Evidence map›Paper›PMID 24088394›Full record

ArticleBMC genomics2013

The shaping and functional consequences of the dosage effect landscape in multiple myeloma.

Mehmet K Samur, Parantu K Shah, Xujun Wang, Stéphane Minvielle, Florence Magrangeas, Hervé Avet-Loiseau, Nikhil C Munshi, Cheng Li

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Genetic Predictors of Mortality in Patients with Multiple Myeloma.The application of clinical genetics · 2021
    Review
  5. Article
  6. Article
  7. Non-coding RNA · 2018
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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

8 authors at 6 institutions in 3 countries.

Mehmet K SamurDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA. Nikhil_Munshi@dfci.harvard.edu.
Parantu K Shah
Xujun Wang
Stéphane Minvielle
Florence Magrangeas
Hervé Avet-Loiseau
Nikhil C Munshi
Cheng Li
Dana-Farber Cancer Institute · USCentre Hospitalier Universitaire de Nantes · FRInserm · FRNantes Université · FRTongji University · CNVA Boston Healthcare System · US

Funding

Trageting the Multiple Myeloma EpigenomeP50CA100707 · NCI · DANA-FARBER CANCER INSTITUTE · PI ANDERSON, KENNETH C., MUNSHI, NIKHIL C. · 2003 to 2023
$45.0M
Targeting Genomic Instability and Evolution in MyelomaP01CA155258 · NCI · DANA-FARBER CANCER INST · PI Jill Corre · 2011 to 2026
$32.5M
Targeting Myeloma Cell-Host Bone Marrow InteractionsP01CA078378 · NCI · DANA-FARBER CANCER INSTITUTE · PI RITZ, JEROME · 1998 to 2014
$26.2M
dChip:Analysis and Visualization of Oligonucleotide Expression and SNP ArraysR01GM077122 · NIGMS · DANA-FARBER CANCER INST · PI LI, CHENG · 2008 to 2010
$1.1M
NCI NIH HHS P01 CA078378NCI NIH HHS P01 CA155258NCI NIH HHS P50 CA100707NIGMS NIH HHS R01 GM077122PHS HHS P01-155258PHS HHS P01-78378PHS HHS P50-100007PHS HHS R01-124929
6 · The paper itself

Abstract

backgroundMultiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs.

resultsWe propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers.

conclusionsOur findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.

Indexed as

Chromosomes, HumanCluster AnalysisDatabases, GeneticDiploidyDNA Copy Number VariationsExonsGene DosageGene Expression ProfilingGene Expression Regulation, NeoplasticGenes, NeoplasmGenetic HeterogeneityHumansKaryotypingMultiple MyelomaOligonucleotide Array Sequence AnalysisPolymorphism, Single Nucleotide

Identifiers

PMID24088394
PMCPMC3907079
OpenAlexW2118820750

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.