Dataset Information

Association of Immune Marker Changes With Progression of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma.

ABSTRACT:

Importance

Multiple myeloma is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS). Risk models that estimate the risk of progression from MGUS to multiple myeloma use data from a single time point, usually the initial workup.

Objective

To longitudinally investigate the alterations of serum immune markers with stable vs progressive MGUS.

Design, setting, and participants

This prospective cross-sectional cohort study included 77 469 adult participants aged 55 to 74 years in the screening arm of the National Cancer Institute Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial who had a diagnosis of progressing MGUS (n = 187) or stable MGUS (n = 498), including light-chain subtype, from November 1993, through December 2011. For each participant, all available serially stored prediagnostic serum samples (N = 3266) were obtained. Data analysis was performed from April 2018, to December 2018.

Main outcomes and measures

Serum protein and monoclonal immunoglobulin levels, serum free light chains, and serum light chains within each immunoglobulin class were measured.

Results

Of 685 individuals included in the study, 461 (67.3%) were men; the mean (SD) age was 69.1 (5.6) years. In cross-sectional modeling, risk factors associated with progressive MGUS were IgA isotype (adjusted odds ratio [OR], 1.80; 95% CI, 1.03-3.13; P = .04), 15 g/L or more monoclonal spike (adjusted OR, 23.5; 95% CI, 8.9-61.9; P < .001), skewed (<0.1 or >10) serum free light chains ratio (adjusted OR, 46.4; 95% CI, 18.4-117.0; P < .001), and severe immunoparesis (≥2 suppressed uninvolved immunoglobulins) (adjusted OR, 19.1; 95% Cl, 7.5-48.3; P < .001). Risk factors associated with progressive light-chain MGUS were skewed serum free light chains ratio (adjusted OR, 44.0; 95% CI, 14.2-136.3; P < .001) and severe immunoparesis (adjusted OR, 48.6; 95% CI, 9.5-248.2; P < .001). In longitudinal analysis of participants with serial samples prior to progression, 23 of 43 participants (53%) had high-risk MGUS before progression; 16 of these 23 (70%) experienced conversion from low-risk or intermediate-risk MGUS within 5 years. Similar results were found for light-chain MGUS.

Conclusions and relevance

The findings of evolving risk patterns support annual blood testing and risk assessment for patients with MGUS or light-chain MGUS.

SUBMITTER: Landgren O

PROVIDER: S-EPMC6646992 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPatients 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.MethodsIn 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).FindingsWe 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).InterpretationUse 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.FundingSU2C Dream Team and Cancer Research UK.