Molecular Classification of Bone Marrow Failure Syndromes: Protein Signatures As Surrogate Biomarker for Accurate Diagnosis of Severe Aplastic Anemia, Hypoplastic Myelodysplastic Syndrome and Paroxysmal Nocturnal Hemoglobinuria Patients

Background/Purpose: Bone marrow failure syndrome is an example of disease entity where accurate diagnosis of Severe Aplastic Anemia (SAA), Paroxysmal Nocturnal Hemoglobinuria (PNH) and Hypoplastic Myelodysplastic Syndrome (MDS) is very challenging. The aim of this study was to identify panels of disease–specific /disease-associated proteins biomarkers to be used for more objective diagnosis and better prediction of disease prognosis of patients presenting with features of bone marrow failure syndromes.

Methodology:

Bone marrow plasma (MBP) and peripheral blood plasma (PBP) samples from 20 patients with bone marrow hypoplasia; including AA/MDS/PNH were subjected to expression proteome analysis using label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).

Results:  Approximately 300 unique protein species were identified of which 107 and 218 were significantly differentially expressed (> 2- ∞- fold change & p < 0.05) in BMP and PBP respectively. These protein fingerprints independently discriminates patients into three distinct clusters; AA/MDS/PNH. Furthermore, only approx. 25% of the proteins were common between the two datasets from BMP and PBP. Some of the identified proteins were filtered and mapped using Ingenuity Pathway Analysis, and were associated with five different networks. The top two of these networks involved cell-to-cell signaling interaction, hematological system development and function, and immune cell trafficking. Only three of the differentially expressed proteins were uniquely expressed in SAA and MDS but absent in PNH, thus making these proteins potential biomarkers.

The probable diagnostic utility of these proteins would be validated in large archival clinical samples.  Our data indicates the utility of multivariate analysis of quantitative proteome data as a means of discovery of disease related or disease specific biomarkers for bone marrow syndromes.

Conclusions:

We have identified protein signatures capable of objective classification of bone marrow failure syndromes patients. Our expression proteomics strategy is very promising for identification of clinically useful biomarkers. These proteins once validated, on a larger cohort of patients, might be valuable to complement the currently existing parameters for reliable and objective disease diagnosis, monitoring treatment response and clinical outcome of bone marrow failure syndrome patients.