Functional proteomic profiling of AML predicts response and survival

Abstract

Because protein function regulates the phenotypic characteristics of cancer, a functional proteomic classification system could provide important information for pathogenesis and prognosis. With the goal of ultimately developing a proteomicbased classification of acute myeloid leukemia (AML), we assayed leukemiaenriched cells from 256 newly diagnosed AML patients, for 51 total and phosphoproteins from apoptosis, cell-cycle, and signal-transduction pathways, using reverse-phase protein arrays. Expression in matched blood and marrow samples were similar for 44 proteins; another 7 had small fold changes (8%-55%), suggesting that functional proteomics of leukemiaenriched cells in the marrow and periphery are similar. Protein expression patterns were independent of clinical characteristics. However, 24 proteins were significantly different between FrenchAmerican-British subtypes, defining distinct signatures for each. Expression signatures for AML with cytogenetic abnormalities involving ؊5 or ؊7 were similar suggesting mechanistic commonalities. Distinct expression patterns for FMS-like tyrosine kinase 3-internal tandem duplication were also identified. Principal component analysis defined 7 protein signature groups, with prognostic information distinct from cytogenetics that correlated with remission attainment, relapse, and overall survival. In conclusion, protein expression profiling patterns in AML correlate with known morphologic features, cytogenetics, and outcome. Confirmation in independent studies may also provide pathophysiologic insights facilitating triage of patients to emerging targeted therapies.

Expression of S100A8 in leukemic cells predicts poor survival in de novo AML patients

“…Cytological FAB subgroups or certain cytogenetic subgroups displayed specific variations of some proteins, and the predictive values of protein profiles have been underscored in acute lymphoblastic leukemia. In AML, the prognostic interest of proteomic approaches was notably demonstrated in a study by Kornblau et al 17 The authors investigated 51 preselected proteins, including 21 phospho-proteins important for apoptosis, cell cycle and signal transduction, using a reverse phase protein array, and showed that seven protein signature groups correlated with relapse and overall survival. Our study provides additional proof of the predictive value of proteomics using a different strategy based on the global evaluation of the protein signature of leukemic cells.…”

“…16 Finally, functional proteomic profiling confirmed that specific protein expression patterns correlate with outcome and prognosis, thus providing new molecular classifications useful for predicting the risk of AML patients at diagnosis. 17 In this context, we developed a surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-based protein profile of leukemic cells associated with tandem MS (MALDI-TOF/TOF and nanoLC MS/MS) to identify new, specific biomarkers of high-risk AML that are present at the time of diagnosis.…”

Reverse‐phase protein arrays for application‐orientated cancer research

“…Furthermore, mutated p53, high-level expression of the BCL2-related protein MCL1, and of Neurophilin-1 were identified predicting an adverse outcome for AML patients and a poor clinical prognosis. These data suggest that patients could potentially benefit from combined therapies aiming at inhibiting the anti- apoptotic effect of MCL1 by restoring p53 function via inhibiting MDM2, and this way interfering with signaling through VEGF [44]. The prognostic potential of RPPA was also demonstrated by Petricoin et al in 2007, who first associated the activation of the AKT/mTOR pathway with the therapeutic modulation of pathway activity in childhood rhabdomyosarcoma [26].…”

Stratified Gaussian graphical models

“…The protein data is taken from Kornblau et al (2009) and contains 256 observations on 51 variables. The optimal SG is displayed using the adjacency matrix in Figure 12, the circles represent stratified edges, the triangles edges to which strata could be added while still retaining a decomposable SG, and the squares edges that cannot be stratified in a decomposable SG.…”