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  • The Paternal Epigenome Makes Its Mark

    Abstract Full Text
    JAMA. 2017; 317(20):2049-2051. doi: 10.1001/jama.2017.1566

    This Medical News article discusses how epigentic changes in a father’s sperm may influence his descendants’ health.

  • Imaging Epigenetics in the Human Brain

    Abstract Full Text
    JAMA. 2016; 316(13):1349-1349. doi: 10.1001/jama.2016.13667
  • JAMA October 4, 2016

    Figure: Imaging Epigenetics in the Human Brain

    A novel PET imaging probe can be used to visualize epigenetic activity in the human brain.
  • Diet-Induced Obesity and Diabetes Can Be Epigenetically Inherited

    Abstract Full Text
    JAMA. 2016; 315(17):1825-1825. doi: 10.1001/jama.2016.4948
  • Scientists Identify Genes Critical to Development of Leukemia

    Abstract Full Text
    JAMA. 2016; 315(9):860-860. doi: 10.1001/jama.2016.1486
  • Ushering Hypertension Into a New Era of Precision Medicine

    Abstract Full Text
    JAMA. 2016; 315(4):343-344. doi: 10.1001/jama.2015.18359

    This Viewpoint discusses the potential influence of precision medicine that incorporates epigenetic analysis in effectively treating hypertension.

  • Epigenetics at the Crossroads of Genes and the Environment

    Abstract Full Text
    JAMA. 2015; 314(11):1129-1130. doi: 10.1001/jama.2015.10414

    This Viewpoint summarizes new trends in the field of epigenetics and epigenetic epidemiology.

  • Epigenetics—The Link Between Infectious Diseases and Cancer

    Abstract Full Text
    JAMA. 2011; 305(14):1484-1485. doi: 10.1001/jama.2011.446
  • Germline Epigenetic Regulation of KILLIN in Cowden and Cowden-like Syndrome

    Abstract Full Text
    free access
    JAMA. 2010; 304(24):2724-2731. doi: 10.1001/jama.2010.1877
  • PTEN Promoter Silencing and Cowden Syndrome: The Role of Epigenetic Regulation of KILLIN

    Abstract Full Text
    JAMA. 2010; 304(24):2744-2745. doi: 10.1001/jama.2010.1863
  • JAMA February 10, 2010

    Figure 2: Comparisons of Mean Probability (or Level) of Oncogenic Pathway Activation Between Age Groups

    Error bars indicate 95% confidence intervals. WH indicates wound healing; TNF, tumor necrosis factor; STAT3, signal transducer and activator of transcription 3; CIN, chromosomal instability; EPI, epigenetic stem cell; IGS, invasiveness; EGFR, epidermal growth factor receptor. By unpaired, 1-tailed t tests, P<.001 for β-catenin and IGS; P<.01 for Src.
  • JAMA February 10, 2010

    Figure 6: Comparisons of Mean Probability (or Level) of Oncogenic Pathway Activation by Sex

    Error bars indicate 95% confidence intervals. WH indicates wound healing; TNF, tumor necrosis factor; STAT3, signal transducer and activator of transcription 3; CIN, chromosomal instability; EPI, epigenetic stem cell; IGS, invasiveness; EGFR, epidermal growth factor receptor. By unpaired, 1-tailed t tests, P<.05 for E2F1, EPI, and Myc; P<.01 for CIN, IGS, and WH.
  • Epigenetic Therapies Offer New Approach to Fighting Cancer at the Genetic Level

    Abstract Full Text
    JAMA. 2010; 303(3):213-214. doi: 10.1001/jama.2009.1914
  • JAMA April 2, 2008

    Figure 2: Hierarchical Clustering of All Patients From D1 Classified as Low Risk Based on the Clinicopathological Prognostic Model With Their Respective Cluster Assignments

    D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan-Meier survival plots yield the associated statistical significance (log-rank P ≤ .05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P = .05) are not reported. Clusters 1, 4, and 5 have prognostic significance. Clusters 1 and 5 represent patients with intermediate and good prognosis, respectively, and cluster 4 represents patients with the worst prognosis.
  • JAMA April 2, 2008

    Figure 3: Hierarchical Clustering of All Patients From D1 Classified as Intermediate Risk Based on the Clinicopathological Prognostic Model With Their Respective Cluster Assignments

    D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan-Meier survival plots yield the associated statistical significance (log-rank P ≤ .05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P = .05) are not reported. Clusters 2 and 3 have prognostic significance. Cluster 2 represents patients with good prognosis and cluster 3 represents patients with poor prognosis.
  • JAMA April 2, 2008

    Figure 4: Hierarchical Clustering of All Patients From D1 Classified as High Risk Based on the Clinicopathological Prognostic Model With Their Respective Cluster Assignments

    D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan-Meier survival plots yield the associated statistical significance (log-rank P ≤ .05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P = .05) are not reported. Clusters 1, 4, and 5 have prognostic significance. Clusters 1 and 5 represent patients with good to intermediate prognosis, and cluster 4 represents patients with poor prognosis.
  • JAMA April 2, 2008

    Figure 5: Independent Validation of the Prognostic Substratification Based on Signatures of Oncogenic Pathway Activation and Tumor Biology/Microenvironment Deregulation in D2

    D2 indicates validation data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Low-risk cohort: patterns of oncogenic pathway activation and tumor biology/microenvironment deregulation are shown as clusters (clusters 1 and 2) representing prognostic subphenotypes of the low-risk cohort illustrating that the patterns of pathway activation identified in the initial discovery data set (D1) are reproducible in D2. In this case, Kaplan-Meier survival analysis illustrates prognostic clusters (clusters 1 and 2), with No. of patients at risk reported at 25-month intervals of follow-up. Intermediate-risk cohort: Kaplan-Meier survival analysis illustrates prognostic clusters (clusters 1 and 5), along with their respective patterns of oncogenic pathway and tumor biology/microenvironment deregulation shown as a heatmap. High-risk cohort: Kaplan-Meier survival analysis demonstrates the prognostic significance of cluster 1 and cluster 3 along with their patterns of oncogenic pathway and tumor biology/microenvironment deregulation as a heatmap. Patterns observed in D2 are identical to the patterns of pathway activation observed in the prognostic clusters (within the high-risk cohort) identified in D1. Red color in the heatmaps indicates a high probability of deregulation and blue indicates a low probability of deregulation. The cluster numbers in D2 are not the same expression patterns as clusters defined in D1.
  • JAMA March 19, 2008

    Figure 1: Types of Epigenetic Information and Epigenetic Inheritance

    A, Types of epigenetic information. The term epigenetics refers to modifications of DNA or associated factors—aside from variations in the primary DNA sequence—that carry information content and are maintained during cell division. Examples of epigenetic modifications are DNA methylation, histone modifications, occupancy of chromatin factors, and changes in chromatin structure. CTCF indicates CCCTC-binding factor. B, Inheritance of DNA methylation. In somatic cells, epigenetic information is replicated during mitosis along with the DNA sequence. The mechanism for replication of DNA methylation is well understood but the mechanism for replication of histone modifications is not.
  • Epigenetics at the Epicenter of Modern Medicine

    Abstract Full Text
    JAMA. 2008; 299(11):1345-1350. doi: 10.1001/jama.299.11.1345
  • JAMA March 19, 2008

    Figure 2: Life Cycle of the Epigenome

    Unlike the DNA sequence, the epigenetic code changes during one's lifetime in ways specific to a given cell type. Shown here are a sperm, which is highly methylated, and an egg, which is not. After fertilization, there is a wave of demethylation that spares imprinted marks (dark brown). Tissue-specific methylation patterns emerge during later embryonic development. Age-related hypermethylation or hypomethylation could theoretically impair or enhance normal gene responsiveness to environmental signals.