Altrock Philipp M, Ferlic Jeremy, Galla Tobias, Tomasson Michael H, Michor Franziska
Philipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.
JCO Clin Cancer Inform. 2018 Dec;2:1-12. doi: 10.1200/CCI.17.00131.
Recent advances have uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework.
We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence, and baseline MM survival. We measured MM-specific mortality and MM prevalence after MGUS detection from simulations and mathematic modeling predictions.
Our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available point estimate of progression risk reduction to 61% risk, starting screening at age 55 years and performing follow-up screening every 6 years reduced total MM prevalence by 19%. The same reduction could be achieved with starting screening at age 65 years and performing follow-up screening every 2 years. A 40% progression risk reduction per patient with MGUS per year would reduce MM-specific mortality by 40%. Specifically, screening onset age and screening frequency can change disease prevalence, and progression risk reduction changes both prevalence and disease-specific mortality. Screening would generally be favorable in high-risk individuals.
Screening efforts should focus on specifically identified groups with high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk individuals with MGUS would require improved preventions.
最近的进展揭示了一些治疗性干预措施，这些措施可能会降低癌前诊断进展的风险，比如意义未明的单克隆丙种球蛋白病（MGUS）进展为多发性骨髓瘤（MM）。目前尚不清楚如何最好地筛查高危人群，以及如何在人群层面评估这些干预措施降低疾病患病率和死亡率的能力。为了解决这些问题，我们开发了一个计算建模框架。
我们对不同个体人群中的MGUS发病率和进展情况进行基于个体的计算建模，以确定在筛查开始时间、间隔和风险组特异性方面的最佳筛查策略。输入数据包括生命表、MGUS发病率和MM基线生存率。我们通过模拟和数学建模预测来测量MGUS检测后的MM特异性死亡率和MM患病率。
我们的框架适用于广泛的筛查和干预场景，包括基线MGUS到MM进展率的变化以及不断演变的MGUS（其中进展随时间增加）。鉴于目前可获得的进展风险降低至61%的点估计，55岁开始筛查并每6年进行一次后续筛查可使MM总患病率降低19%。65岁开始筛查并每2年进行一次后续筛查也可实现相同程度的降低。每位MGUS患者每年进展风险降低40%将使MM特异性死亡率降低40%。具体而言，筛查起始年龄和筛查频率可改变疾病患病率，而进展风险降低则会改变患病率和疾病特异性死亡率。一般来说，筛查对高危个体有利。
筛查工作应专注于明确识别出的MGUS终生风险高的特定群体，对这些群体而言，筛查益处可能显著。对低风险MGUS个体进行筛查将需要改进预防措施。
Zotero 插件
