Comparative proteomic analysis of patients with acute myeloid leukemia before and after induction therapy

BACKGROUND

Acute myeloid leukemia (AML) is a malignant disorder of hematopoietic stem and progenitor cells, characterized by accumulation of immature blasts in the bone marrow and peripheral blood of affected patients. Response to chemotherapy treatment in patients with AML is wide-ranging, and to date there are no adequate molecular biomarkers used to predict clinical outcome.

OBJECTIVE

The aim of this study was to identify potential protein biomarkers which could help predict response to induction treatment in AML patients.

METHODS

Peripheral blood samples were obtained from 15 AML patients both before and after treatment. A comparative proteomic analysis was performed using 2D gel electrophoresis followed by Mass Spectrometry.

RESULTS

This comparative proteomic study, combined with a protein network analysis, revealed several proteins that could be considered potential biomarkers of poor prognosis in AML: GAPDH which favors increased glucose metabolism; eEF1A1 and Annexin A1 that promote proliferation and migration, cofilin 1 which plays a role in the activation of apoptosis; and GSTP1 which is involved in the processes of detoxification and chemoresistanceCONCLUSIONS: This study gives an insight into a panel of protein biomarkers with prognostic potential that should be further investigated.

Interaction network of proteins associated with unfavorable prognosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant disorder of hematopoietic stem and progenitor cells, characterized by accumulation of immature blasts in the bone marrow and peripheral blood of affected patients. Standard induction therapy leads to complete remission in approximately 50% to 75% of patients. In spite of favorable primary response rates, only 20% to 30% of patients enjoy long-term disease free survival. Identifying proteins involved in prognosis is important for proposing biomarkers that can aid in the clinical management of the disease. The aim of this study was to construct a protein-protein interaction (PPI) network based on serum proteins associated with unfavorable prognosis of AML, and analyze the biological pathways underlying molecular complexes in the network. We identified 16 candidate serum proteins associated with unfavorable prognosis (in terms of poor response to treatment, poor overall survival, short complete remission, and relapse) in AML via a search in the literature: IL2RA, FTL, HSP90AA1, D2HGDH, PLAU, COL18A1, FGF19, SPP1, FGA, PF4, NME1, TNF, ANGPT2, B2M, CD274, LGALS3. The PPI network was constructed with Cytoscape using association networks from String and BioGRID, and Gene Ontology enrichment analysis using the ClueGo pluggin was performed. The central protein in the network was found to be PTPN11 which is involved in modulating the RAS-ERK, PI3K-AKT and JAK-STAT pathways, as well as in hematopoiesis, and in the regulation of apoptotic genes. Therefore, a dysregulation of this protein and/or of the proteins connected to it in the network leads to the defective activation of these signaling pathways and to a reduction in apoptosis. Together, this could cause an increase in the frequency of leukemic cells and a resistance to apoptosis in response to treatment.

Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

BACKGROUND

Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images.

RESULTS

An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%.

CONCLUSIONS

The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.