Evidence map›Paper›PMID 19657360›Full record

ReviewLeukemia2009

The molecular characterization and clinical management of multiple myeloma in the post-genome era.

Y Zhou, B Barlogie, J D Shaughnessy

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Leukemia, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
7.0field-weighted citation impact, top 2% 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.

  1. Multiple Myeloma, Version 2.2016: Clinical Practice Guidelines in Oncology.Journal of the National Comprehensive Cancer Network : JNCCN · 2015
    Guideline
  2. Trial
  3. Trial
  4. Trial
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Robust Sampling of Defective Pathways in Multiple Myeloma.International journal of molecular sciences · 2019
    Article
  14. Treatment approach for young, fit, newly diagnosed multiple myeloma patients.Hematology. American Society of Hematology. Education Program · 2018
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Observational
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

3 authors at 1 institution in 1 country.

Y ZhouDonna D and Donald M Lambert Laboratory for Myeloma Genetics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
B Barlogie
J D Shaughnessy
University of Arkansas for Medical Sciences · US

Funding

Tumor Cell-Microenvironment Interactions in the Molecular Pathogenesis of MultiplP01CA055819 · NCI · UNIV OF ARKANSAS FOR MED SCIS · PI BELLAMY, WILLIAM T · 1993 to 2013
$49.7M
Molecular Diagnosis and Prognosis of Multiple MyelomaR33CA097513 · NCI · UNIVERSITY OF ARKANSAS MED SCIS LTL ROCK · PI SHAUGHNESSY, JOHN DAMIAN · 2002 to 2004
$1.4M
NCI NIH HHS CA55819-09NCI NIH HHS CA97513-01NCI NIH HHS P01 CA055819NCI NIH HHS R33 CA097513
6 · The paper itself

Abstract

Cancer-causing mutations disrupt coordinated, precise programs of gene expression that govern cell growth and differentiation. Microarray-based gene-expression profiling (GEP) is a powerful tool to globally analyze these changes to study cancer biology and clinical behavior. Despite overwhelming genomic chaos in multiple myeloma (MM), expression patterns within tumor samples are remarkably stable and reproducible. Unique expression patterns associated with recurrent chromosomal translocations and ploidy changes defined molecular classes with differing clinical features and outcomes. Combined molecular techniques also dissected two distinct, reproducible forms of hyperdiploid disease and have molecularly defined MM with high risk for poor clinical outcome. GEP is now used to risk-stratify patients with newly diagnosed MM. Groups with high-risk features are evident in all GEP-defined MM classes, and GEP studies of serial samples showed that risk increases over time, with relapsed disease showing dramatic GEP shifts toward a signature of poor outcomes. This suggests a common mechanism of disease evolution and potentially reflects preferential expansion of therapy-resistant cells. Correlating GEP-defined disease class and risk with outcomes of therapeutic regimens reveals class-specific benefits for individual agents, as well as mechanistic insights into drug sensitivity and resistance. Here, we review modern genomics contributions to understanding MM pathogenesis, prognosis, and therapy.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticGenomicsHumansMultiple MyelomaPrognosisRisk Factors

Identifiers

PMID19657360
PMCPMC3686133
OpenAlexW2008886056

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.