Kastrinos Fay, Uno Hajime, Ukaegbu Chinedu, Alvero Carmelita, McFarland Ashley, Yurgelun Matthew B, Kulke Matthew H, Schrag Deborah, Meyerhardt Jeffrey A, Fuchs Charles S, Mayer Robert J, Ng Kimmie, Steyerberg Ewout W, Syngal Sapna
Fay Kastrinos and Ashley McFarland, Columbia University Medical Center, New York, NY; Hajime Uno, Chinedu Ukaegbu, Matthew B. Yurgelun, Matthew H. Kulke, Deborah Schrag, Jeffrey A. Meyerhardt, Charles S. Fuchs, Robert J. Mayer, Kimmie Ng, and Sapna Syngal, Dana-Farber Cancer Institute; Carmelita Alvero, Harvard T.H. Chan School of Public Health; Matthew B. Yurgelun, Matthew H. Kulke, Deborah Schrag, Jeffrey A. Meyerhardt, Charles S. Fuchs, Robert J. Mayer, Kimmie Ng, and Sapna Syngal, Harvard Medical School; Sapna Syngal, Brigham and Women's Hospital, Boston, MA; and Ewout W. Steyerberg, University Medical Center Rotterdam, Rotterdam, the Netherlands.
J Clin Oncol. 2017 Jul 1;35(19):2165-2172. doi: 10.1200/JCO.2016.69.6120. Epub 2017 May 10.
Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM, that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM model. External validation of PREMM was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM exceeded those of PREMM. Decision curve analysis supported germline LS testing for PREMM scores ≥ 2.5%. Conclusion PREMM provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM provides performance that is superior to the existing PREMM model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer.
目的 目前的林奇综合征（LS）预测模型可量化个体携带错配修复（MMR）三个基因（MLH1、MSH2和MSH6）致病种系突变的风险。我们开发了一种新的预测模型PREMM，该模型纳入了PMS2和EPCAM基因，以提供全面的LS风险评估。
患者与方法 通过对18734名对所有五个基因进行检测的个体的临床和种系数据进行多分类逻辑回归分析，开发PREMM以预测任何一个LS基因发生突变的可能性。突变状态的预测因素包括性别、基因检测时的年龄、先证者和家族癌症病史。通过受试者操作特征曲线（AUC）下的面积评估辨别能力，并通过决策曲线分析确定临床影响；与现有的PREMM模型进行比较。在一个基于诊所的1058例结直肠癌患者队列中对PREMM进行外部验证。
结果 在开发队列的18734例患者中，有1000例（5%）检测到致病突变；突变包括MLH1（n = 306）、MSH2（n = 354）、MSH6（n = 177）、PMS2（n = 141）和EPCAM（n = 22）。PREMM区分携带者与非携带者的AUC为0.81（95%CI，0.79至0.82），在验证队列中的表现相似（AUC，0.83；95%CI，0.75至0.92）。PMS2突变的预测比其他基因更困难（AUC，0.64；95%CI，0.60至0.68）。PREMM的性能特征超过了PREMM。决策曲线分析支持对PREMM评分≥2.5%的个体进行种系LS检测。
结论 PREMM提供了所有五个LS基因的全面风险估计，并支持对评分≥2.5%的个体进行LS基因检测。在此阈值下，PREMM在识别LS携带者方面的表现优于现有的PREMM模型，包括那些表型较弱和未受癌症影响的个体。
