Evidence map›Paper›PMID 40485257›Full record

ArticleCancer biomarkers : section A of Disease markers2025

Polyclonal plasma cell (PolyPC) signature as a key indicator for predicting the progression of MGUS to multiple myeloma.

Fumou Sun, Yan Cheng, Catherine Ma, Hongwei Xu, Clyde Bailey, David Mery, Timothy Cody Ashby, Daisy Alapat, Yong Li, Ken H Young and 8 more

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Fumou SunDivision of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID 0000-0003-2034-3492
Yan ChengMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0001-7877-9622
Catherine MaMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Hongwei XuMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Clyde BaileyMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0009-0008-3694-0797
David MeryMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0002-8327-9777
Timothy Cody AshbyDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0002-9361-0283
Daisy AlapatDepartment of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0002-3414-8375
Yong LiDepartment of Medicine, Baylor College of Medicine, Houston, TX, USA.ORCID 0000-0001-8838-1714
Ken H YoungDepartment of Pathology, Duke University Medical Center and Duke Cancer Institute, Durham, NC, USA.
Samer Al HadidiMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0003-4297-8042
Sharmilan ThanendrarajanMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Carolina SchinkeMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0002-2699-1741
Maurizio ZangariMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0002-1720-3854
Frits van RheeMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Guido TricotMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
John D ShaughnessyMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Fenghuang ZhanMyeloma Center, Department of Internal Medicine, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Funding

Towards a better understanding of the critical role and therapeutic potential of drug resistant genes in multiple myelomaR01CA236814 · NCI · UNIV OF ARKANSAS FOR MED SCIS · PI FENGHUANG ZHAN · 2020 to 2026
$2.5M
BLRD VA I01 BX006235NCI NIH HHS R01 CA236814
6 · The paper itself

Abstract

BackgroundMultiple myeloma (MM) is virtually always preceded by monoclonal gammopathy of undetermined significance (MGUS). Elevated serum markers are used to classify MGUS patients into clinical risk categories. Previous research has indicated that the absence of a normal plasma cell signature in MGUS is linked to early progression.ObjectiveTo confirm that the presence of a "polyclonal plasma cell (PolyPC) signature" serves as a robust negative predictor of MGUS progression.Methods374 MGUS patients were enrolled, including 334 patients with stable disease and 40 patients who progressed to MM within 10 years. An oligonucleotide microarray analysis was performed on mRNA extracted from CD138-selected bone marrow plasma cells to evaluate gene expression profiles. The PolyPC signature was developed and validated to assess its role in predicting disease progression. Statistical analyses included Cox proportional hazards models to evaluate progression risk and receiver operating characteristic (ROC) curve analysis to determine the sensitivity, specificity, and overall predictive performance of the PolyPC score.ResultsThrough this retrospective study, we developed PolyPC signature based on gene expression profiles of normal, uninvolved plasma cells to predict MGUS progression risk. ROC analysis demonstrated that this signature accurately predicted the risk of MGUS progression (C-statistic: 0.792). A PolyPC score ≤ 11.6 identified a subset of 89 patients with a 10-year progression probability of 31.5% (28/89), while the remaining 285 patients had a progression probability of only 4.2% (12/285) (

Indexed as

Biomarkers, TumorMonoclonal Gammopathy of Undetermined SignificanceMultiple MyelomaPlasma CellsAdultAgedAged, 80 and overDisease ProgressionFemaleGene Expression ProfilingHumansMaleMiddle AgedPrognosisRetrospective StudiesROC CurveBiomarkers, Tumorgene expression profilingmonoclonal gammopathy of undetermined significance (MGUS)multiple myelomapolyclonal plasma cell (PolyPC)progression prediction

Identifiers

PMID40485257
PMCPMC12288413

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.