A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia.

Journal Article (Journal Article)

PURPOSE: Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by a variable clinical course. Several parameters have prognostic capabilities but are associated with altered response to therapy in only a small subset of patients. EXPERIMENTAL DESIGN: We used gene expression profiling methods to generate predictors of therapy response and prognosis. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemoimmunotherapy were created using cancer cell lines and patient leukemia cell samples. We validated and applied these three signatures to independent clinical data from four cohorts, representing a total of 301 CLL patients. RESULTS: A genomic signature of prognosis created from patient leukemic cell gene expression data coupled with clinical parameters significantly differentiated patients with stable disease from those with progressive disease in the training data set. The progression signature was validated in two independent data sets, showing a capacity to accurately identify patients at risk for progressive disease. In addition, genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were generated and could accurately distinguish responding and nonresponding CLL patients. CONCLUSIONS: Thus, microarray analysis of CLL lymphocytes can be used to refine prognosis and predict response to different therapies. These results have implications for standard and investigational therapeutics in CLL patients.

Full Text

Duke Authors

Cited Authors

  • Friedman, DR; Weinberg, JB; Barry, WT; Goodman, BK; Volkheimer, AD; Bond, KM; Chen, Y; Jiang, N; Moore, JO; Gockerman, JP; Diehl, LF; Decastro, CM; Potti, A; Nevins, JR

Published Date

  • November 15, 2009

Published In

Volume / Issue

  • 15 / 22

Start / End Page

  • 6947 - 6955

PubMed ID

  • 19861443

Pubmed Central ID

  • PMC2783430

Electronic International Standard Serial Number (EISSN)

  • 1557-3265

Digital Object Identifier (DOI)

  • 10.1158/1078-0432.CCR-09-1132


  • eng

Conference Location

  • United States