首页 | 本学科首页   官方微博 | 高级检索  
   检索      


A two-gene signature, SKI and SLAMF1, predicts time-to-treatment in previously untreated patients with chronic lymphocytic leukemia
Authors:Schweighofer Carmen D  Coombes Kevin R  Barron Lynn L  Diao Lixia  Newman Rachel J  Ferrajoli Alessandra  O'Brien Susan  Wierda William G  Luthra Rajyalakshmi  Medeiros L Jeffrey  Keating Michael J  Abruzzo Lynne V
Institution:Department of Hematopathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America.
Abstract: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.
Keywords:
本文献已被 PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号