Proteomics-based prediction of clinical response in acute myeloid leukemia

Objective: Response to chemotherapy is achieved in 60% to 70% of patients with acute myeloid leukemia. The ability to predict responders may help in stratifying patients and exploring different therapeutic approaches for nonresponders. Proteomics methods were used to search for predictive factors or combinations of factors. Materials and Methods: Peripheral blood plasma samples from 41 patients with confirmed acute myeloid leukemia with intermediate or poor cytogenetics were obtained prior to induction therapy for proteomic analysis. For each plasma sample, four fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces and 12 surface-enhanced laser desorption/ionization spectra were generated. Peaks that correlated with response were identified, and decision trees incorporating these peaks along with various clinical and laboratory findings were constructed to predict response. Results: Multiple decision trees were constructed. One peak, when combined with age, provided strong positive prediction of responders with 83% accuracy. A second tree, which combined one peak with both cytogenetics and the percent of monocytes in peripheral blood, detected responders with 95% accuracy. A third peak was adequate to predict responders in the intermediate cytogenetic group with 86% accuracy. Conclusions: Proteomic analysis should be further explored to define factors important in predicting clinical response in patients with acute myeloid leukemia.