within 6 months, 12 relapsed in 6-12 months, and 25 had a long first remission ( & gt;12 months). We also developed an enhanced exome sequencing (EES) approach to identify and follow leukemia-associated variants over time. In 12 additional patients that achieved morphologic remission after induction chemotherapy, we used EES to identify and track variants at time of diagnosis, time of morphologic remission (roughly 30 days later), and a final time point corresponding to eventual relapse (n=8) or extended remission (n=4). No novel coding or non-coding variants present at the time of diagnosis were found to be predictive of refractory disease or early relapse. Using EES, however, we were able to detect leukemia-associated variants in the initial remission bone marrow in all eight patients who eventually relapsed. One persistent leukemia-associated variant was also detected in one patient still in remission, but all other variants in that patient were eliminated. We also detected 64 somatic variants that became enriched following chemotherapy, but were not detected in the original leukemic cells. These may represent relapse-specific variants or oligoclonal hematopoiesis after bone marrow recovery. Overall, our data suggest that the persistence of leukemia-associated variants after bone marrow recovery from cytotoxic therapy is strongly correlated with relapse, and may be used to complement more traditional, morphologic measures of leukemic cell clearance. 1. Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England Journal of Medicine 2013;368:2059-74. 2. Byrd JC, Mrozek K, Dodge RK, et al. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325-36. 3. Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 2010;116:354-65. 4. Schlenk RF, Dohner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. The New England Journal of Medicine 2008;358:1909-18. 5. Kihara R, Nagata Y, Kiyoi H, et al. Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia 2014;28:1586-95. 6. Ley TJ, Ding L, Walter MJ, et al. DNMT3A mutations in acute myeloid leukemia. The New England Journal of Medicine 2010;363:2424-33. This abstract is also presented as a poster at the Translation of the Cancer Genome conference. Citation Format: Jeffery M. Klco, Christopher A. Miller, Malachi Griffith, Allegra Petti, David H. Spencer, Shamika Ketkar-Kulkarni, Lukas D. Wartman, Matthew Christopher, Tamara L. Lamprecht, Jacqueline E. Payton, Jack Baty, Sharon E. Heath, Obi L. Griffith, Dong Shen, Jasreet Hundal, Gue Su Chang, Robert S. Fulton, Michelle O'laughlin, Catrina Fronick, Vincent Magrini, Ryan Demeter, David E. Larson, Shashikant Kulkarni, Bradley A. Ozenberger, John S. Welch, Matthew J. Walker, Timothy A. Graubert, Peter Westervelt, Jerald P. Radich, Daniel C. Link, Elaine R. Mardis, John F. DiPersio, Richard K. Wilson. Genomic approaches for risk assessment in acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR03.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 7
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 3531-3531
    Abstract: Acute promyelocytic leukemia (APL) is an AML subtype that is characterized by aberrant expansion of immature myeloid progenitors and precursors that are arrested at the promyelocyte stage. Almost all APL cases are characterized by the t(15;17)(q22;q11.2) translocation that creates the PML-RARA fusion oncogene. Human APL cells are known to have a canonical expression signature and a specific methylation phenotype that is unique to this form of AML. Our laboratory previously created a mouse model of APL by expressing a human PML-RARA cDNA from the mouse Cathepsin G (Ctsg) locus (Ctsg-PML-RARA), which activates human PML- RARA expression in early myeloid progenitor cells, with peak expression in promyelocytes. After a long latent period (6-12 months), ~60% of these mice develop a clonal, APL-like myeloid malignancy. The long latent period is probably due to the requirement for cooperating mutations that synergize with PML-RARA to accelerate the disease. Human APL samples have a unique gene expression signature that distinguishes them from all other subtypes of AML. We evaluated RNA-Seq data derived from Poly A+ enriched cDNAs obtained from purified promyelocytes derived from 3 young (6 week old) WT and 3 Ctsg-PML-RARA mice. We identified 779 annotated genes that are significantly dysregulated in murine promyelocytes expressing PML-RARA with a log2 fold change 〉 = 2 and P 〈 0.05. Some of these genes included Spib/Pu.1, Pou2af1, Jak2, Runx1, and many others. We also identified a set of 24,018 RNAs in promyelocytes that were defined as novel transcripts. This set contains 7,413 lncRNAs with an FPKM value of 〉 = 2. Differential expression analysis yielded 56 dysregulated lncRNA regions in PML-RARA expressing promyelocytes. To explore the association between gene dysregulation and DNA methylation in promyelocytes, we carried out whole-genome bisulfite sequencing using DNA derived from the purified promyelocytes of a 6 week old Ctsg-PML-RARA mouse, and a WT littermate. We generated a total of approximately 800 million sequencing reads, of which 78% mapped uniquely to the reference genome (mm9); we were able to map ~19 million CpGs with at least 10x coverage. Differential methylation analysis performed on ~4.5 million 1 Kb windows spanning the entire genome identified 17,633 differentially methylated regions with a mean difference of 〉 = 25% and a q-value of 〈 0.01, the vast majority of which (17,264, 98%) were hypomethylated in the Ctsg-PML-RARA promyelocytes. These windows overlap several known genes, including Runx1, Jak2, Dnmt3a, Gata2, and the Hoxa and Hoxb gene clusters. Using more strict criteria ( 〉 50% mean methylation difference), we identified 87 differentially methylated regions of at least 2 Kb in size. Of these 87 distinct regions, 74 (85%) were hypomethylated in PML-RARA promyelocytes, and 13 were hypermethylated; examples of both as shown in Figure 1. These data strongly suggest that PML-RARA has at least two distinct mechanisms by which it can modify DNA methylation. In regions where CpGs are hypomethylated, PML-RARA may be blocking the normal methylation of CpGs by the de novo DNA methyltransferases Dnmt3a and/or Dnmt3b. In contrast, PML-RARA may be directing de novo methyltransferases to act on the hypermethylated regions. Regardless, these data, when coupled with comprehensive chromatin accessibility mapping and complete RNA sequencing data, should provide new insights into the mechanisms used by PML-RARA to alter gene expression and initiate APL. Figure1. Examples of differentially methylated regions. Black=WT cells. Red=PML-RARA expressing cells. Each CpG in the region is represented as a dot. Scale is 0-100% methylated at each position. Top panel: a region on chromosome 8 that is hypomethylated in PML-RARA expressing promyelocytes. Bottom panel: a region on chromosome 4 that is hypermethylated in PML-RARA expressing promyelocytes. Figure1. Examples of differentially methylated regions. Black=WT cells. Red=PML-RARA expressing cells. Each CpG in the region is represented as a dot. Scale is 0-100% methylated at each position. Top panel: a region on chromosome 8 that is hypomethylated in PML-RARA expressing promyelocytes. Bottom panel: a region on chromosome 4 that is hypermethylated in PML-RARA expressing promyelocytes. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 8
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 21, No. 4 ( 2011-04), p. 626-633
    Abstract: Metagenomic characterization of complex biomes remains challenging. Here we describe a modification of digital karyotyping—biome representational in silico karyotyping (BRISK)—as a general technique for analyzing a defined representation of all DNA present in a sample. BRISK utilizes a Type IIB DNA restriction enzyme to create a defined representation of 27-mer DNAs in a sample. Massively parallel sequencing of this representation allows for construction of high-resolution karyotypes and identification of multiple species within a biome. Application to normal human tissue demonstrated linear recovery of tags by chromosome. We apply this technique to the biome of the oral mucosa and find that greater than 25% of recovered DNA is nonhuman. DNA from 41 microbial species could be identified from oral mucosa of two subjects. Of recovered nonhuman sequences, fewer than 30% are currently annotated. We characterized seven prevalent unknown sequences by chromosome walking and find these represent novel microbial sequences including two likely derived from novel phage genomes. Application of BRISK to archival tissue from a nasopharyngeal carcinoma resulted in identification of Epstein-Barr virus infection. These results suggest that BRISK is a powerful technique for the analysis of complex microbiomes and potentially for pathogen discovery.
    Type of Medium: Online Resource
    ISSN: 1088-9051
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2011
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 9
    In: Experimental Hematology, Elsevier BV, Vol. 44, No. 7 ( 2016-07), p. 603-613
    Type of Medium: Online Resource
    ISSN: 0301-472X
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 2005403-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_1 ( 2015-11-15), p. PR11-PR11
    Abstract: Acute myeloid leukemia is heterogeneous with respect to clinical outcome and molecular pathogenesis. Approximately 20% of AML cases are refractory to induction chemotherapy, and about 50% of patients ultimately relapse within a time interval that ranges from months to years. At the molecular level, diverse chromosomal abnormalities and genetic mutations have been observed across patients1. Although several clinical factors (age, white blood cell count), cytogenetic aberrations (t[15;17] translocation, loss of chromosome 5) 2-4, and genetic mutations (DNMT3A, FLT3) have been associated with differences in survival 5,6, these factors are of limited prognostic utility. Moreover, few studies have integrated sequence data with clinical and cytogentic factors to build predictive models of patient outcome. Here, we sought to identify genomic predictors of refractory disease or early relapse. We used whole genome and exome sequencing to analyze the genomes of 71 adult de novo AML patients treated with anthracycline and cytarabine-based induction chemotherapy. Of these, 34 had refractory disease or relapsed within 6 months, 12 relapsed in 6-12 months, and 25 had a long first remission ( & gt;12 months). We also developed an enhanced exome sequencing (EES) approach to identify and follow leukemia-associated variants over time. In 12 additional patients that achieved morphologic remission after induction chemotherapy, we used EES to identify and track variants at time of diagnosis, time of morphologic remission (roughly 30 days later), and a final time point corresponding to eventual relapse (n=8) or extended remission (n=4). No novel coding or non-coding variants present at the time of diagnosis were found to be predictive of refractory disease or early relapse. Using EES, however, we were able to detect leukemia-associated variants in the initial remission bone marrow in all eight patients who eventually relapsed. One persistent leukemia-associated variant was also detected in one patient still in remission, but all other variants in that patient were eliminated. We also detected 64 somatic variants that became enriched following chemotherapy, but were not detected in the original leukemic cells. These may represent relapse-specific variants or oligoclonal hematopoiesis after bone marrow recovery. Overall, our data suggest that the persistence of leukemia-associated variants after bone marrow recovery from cytotoxic therapy is strongly correlated with relapse, and may be used to complement more traditional, morphologic measures of leukemic cell clearance. 1. Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England Journal of Medicine 2013;368:2059-74. 2. Byrd JC, Mrozek K, Dodge RK, et al. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325-36. 3. Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 2010;116:354-65. 4. Schlenk RF, Dohner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. The New England Journal of Medicine 2008;358:1909-18. 5. Kihara R, Nagata Y, Kiyoi H, et al. Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia 2014;28:1586-95. 6. Ley TJ, Ding L, Walter MJ, et al. DNMT3A mutations in acute myeloid leukemia. The New England Journal of Medicine 2010;363:2424-33. Citation Format: Jeffery M. Klco, Christopher A. Miller, Malachi Griffith, Allegra Petti, David H. Spencer, Shamika Ketkar-Kulkarni, Lukas D. Wartman, Matthew Christopher, Tamara L. Lamprecht, Jacqueline E. Payton, Jack Baty, Sharon E. Heath, Obi L. Griffith, Dong Shen, Jasreet Hundal, Gue Su Chang, Robert S. Fulton, Michelle O'laughlin, Catrina Fronick, Vincent Magrini, Ryan Demeter, David E. Larson, Shashikant Kulkarni, Bradley A. Ozenberger, John S. Welch, Matthew J. Walker, Timothy A. Graubert, Peter Westervelt, Jerald P. Radich, Daniel C. Link, Elaine R. Mardis, John F. DiPersio, Richard K. Wilson. Genomic approaches for risk assessment in acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR11.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...