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J Clin Oncol. 2009 Apr 1;27(10):1637-43. doi: 10.1200/JCO.2008.18.1701. Epub 2009 Feb 17.

Characteristics associated with important clinical end points in patients with chronic lymphocytic leukemia at initial treatment.

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  • 1Department of Leukemia and Biostatistics and Applied Mathematics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.



Response to front-line treatment and subsequent clinical course for patients with chronic lymphocytic leukemia (CLL) are heterogeneous. Identifying pretreatment patient characteristics or prognostic factors associated with clinical outcomes is important for counseling patients, conducting clinical research, and evaluating trial results.


We evaluated the pretreatment characteristics of 595 previously untreated patients who had National Cancer Institute Working Group indications to initiate front-line therapy for predictors of complete response (CR), time to treatment failure (TTF), and overall survival (OS). Multivariable models were developed for all three end points.


CR is an important treatment end point correlated with longer TTF and OS. In this retrospective analysis, front-line treatment regimen was a significant independent predictive factor for all three end points; chemoimmunotherapy was the superior treatment regimen. Considering front-line treatment regimen, other independent patient characteristics associated with CR included age and beta(2)-microglobulin (beta-2M). TTF was independently associated with age, beta-2M, percent lymphocytes in bone marrow, and treatment regimen. Improved OS was independently associated with younger age, lower beta-2M, and treatment regimen. Two weighted prognostic models or nomograms, one including and one excluding treatment regimen, were constructed using significant characteristics to predict 5- and 10-year survival probability and estimate median survival time.


Identifying pretreatment patient characteristics associated with CR, TTF, and OS establishes a baseline to compare and incorporate new prognostic factors. Treatment had an impact on the significance of these factors. Prognostic models may help patients and clinicians in decision making as well as facilitate clinical research through design and analyses of clinical trials.

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