A two-gene signature, SKI and SLAMF1, predicts time-to-treatment in previously untreated patients with chronic lymphocytic leukemia.
A two-gene signature, SKI and SLAMF1, predicts time-to-treatment in previously untreated patients with chronic lymphocytic leukemia.
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DOI：
10.1371/journal.pone.0028277
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Abruzzo LV
中科院分区：
文献类型：
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作者：
Schweighofer CD;Coombes KR;Barron LL;Diao L;Newman RJ;Ferrajoli A;O'Brien S;Wierda WG;Luthra R;Medeiros LJ;Keating MJ;Abruzzo LV
我们开发并验证了一种双基因标记，可以预测以前未经治疗的慢性淋巴细胞白血病（CLL）患者的预后。使用来自131名患者队列的65个样本训练集，我们确定了预测治疗时间（TTT）和总生存期（OS）的最佳临床模型。为了确定训练集中表达与预后相关的单个基因或组合，我们交叉验证了单变量和多变量模型来预测TTT。我们确定了四个基因组（5、6、12或13个基因）来构建多变量预后模型。通过优化训练集上的每个基因集，我们构建了11个模型来预测从诊断到治疗的时间。每个模型还预测了OS，并为最佳临床模型增加了价值。为了确定当添加到临床变量时，哪个贡献了最大的价值，我们应用了赤池信息标准。两个基因在具有临床变量的模型中一致保留：SKI （v-SKI禽肉瘤病毒癌基因同源物）和SLAMF1（信号淋巴细胞激活分子家族成员1；CD150）。我们对双基因模型进行了优化，并在66个样本的独立测试集上进行了验证。该双基因模型在测试集上预测预后优于任何已知的预测因子，包括ZAP70和血清β2-微球蛋白。
We developed and validated a two-gene signature that predicts prognosis in previously-untreated chronic lymphocytic leukemia (CLL) patients. Using a 65 sample training set, from a cohort of 131 patients, we identified the best clinical models to predict time-to-treatment (TTT) and overall survival (OS). To identify individual genes or combinations in the training set with expression related to prognosis, we cross-validated univariate and multivariate models to predict TTT. We identified four gene sets (5, 6, 12, or 13 genes) to construct multivariate prognostic models. By optimizing each gene set on the training set, we constructed 11 models to predict the time from diagnosis to treatment. Each model also predicted OS and added value to the best clinical models. To determine which contributed the most value when added to clinical variables, we applied the Akaike Information Criterion. Two genes were consistently retained in the models with clinical variables: SKI (v-SKI avian sarcoma viral oncogene homolog) and SLAMF1 (signaling lymphocytic activation molecule family member 1; CD150). We optimized a two-gene model and validated it on an independent test set of 66 samples. This two-gene model predicted prognosis better on the test set than any of the known predictors, including ZAP70 and serum β2-microglobulin.
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影响因子：
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