Pilot study of SELDI-TOF (surface enhanced laser desorption/ionization time of flight) in bone marrow failure syndromes
Authors: S. R. Kalmadi, R. Wu, M. Plasinova, E. Sloand, N. Galili, A. Raza, J. Maciejewski
Journal: Journal of Clinical Oncology (2005)

6547 Background: Clinically, bone marrow failure syndromes show significant overlap. Proteomic technology such as SELDI-TOF method enable screening of protein patterns without knowledge of the identity of targets. Methods: We included 43 Aplastic anemia, 166 Myelodysplastic syndrome (MDS), 34 Myeloproliferative disorder (MPD), 31 Paroxysmal Nocturnal Hemoglobinuria (PNH) patients and 66 control specimens. Our analysis included learning sets in which 20 randomly selected samples from individual categories were compared to normal in order to identify specific peptide peaks. We used the Protein Chip Reader from Ciphergen. H4 chips were selected for further study; spectra with >80 peaks were considered as informative. A scoring system was applied for analysis in which signal/noise ratio was a sole criterion for peak recognition and assignment of non-parametric scores. Reproducibility for each array run was confirmed by repeating two measurements of one standard plasma sample. CART analysis with decision trees and splitter peaks was used. Results: We constructed training sets comparing disease entities to normal controls and also to compare disease entities between themselves. We have also done some preliminary validation with blinded, training sets using non-overlapping patient samples. Conclusions: Our ongoing study demonstrates the feasibility of SELDI technology in detecting diagnostic protein “signatures” for individual bone marrow failure states. Validation sets with blinded, non-overlapping patient samples are being done to validate these results. This may allow for more precise definition and supplement the traditional diagnostic approaches. Samples with atypical spectra can be investigated for features that may prompt diagnostic reassignment; e.g., in patients with hypocellular MDS to AA, and PNH to AA/PNH syndrome. Identification of signature peaks could also aid in the discovery of therapeutic targets. Distinction of disease entities from healthy controls: Disease Sensitivity Specificity Selection criteria RA with normal cytogenetics (N=20) 65% 84% M/Z 1865Da (+) & 3096Da(-) 5q- (N=19) 79% 84% M/Z 2190Da(+) RAEB (N=21) 86% 63% M/Z 7343Da(+) & 3895Da(-) Trisomy 8 (N=16) 68% 69% M/Z 1933Da(+) & 3096Da(-) RARS (N=16) 100% 58% M/Z 2190Da(+) & 3895Da(-) AA (N=21) 76% 84% M/Z 2190Da(+) MPD (N=18) 72% 95% M/Z 6673Da(+) & 4463Da(-) PNH (N=21) 90% 68% M/Z 3337Da(+) & 3895Da(-) Distinction between individual disease entities: Diseases compared Sensitivity Specificity Selection criteria AA (N=21) v/s RA with normal cytogenetics (N=21) 62% 71% Selected as AA when M/Z 2898Da+ AA (N=21) v/s PNH (N=21) 67% 71% Selected as AA when M/Z 2866Da+ RA with normal cytogenetics (N=20) v/s RARS (N=16) 75% 75% Selected as RA with normal cytogenetics when M/Z 3924Da+ RA with normal cytogenetics (N=20) v/s 5q- 63% 80% Selected as RA with normal cytogenetics when M/Z 2807Da+ RA with normal cytogenetics (N=20) v/s trisomy 8 69% 65% Selected as RA with normal cytogenetics when M/Z 3190Da+ Validation of training set results with NC (N=19): Disease Training Sensitivity Validation Specificity Sensitivity Selection criteria Specificity AA (N=19) v/s NC 76% 84% 53% 74% M/Z 2190Da+ RAEB (N=17) v/s NC 86% 63% 94% 53% M/Z 7343Da+ & 3895Da- No significant financial relationships to disclose.