• Something wrong with this record ?

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

A. Cowan, F. Ferrari, SS. Freeman, R. Redd, H. El-Khoury, J. Perry, V. Patel, P. Kaur, H. Barr, DJ. Lee, E. Lightbody, K. Downey, D. Argyelan, F. Theodorakakou, D. Fotiou, CI. Liacos, N. Kanellias, SJ. Chavda, L. Ainley, V. Sandecká, L....

. 2023 ; 10 (3) : e203-e212. [pub] -

Language English Country England, Great Britain

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc23003915

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.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc23003915
003      
CZ-PrNML
005      
20230425140954.0
007      
ta
008      
230418s2023 enk f 000 0|eng||
009      
AR
024    7_
$a 10.1016/S2352-3026(22)00386-6 $2 doi
035    __
$a (PubMed)36858677
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a enk
100    1_
$a Cowan, Annie $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
245    10
$a Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study / $c A. Cowan, F. Ferrari, SS. Freeman, R. Redd, H. El-Khoury, J. Perry, V. Patel, P. Kaur, H. Barr, DJ. Lee, E. Lightbody, K. Downey, D. Argyelan, F. Theodorakakou, D. Fotiou, CI. Liacos, N. Kanellias, SJ. Chavda, L. Ainley, V. Sandecká, L. Pospíšilová, J. Minarik, A. Jungova, J. Radocha, I. Spicka, O. Nadeem, K. Yong, R. Hájek, E. Kastritis, CR. Marinac, MA. Dimopoulos, G. Get, L. Trippa, IM. Ghobrial
520    9_
$a 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.
650    _2
$a lidé $7 D006801
650    _2
$a ženské pohlaví $7 D005260
650    _2
$a mužské pohlaví $7 D008297
650    12
$a monoklonální gamapatie nejasného významu $7 D008998
650    12
$a mnohočetný myelom $7 D009101
650    _2
$a retrospektivní studie $7 D012189
650    _2
$a algoritmy $7 D000465
650    _2
$a kreatinin $7 D003404
655    _2
$a časopisecké články $7 D016428
700    1_
$a Ferrari, Federico $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Biostatistics and Research Decision Sciences, Merck & Co, Rahway, NJ, USA
700    1_
$a Freeman, Samuel S $u Bioinformatics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
700    1_
$a Redd, Robert $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a El-Khoury, Habib $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Perry, Jacqueline $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Patel, Vidhi $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Kaur, Priya $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Barr, Hadley $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Lee, David J $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
700    1_
$a Lightbody, Elizabeth $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Downey, Katelyn $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Argyelan, David $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Theodorakakou, Foteini $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Fotiou, Despina $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Liacos, Christine Ivy $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Kanellias, Nikolaos $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Chavda, Selina J $u UCL Cancer Institute, University College London, London, UK
700    1_
$a Ainley, Louise $u UCL Cancer Institute, University College London, London, UK
700    1_
$a Sandecká, Viera $u Department of Internal Medicine, Hematology and Oncology, University Hospital Brno, Brno, Czech Republic
700    1_
$a Pospíšilová, Lenka $u Institute of Biostatistics and Analyses, Brno, Czech Republic
700    1_
$a Minarik, Jiri $u Department of Hemato-Oncology, University Hospital Olomouc, Olomouc, Czech Republic
700    1_
$a Jungova, Alexandra $u Department of Hematology and Oncology, University Hospital Pilsen, Pilsen, Czech Republic
700    1_
$a Radocha, Jakub $u Fourth Department of Internal Medicine Hematology, Faculty of Medicine in Hradec Kralove, University Hospital Hradec Kralove, Charles University, Czech Republic
700    1_
$a Spicka, Ivan $u First Department of Medicine, Department of Hematology, First Faculty of Medicine, Charles University and General Hospital in Prague, Czech Republic
700    1_
$a Nadeem, Omar $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Yong, Kwee $u UCL Cancer Institute, University College London, London, UK
700    1_
$a Hájek, Roman $u Fourth Department of Internal Medicine-Hematology, University Hospital in Ostrava, Ostrava, Czech Republic
700    1_
$a Kastritis, Efstathios $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Marinac, Catherine R $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Dimopoulos, Meletios A $u Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
700    1_
$a Get, Gad $u Bioinformatics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
700    1_
$a Trippa, Lorenzo $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
700    1_
$a Ghobrial, Irene M $u Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: irene_ghobrial@dfci.harvard.edu
773    0_
$w MED00193479 $t The Lancet. Haematology $x 2352-3026 $g Roč. 10, č. 3 (2023), s. e203-e212
856    41
$u https://pubmed.ncbi.nlm.nih.gov/36858677 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20230418 $b ABA008
991    __
$a 20230425140950 $b ABA008
999    __
$a ok $b bmc $g 1924524 $s 1190124
BAS    __
$a 3
BAS    __
$a PreBMC-MEDLINE
BMC    __
$a 2023 $b 10 $c 3 $d e203-e212 $e - $i 2352-3026 $m The Lancet. Haematology $n Lancet Haematol $x MED00193479
LZP    __
$a Pubmed-20230418

Find record

Citation metrics

Archiving options