Chronic lymphocytic leukemia (CLL), the most frequent form of adult leukemia in Western countries, is characterized by a highly variable clinical course. Expression profiling of a series of 160 CLL patients allowed interrogating the genes presumably playing a role in pathogenesis, relating the expression of functionally relevant signatures with the time to treatment. First, we identified genes relevant to the biology and prognosis of CLL to build a CLL disease-specific oligonucleotide microarray. Second, we hybridized a training series on the CLL-specific chip, generating a biology-based predictive model. Finally, this model was validated in a new CLL series. Clinical variability in CLL is related with the expression of two gene clusters, associated with B-cell receptor (BCR) signaling and mitogen-activated protein kinase (MAPK) activation, including nuclear factor-kappaB1 (NF-kappaB1). The expression of these clusters identifies three risk-score groups with treatment-free survival probabilities at 5 years of 83, 50 and 17%. This molecular predictor can be applied to early clinical stages of CLL. This signature is related to immunoglobulin variable region somatic hypermutation and surrogate markers. There is a molecular heterogeneity in CLL, dependent on the expression of genes defining BCR and MAPK/NF-kappaB clusters, which can be used to predict time to treatment in early clinical stages.