Prognosis at diagnosis: integrating molecular biologic insights into clinical practice for patients with CLL

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Abstract
Heterogeneity in the clinical behavior of patients with chronic lymphocytic leukemia (CLL) makes it difficult for physicians to accurately identify which patients may benefit from an early or more aggressive treatment strategy and to provide patients with relevant prognostic information. Given the potential efficacy of newer therapies and the desire to treat patients at “optimum” times, it is more important than ever to develop sensitive stratification parameters to identify patients with poor prognosis. The evolution of risk stratification models has advanced from clinical staging and use of basic laboratory parameters to include relevant biologic and genetic features. This article will review the dramatic progress in prognostication for CLL and will propose statistical modeling techniques to evaluate the utility of these new measures in predictive models to help determine the optimal combination of markers to improve prognostication for individual patients. This discussion will also elaborate which markers and tools should be used in current clinical practice and evaluated in ongoing clinical trials.

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