Evidence map›Paper›PMID 40316968›Full record

ArticleBMC medicine2025

Exploring secondary extramedullary myeloma disease: a five-predictor scoring system with spotlight on double-hit cytogenetics.

Yi Tao, Shi-Wei Jin, Zhe Wang, Mengmeng Pan, Wanyan Ouyang, Jie Xu, Yuanfang Liu, Yan Wang, Weiping Zhang, Jian Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Yi Tao *State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. smmu018@163.com.
Shi-Wei Jin *State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Zhe Wang *Department of Physiology, College of Basic Medical Sciences, Naval Medical University, Shanghai, China.
Mengmeng PanState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Wanyan OuyangState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Jie XuState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Yuanfang LiuState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Yan WangState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Weiping ZhangState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Jian LiState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Jian-Qing MiState Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Department of Hematology, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. jianqingmi@shsmu.edu.cn.

Funding

National Natural Science Foundation of China 82070227National Research Center for Translational Medicine NRCTM (SH)-2021-10
6 · The paper itself

Abstract

backgroundExtramedullary myeloma disease (EMD) can present at disease relapse (secondary EMD, sEMD) and confers an aggressive clinical course. Identifying predictive markers for sEMD is crucial for clinical management.

methodsOur study, spanning February 2013 to October 2022, identified sEMD in 77 (12.5%) of 618 newly diagnosed multiple myeloma patients. We categorized sEMD patients into bone-related extramedullary (EM-B) and extraosseous extramedullary (EM-E) relapse groups, as well as into early and late relapse groups based on the median interval from initial MM diagnosis, and assessed their overall survival (OS). We investigated independent predictors for the development of sEMD and focused on double-hit (DH) myeloma, one of the predictors of sEMD. Through the analysis of single-cell RNA from DH myeloma samples, we explored the potential mechanisms by which it may contribute to sEMD.

resultsMedian OS post-sEMD diagnosis was 11 months, with no significant OS difference between EM-B and EM-E relapse groups. A median interval of 22 months from initial MM diagnosis to sEMD relapse divided the 77 sEMD patients into early and late relapse groups, with early sEMD associated with significantly inferior OS post-sEMD (5.0 vs 27.0 months, p = 0.028). Driven by the prognostic difference of early vs late sEMD relapse, we used a time-to-event model and identified five independent predictors: double-hit (DH) cytogenetics, ≥ 3 osteolytic lesions, IgD subtype, and non-autologous stem-cell transplantation (ASCT) status, each scoring one point, alongside EM-E scoring two points. These predictors informed an additive score, stratifying patients into low (0-2 points) and high (3-5 points) risk categories for sEMD, showing a significant difference in 3-year sEMD rates (6.6% vs 52.8%, p < 0.001). Moreover, the single-cell RNA sequencing of newly diagnosed DH myeloma samples uncovered significant mitogen-activated protein kinase (MAPK) activation in DH cells and exhaustion in CD8 + memory and NK effector cells. Potential therapeutic targets such as EZH2 have emerged from this analysis.

conclusionsOur study introduces a five-predictor scoring system informed by the potential mechanisms underlying sEMD progression in DH myeloma, with the goal of delaying or possibly preventing sEMD.

Indexed as

Multiple MyelomaAdultAgedCytogenetic AnalysisFemaleHumansMaleMiddle AgedDouble-hit myelomaOverall survivalPredictorSecondary extramedullary myelomaSingle-cell RNA sequencing

Identifiers

PMID40316968
PMCPMC12046924

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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.