T-cell acute lymphoblastic leukemia (T-ALL) presents a difficult medical problem. T-ALL's clinical features and the biological properties of the leukemia cells are not predictive of prognosis, and thus have not been useful for risk-specific adjustments in therapeutic intensity. Microarray gene expression analyses of T-cell leukemic lymphoblasts have not only improved our understanding of the biological heterogeneity of this disease but have revealed clinically relevant molecular subtypes. Five different multistep molecular pathways have been identified that lead to T-ALL, involving activation of different T-ALL oncogenes: (1) HOX11, (2) HOX11L2, (3) TAL1 plus LMO1/2, (4) LYL1 plus LMO2, and (5) MLL-ENL. Gene expression studies indicate activation of a subset of these genes-HOX11, TAL1, LYL1, LMO1, and LMO2-in a much larger fraction of T-ALL cases than those harboring activating chromosomal translocations. In many such cases, the abnormal expression of one or more of these oncogenes is biallelic, implicating upstream regulatory mechanisms. Among these molecular subtypes, overexpression of the HOX11 orphan homeobox gene occurs in approximately 5% to 10% of childhood and 30% of adult T-ALL cases. Patients with HOX11-positive lymphoblasts have an excellent prognosis when treated with modern combination chemotherapy, while cases at high risk of early failure are included largely in the TAL1- and LYL1-positive groups. Supervised learning approaches applied to microarray data have identified a group of genes whose expression is able to distinguish high-risk cases. Further analyses of gene expression signatures of T-ALL lymphoblasts are especially needed for patients treated on modern combination chemotherapy trials to clearly distinguish the 10% to 15% of patients who fail induction or relapse in the first year of treatment. These high-risk patients would be ideal candidates for more intensive therapies in first remission, such as myeloablative regimens with stem cell rescue. Based on the rapid pace of research in T-ALL, made possible in large part through microarray technology, deep analysis of molecular pathways should lead to new and much more specific targeted therapies.