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Cited by 86Multiple Myeloma Research and TreatmentsChronic Lymphocytic Leukemia ResearchMultiple Sclerosis Research Studies

Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study

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Cowan, Annie, Ferrari, Federico, Freeman, Samuel S, Redd, Robert, El-Khoury, Habib, Perry, Jacqueline, Patel, Vidhi, Kaur, Priya +26 more·2023·The Lancet Haematology
DOI 10.1016/s2352-3026(22)00386-6PMC PMC9991855
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Abstract

BACKGROUND: Patients with precursors to multiple myeloma are dichotomised as having monoclonal gammopathy of undetermined significance or smouldering multiple myeloma on the basis of monoclonal protein concentrations or bone marrow plasma cell percentage. Current risk stratifications use laboratory measurements at diagnosis and do not incorporate time-varying biomarkers. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time-varying biomarkers to model risk of progression to multiple myeloma. METHODS: In this retrospective, multicohort study, we included patients who were 18 years or older with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma. We evaluated several modelling approaches for predicting disease progression to multiple myeloma using a training cohort (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022). We created the PANGEA models, which used data on biomarkers (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage) and haemoglobin trajectories from medical records to predict progression from precursor disease to multiple myeloma. The models were validated in two independent validation cohorts from National and Kapodistrian University of Athens (Athens, Greece; from Jan 26, 2020, to Feb 7, 2022; validation cohort 1), University College London (London, UK; from June 9, 2020, to April 10, 2022; validation cohort 1), and Registry of Monoclonal Gammopathies (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). We compared the PANGEA models (with bone marrow [BM] data and without bone marrow [no BM] data) to current criteria (International Myeloma Working Group [IMWG] monoclonal gammopathy of undetermined significance and 20/2/20 smouldering multiple myeloma risk criteria). FINDINGS: We included 6441 patients, 4931 (77%) with monoclonal gammopathy of undetermined significance and 1510 (23%) with smouldering multiple myeloma. 3430 (53%) of 6441 participants were female. The PANGEA model (BM) improved prediction of progression from smouldering multiple myeloma to multiple myeloma compared with the 20/2/20 model, with a C-statistic increase from 0·533 (0·480-0·709) to 0·756 (0·629-0·785) at patient visit 1 to the clinic, 0·613 (0·504-0·704) to 0·720 (0·592-0·775) at visit 2, and 0·637 (0·386-0·841) to 0·756 (0·547-0·830) at visit three in validation cohort 1. The PANGEA model (no BM) improved prediction of smouldering multiple myeloma progression to multiple myeloma compared with the 20/2/20 model with a C-statistic increase from 0·534 (0·501-0·672) to 0·692 (0·614-0·736) at visit 1, 0·573 (0·518-0·647) to 0·693 (0·605-0·734) at visit 2, and 0·560 (0·497-0·645) to 0·692 (0·570-0·708) at visit 3 in validation cohort 1. The PANGEA models improved prediction of monoclonal gammopathy of undetermined significance progression to multiple myeloma compared with the IMWG rolling model at visit 1 in validation cohort 2, with C-statistics increases from 0·640 (0·518-0·718) to 0·729 (0·643-0·941) for the PANGEA model (BM) and 0·670 (0·523-0·729) to 0·879 (0·586-0·938) for the PANGEA model (no BM). INTERPRETATION: Use of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies. FUNDING: SU2C Dream Team and Cancer Research UK.

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  • These third-group patients are well captured by integrating independent disease-burden-based prognostic scores (e.g., IMWG and PETHEMA).

    “Typically, these patients are well captured by integrating independent disease-burden-based prognostic scores (e.g., IMWG and PETHEMA).7,102,103”

    From Evolution of Multiple Myeloma from a Genomic Perspective (2026) · cites this paper as [7]

    The Discussion (page unavailable in our copy) says the 20/2/20 and PETHEMA criteria disagree about which patients are high risk, rather than showing that combining existing scores captures them well. The Results report that PANGEA classified 58% of patients with smouldering myeloma who later progressed as high risk when 20/2/20 had classified them as intermediate or low risk. The paper does not test an integrated IMWG–PETHEMA score. (LLM, conf=0.81)

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  • Multiple myeloma does not develop rapidly; symptoms and organ damage may take decades to manifest.

    “This tumor does not develop rapidly; instead, it may take decades for symptoms and organ damage to manifest.4-8”

    From Evolution of Multiple Myeloma from a Genomic Perspective (2026) · cites this paper as [7]

    The Introduction (page unavailable) says multiple myeloma is often preceded by asymptomatic precursor conditions and distinguishes them from symptomatic disease, including organ damage. The Results (page unavailable) report follow-up extending over decades for some patients, but do not establish that symptoms and organ damage themselves take decades to manifest. (LLM, conf=0.78)

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Research works printed but not linked: 22

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Authors
Cowan, A., Ferrari, F., Freeman, S. S., Redd, R., El-Khoury, H., Perry, J., Patel, V., Kaur, P., Barr, H., Lee, D. J., Lightbody, E., Downey, K., Argyelan, D., Theodorakakou, F., Fotiou, D., Liacos, C. I., Kanellias, N., Chavda, S. J., Ainley, L., Sandecká, V., Pospíšilová, L., Minarik, J., Jungova, A., Radocha, J., Spicka, I., Nadeem, O., Yong, K., Hájek, R., Kastritis, E., Marinac, C. R., Dimopoulos, M. A., Getz, G., Trippa, L., & Ghobrial, I. M.
Affiliations
  • Dana-Farber Cancer Institute (US)
  • Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
  • Broad Institute (US)
  • Massachusetts General Hospital (US)
  • National and Kapodistrian University of Athens (GR)
  • University College London (GB)
  • University Hospital Brno (CZ)
  • Charles University (CZ)
  • University Hospital Ostrava (CZ)
Corresponding author
Ghobrial, Irene M
ORCID
Venue
The Lancet Haematology
Publisher
Elsevier BV
Published
2023-03
Volume
10
Issue
3
Pages
e203-e212
ISSN
2352-3026 (ISSN-L)
DOI
10.1016/s2352-3026(22)00386-6
PMC
PMC9991855
OpenAlex
W4322422815
Cited by
86
References
4
Citations by year
2026: 23 · 2025: 27 · 2024: 25 · 2023: 10
FWCI
17.56
Type
journal article
Language
en
Update notices
erratum (2024-03-01)
Open access
Licence
Locations
  • The Lancet Haematology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
Funders
  • Dana-Farber Cancer Institute
  • Stand up to Cancer (SU2C-AACR-DT-28-18)
  • Cancer Research UK
  • NIH
  • V Foundation
  • Dr Miriam and Sheldon G Adelson Medical Research Foundation
Topics
  • Health Sciences › Medicine › Hematology › Multiple Myeloma Research and Treatments
  • Health Sciences › Medicine › Genetics › Chronic Lymphocytic Leukemia Research
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
Keywords
monoclonal gammopathy of undetermined significance, smoldering multiple myeloma, progression to multiple myeloma, risk stratification, free light chain ratio, bone marrow plasma cell percentage, creatinine, machine learning prediction
MeSH terms
Algorithms, Creatinine, Female, Humans, Male, Monoclonal Gammopathy of Undetermined Significance, Multiple Myeloma, Retrospective Studies

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