We have used Reverse Phase Protein Arrays (RPPA) to perform proteomic profiling in Acute Myelogenous Leukemia (AML) focusing on cell cycle, apoptosis and signal transduction pathway proteins (ASH 2006, abstract #107). Protein expression signatures were derived from this dataset of 436 AML patients, analyzed for 30 total and 22 phopho- proteins. The predictive ability of these RPPA derived protein expression signatures has not been prospectively tested to determine if they are valid. This dataset presented an opportunity to validate this as there was a population of patients with known FLT3-ITD and D835 mutation status (n=297) and another population where the status was unknown (n=139), among which 55 had sufficient sample available for mutation analysis. Prior to performing the mutation analysis a predictive model was built using linear regression with part of the data utilized for training and the reminder for validation. The model was designed to predict for the presence of mutation, either ITD or D835, although there are differences int eh signature of each. The total population had 85 cases with FLT3-ITD and 15 with the D835 mutation. The optimal model that was developed, using 30%, 50% and 70% of the samples for training and the remainder for validation, had a median validation accuracy of 68%, 70% and 72% respectively. Prospective predictions of FLT3-ITD or D835 mutation status were then made for all samples lacking FLT3-ITD or D835 mutation data. Mutation analysis was then performed using PCR amplification followed by 2-D gel electrophoresis (FLT3-ITD) to evaluate for PCR product size, or sequencing (D835) on 55 samples. This revealed 9 cases with FLT3-ITD, 3 with a D835 mutation, 1 with both and 43 without mutation. Among these 55 cases the model correctly predicted that 8 of the 12 mutant cases would be mutant including 8 of 10 with a FLT3-ITD, but 0 of 2 with only the D835 mutation. Among the 43 wildtype cases 36 were accurately predicted to be wildtype, while 7 were incorrectly predicted to have the mutation mutant. This yields an overall accuracy (OA) of 80%, Sensitivity =66%, Specificity=90%, Positive Predictive Value (PPV) of 53%, False positive rate of (FPR) of 16%. Since most patients with FLT3-ITD have Diploid cytogenetics we also looked at the predictive accuracy of the protein expression signature in that population. Among 23 patients with Diploid cytogenetics the overall accuracy was OA) of 83%, Sensitivity =75%, Specificity=87%, Positive Predictive Value (PPV) of 75%, False positive rate of (FPR) of 13%. Since FLT3-ITD and D835 carry different prognostic impact, and had different protein expression signatures, greater accuracy might be achieved if separate models were developed for each mutation individually. The model demonstrated that RPPA derived protein expression signatures can accurately be used to predict mutation status providing the first prospective validation of protein expression signatures in AML.
Disclosure: No relevant conflicts of interest to declare.