Evidence map›Paper›PMID 25349303›Full record

ArticleMolecular cancer therapeutics2014

Utilization of the Eμ-Myc mouse to model heterogeneity of therapeutic response.

Rachel E Rempel, Xiaolei Jiang, Paul Fullerton, Tuan Zea Tan, Jieru Ye, Jieying Amelia Lau, Seiichi Mori, Jen-Tsan Chi, Joseph R Nevins, Daphne R Friedman

Open access · greenAbstract read
In one paragraph

Article in Molecular cancer therapeutics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact, top 88% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 20 citations in OpenAlex.

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  12. Mouse Models ofFrontiers in immunology · 2020
    Review
  13. Uncovering the Role of RNA-Binding Protein hnRNP K in B-Cell Lymphomas.Journal of the National Cancer Institute · 2020
    Article
  14. Article
  15. Eμ-TCL1xMyc: A Novel Mouse Model for Concurrent CLL and B-Cell Lymphoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 3 countries.

Rachel E RempelDuke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina.
Xiaolei JiangDuke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina.
Paul FullertonDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
Tuan Zea TanCancer Science Institute of Singapore, National University of Singapore, Singapore.
Jieru YeCancer Science Institute of Singapore, National University of Singapore, Singapore.
Jieying Amelia LauCancer Science Institute of Singapore, National University of Singapore, Singapore.
Seiichi MoriThe Cancer Institute, Japanese Foundation of Cancer Research, Tokyo, Japan.
Jen-Tsan ChiDuke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina. Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina.
Joseph R NevinsDuke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina. Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina.
Daphne R FriedmanDuke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina. Department of Medicine, Duke University Medical Center, Durham, North Carolina. Durham Veterans Affairs Medical Center, Durham, North Carolina. daphne.friedman@duke.edu.
Duke University · USNational University of Singapore · SGBaylor Genetics · USJapanese Foundation For Cancer Research · JP

Funding

Women's Cancer Research ProgramP30CA014236 · NCI · DUKE UNIVERSITY · PI DONNA NIEDZWIECKI · 1985 to 2026
$174.8M
Gene expression programs of lactic acidosis in human cancersR01CA125618 · NCI · DUKE UNIVERSITY · PI CHI, JEN-TSAN ASHLEY · 2007 to 2016
$3.3M
Oncogenic Gene Regulatory NetworksR01CA106520 · NCI · DUKE UNIVERSITY · PI CHI, JEN-TSAN ASHLEY · 2005 to 2014
$2.9M
NCI NIH HHS CA106520NCI NIH HHS P30 CA014236NCI NIH HHS R01 CA106520NCI NIH HHS R01 CA125618
6 · The paper itself

Abstract

Human aggressive B-cell non-Hodgkin lymphomas (NHL) encompass the continuum between Burkitt lymphoma and diffuse large B-cell lymphoma (DLBCL), and display considerable clinical and biologic heterogeneity, most notably related to therapy response. We previously showed that lymphomas arising in the Eμ-Myc transgenic mouse are heterogeneous, mirroring genomic differences between Burkitt lymphoma and DLBCL. Given clinical heterogeneity in NHL and the need to develop strategies to match therapeutics with discrete forms of disease, we investigated the extent to which genomic variation in the Eμ-Myc model predicts response to therapy. We used genomic analyses to classify Eμ-Myc lymphomas, link Eμ-Myc lymphomas with NHL subtypes, and identify lymphomas with predicted resistance to conventional and NF-κB-targeted therapies. Experimental evaluation of these predictions links genomic profiles with distinct outcomes to conventional and targeted therapies in the Eμ-Myc model, and establishes a framework to test novel targeted therapies or combination therapies in specific genomically defined lymphoma subgroups. In turn, this will rationally inform the design of new treatment options for aggressive human NHL.

Indexed as

Genes, mycAnimalsAntineoplastic Combined Chemotherapy ProtocolsCluster AnalysisDisease Models, AnimalDisease ProgressionGene Expression ProfilingHumansLymphoma, B-CellMiceMice, TransgenicMolecular Targeted TherapyPrognosisSignal TransductionSpecies SpecificityTreatment Outcome

Identifiers

PMID25349303
PMCPMC4258467
OpenAlexW2060056450

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.