Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Biostatistics and Research Decision Sciences, Merck & Co, Rahway, NJ, USA.
Lancet Haematol. 2023 Mar;10(3):e203-e212. doi: 10.1016/S2352-3026(22)00386-6.
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.
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).
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).
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.
SU2C Dream Team and Cancer Research UK.
多发性骨髓瘤前体患者根据单克隆蛋白浓度或骨髓浆细胞百分比分为意义未明的单克隆丙种球蛋白血症或冒烟型多发性骨髓瘤。目前的风险分层使用诊断时的实验室测量值，不包括时变生物标志物。我们的目标是开发一种意义未明的单克隆丙种球蛋白血症和冒烟型多发性骨髓瘤分层算法，该算法利用可及的、时变的生物标志物来预测进展为多发性骨髓瘤的风险。
在这项回顾性多队列研究中，我们纳入了年龄在 18 岁及以上的意义未明的单克隆丙种球蛋白血症或冒烟型多发性骨髓瘤患者。我们使用来自 Dana-Farber 癌症研究所（波士顿，MA，美国；注释日期为 2019 年 11 月 13 日至 2022 年 4 月 13 日）的训练队列评估了几种预测疾病进展为多发性骨髓瘤的建模方法。我们创建了 PANGEA 模型，该模型使用来自病历的生物标志物（单克隆蛋白浓度、游离轻链比、年龄、肌酐浓度和骨髓浆细胞百分比）和血红蛋白轨迹数据来预测前体疾病向多发性骨髓瘤的进展。该模型在两个独立的验证队列（来自雅典国立和 Kapodistrian 大学的队列 1（2020 年 1 月 26 日至 2022 年 2 月 7 日）、来自伦敦大学学院的队列 1（2020 年 6 月 9 日至 2022 年 4 月 10 日）和来自捷克共和国的骨髓瘤登记处的队列 2（2004 年 1 月 5 日至 2022 年 3 月 10 日）中进行了验证。我们将 PANGEA 模型（有骨髓[BM]数据和无骨髓[无 BM]数据）与当前标准（国际骨髓瘤工作组[IMWG]意义未明的单克隆丙种球蛋白血症和 20/2/20 冒烟型多发性骨髓瘤风险标准）进行了比较。
我们纳入了 6441 名患者，其中 4931 名（77%）为意义未明的单克隆丙种球蛋白血症，1510 名（23%）为冒烟型多发性骨髓瘤。6441 名参与者中 3430 名（53%）为女性。与 20/2/20 模型相比，PANGEA 模型（BM）改善了冒烟型多发性骨髓瘤向多发性骨髓瘤进展的预测，在就诊 1 时 C 统计量从 0.533（0.480-0.709）增加到 0.756（0.629-0.785），就诊 2 时从 0.613（0.504-0.704）增加到 0.720（0.592-0.775），就诊 3 时从 0.637（0.386-0.841）增加到 0.756（0.547-0.830），在验证队列 1 中。与 20/2/20 模型相比，PANGEA 模型（无 BM）改善了冒烟型多发性骨髓瘤向多发性骨髓瘤进展的预测，在就诊 1 时 C 统计量从 0.534（0.501-0.672）增加到 0.692（0.614-0.736），就诊 2 时从 0.573（0.518-0.647）增加到 0.693（0.605-0.734），就诊 3 时从 0.560（0.497-0.645）增加到 0.692（0.570-0.708），在验证队列 1 中。与 IMWG 滚动模型相比，PANGEA 模型在验证队列 2 中改善了意义未明的单克隆丙种球蛋白血症向多发性骨髓瘤进展的预测，就诊 1 时 C 统计量增加，PANGEA 模型（BM）从 0.640（0.518-0.718）增加到 0.729（0.643-0.941），PANGEA 模型（无 BM）从 0.670（0.523-0.729）增加到 0.879（0.586-0.938）。
在临床实践中使用 PANGEA 模型将使前体疾病患者能够更准确地衡量其进展为多发性骨髓瘤的风险，从而促使采取更适当的治疗策略。
SU2C 梦想团队和英国癌症研究协会。
