ArticlePloS one2011
A two-gene signature, SKI and SLAMF1, predicts time-to-treatment in previously untreated patients with chronic lymphocytic leukemia.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis.The Cochrane database of systematic reviews · 2020Pooled it
- MicroRNA-195-5p inhibits the progression of hemangioma via targeting SKI.Experimental and therapeutic medicine · 2022Article
- Identification and Validation of Immune Molecular Subtypes and Immune Landscape Based on Colon Cancer Cohort.Frontiers in medicine · 2022Article
- Super-Enhancer-Associated nine-gene prognostic score model for prediction of survival in chronic lymphocytic leukemia patients.Frontiers in genetics · 2022Article
- Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis.Cancer cell international · 2021Article
- SLAMF6 in health and disease: Implications for therapeutic targeting.Clinical immunology (Orlando, Fla.) · 2019Review
- Review
- Article
- A combination of an anti-SLAMF6 antibody and ibrutinib efficiently abrogates expansion of chronic lymphocytic leukemia cells.Oncotarget · 2016Article
- SLAMF1 regulation of chemotaxis and autophagy determines CLL patient response.The Journal of clinical investigation · 2016Article
- Article
- Personalized medicine in CLL: current status and future perspectives.Cancer letters · 2014Review
- Transcriptional cofactors Ski and SnoN are major regulators of the TGF-β/Smad signaling pathway in health and disease.Signal transduction and targeted therapyArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
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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