Department of Hematopathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America.
PLoS One. 2011;6(12):e28277. doi: 10.1371/journal.pone.0028277. Epub 2011 Dec 14.
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.
我们开发并验证了一种两基因标志物，可预测未经治疗的慢性淋巴细胞白血病（CLL）患者的预后。我们使用了来自 131 名患者队列的 65 个样本训练集，确定了预测治疗时间（TTT）和总生存期（OS）的最佳临床模型。为了确定与预后相关的表达的训练集中的单个基因或基因组合，我们对单变量和多变量模型进行了交叉验证，以预测 TTT。我们确定了四个基因集（5、6、12 或 13 个基因）来构建多变量预后模型。通过在训练集上优化每个基因集，我们构建了 11 个预测从诊断到治疗时间的模型。每个模型还预测了 OS，并为最佳临床模型增加了价值。为了确定添加到临床变量时哪个贡献最大，我们应用了赤池信息量准则。两个基因在包含临床变量的模型中始终保留：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）。我们优化了一个两基因模型，并在 66 个样本的独立测试集中对其进行了验证。与已知的预测因子（包括 ZAP70 和血清β2-微球蛋白）相比，该两基因模型在测试集中对预后的预测效果更好。
