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A Two-Gene Signature, SKI and SLAMF1, Predicts Time-to-Treatment in Previously Untreated Patients with Chronic Lymphocytic Leukemia

Author

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  • Carmen D Schweighofer
  • Kevin R Coombes
  • Lynn L Barron
  • Lixia Diao
  • Rachel J Newman
  • Alessandra Ferrajoli
  • Susan O'Brien
  • William G Wierda
  • Rajyalakshmi Luthra
  • L Jeffrey Medeiros
  • Michael J Keating
  • Lynne V Abruzzo

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.

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  • Carmen D Schweighofer & Kevin R Coombes & Lynn L Barron & Lixia Diao & Rachel J Newman & Alessandra Ferrajoli & Susan O'Brien & William G Wierda & Rajyalakshmi Luthra & L Jeffrey Medeiros & Michael J , 2011. "A Two-Gene Signature, SKI and SLAMF1, Predicts Time-to-Treatment in Previously Untreated Patients with Chronic Lymphocytic Leukemia," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0028277
    DOI: 10.1371/journal.pone.0028277
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    References listed on IDEAS

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    1. Keith Baggerly, 2010. "Disclose all data in publications," Nature, Nature, vol. 467(7314), pages 401-401, September.
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    Cited by:

    1. Edouard Cornet & Agathe Debliquis & Valérie Rimelen & Natacha Civic & Mylène Docquier & Xavier Troussard & Bernard Drénou & Thomas Matthes, 2015. "Developing Molecular Signatures for Chronic Lymphocytic Leukemia," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-19, June.

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