The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history.
The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history.
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DOI：
10.1053/j.gastro.2010.08.021
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发表时间：
2011-01
期刊：
影响因子：
29.4
通讯作者：
Syngal S
中科院分区：
文献类型：
--
作者：
Kastrinos F;Steyerberg EW;Mercado R;Balmaña J;Holter S;Gallinger S;Siegmund KD;Church JM;Jenkins MA;Lindor NM;Thibodeau SN;Burbidge LA;Wenstrup RJ;Syngal S
我们开发并验证了一个模型，以估计错配修复（MMR）基因MLH1、MSH2和MSH6的突变风险，该模型基于个人和家族史的癌症。分析了4539个MLH1、MSH2和MSH6突变先证者的数据。建立多变量多分逻辑回归模型（premm1,2,6）预测MMR基因突变的总体风险和3个基因中每个基因突变的风险。该模型的判别能力在1827例基于人群的CRC病例中得到验证。12%的原始队列携带致病性突变（MLH1为204，MSH2为250，MSH6为71）。premm1,2,6模型纳入了先证者和一级和二级亲属的以下因素（优势比；95%可信区间[CI]）：男性（1.9;1.5-2.4），CRC(4.3; 3.3 - 5.6)，多发性CRC(13.7; 8.5-22)，子宫内膜癌（6.1;4.6-8.2）和结肠外癌（3.3;2.4-4.6）。MLH1突变携带者的工作特征曲线下面积为0.86 (95% CI: 0.82 ~ 0.91)， MSH2突变携带者为0.87 (95% CI: 0.83 ~ 0.92)， MSH6突变携带者为0.81 (95% CI: 0.69 ~ 0.93)；在验证中，整个队列（95% CI: 0.86-0.90）和基于人群的病例（95% CI: 0.83-0.92）的死亡率为0.88。我们开发了premm1,2,6模型，该模型结合了先证者及其亲属的癌症病史信息，以估计个体MMR基因MLH1， MSH2和MSH6突变的风险。这种基于网络的决策工具可用于评估遗传性结直肠癌的风险并指导临床管理。
We developed and validated a model to estimate the risks for mutations in the mismatch repair (MMR) genes MLH1, MSH2, and MSH6 based on personal and family history of cancer. Data were analyzed from 4539 probands tested for mutations in MLH1, MSH2, and MSH6. A multivariable polytomous logistic regression model (PREMM1,2,6) was developed to predict the overall risk of MMR gene mutations and the risk of mutation in each of the 3 genes. The model’s discriminative ability was validated in 1827 population-based CRC cases. Twelve percent of the original cohort carried pathogenic mutations (204 in MLH1, 250 in MSH2, and 71 in MSH6). The PREMM1,2,6 model incorporated the following factors from the probands and first- and second-degree relatives (odds ratio; 95% confidence intervals [CI]): male sex (1.9; 1.5–2.4), a CRC (4.3; 3.3–5.6), multiple CRCs (13.7; 8.5–22), endometrial cancer (6.1; 4.6–8.2), and extracolonic cancers (3.3; 2.4–4.6). The areas under the receiver operating characteristic curves were 0.86 (95% CI: 0.82–0.91) for MLH1 mutation carriers, 0.87 (95% CI: 0.83–0.92) for MSH2, and 0.81 (95% CI: 0.69– 0.93) for MSH6; in validation, they were 0.88 for the overall cohort (95% CI: 0.86–0.90) and the population-based cases (95% CI: 0.83–0.92). We developed the PREMM1,2,6 model that incorporates information on cancer history from probands and their relatives to estimate an individual’s risk for mutations in the MMR genes MLH1, MSH2, and MSH6. This web-based decision making tool can be used to assess risk for hereditary CRC and guide clinical management.
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影响因子：
2
作者：
Steyerberg, Ewout W.;Balmana, Judith;Syngal, Sapna
通讯作者：
Syngal, Sapna
影响因子：
6.4
作者：
Monzon, Jose G.;Cremin, Carol;Gill, Sharlene
通讯作者：
Gill, Sharlene
影响因子：
--
作者：
McGeechan, Kevin;Macaskill, Petra;Wong, Tien Y.
通讯作者：
Wong, Tien Y.
影响因子：
120.7
作者：
Balmana, Judith;Stockwell, David H.;Syngal, Sapna
通讯作者：
Syngal, Sapna
影响因子：
45.3
作者：
Goecke, Timm;Schulmann, Karsten;Moeslein, Gabriela
通讯作者：
Moeslein, Gabriela