Models of granulocyte DNA structure are highly predictive of myelodysplastic syndrome

Donald C. Malins, Katie M. Anderson, Nayak L. Polissar, Gary K. Ostrander, Edward T. Knobbe, Virginia M. Green, Naomi K. Gilman, Jerry L. Spivak

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We have used statistical models based on Fourier transform-infrared spectra to differentiate between the DNA structure of normal granulocytes and those obtained from patients with myelodysplastic syndrome (MDS). The substantial degree of discrimination achieved between the two DNA groups is attributed to differences in the nucleotide base and backbone structures. These structural differences allowed for the development of a discriminant analysis model that predicted, with high sensitivity and specificity, which DNA came from normal granulocytes vs. granulocytes from MDS patients. The findings are a promising basis for developing a blood test to diagnose and predict the occurrence of MDS, for which there is currently a paucity of molecular markers.

Original languageEnglish (US)
Pages (from-to)5008-5011
Number of pages4
JournalProceedings of the National Academy of Sciences of the United States of America
Volume101
Issue number14
DOIs
StatePublished - Apr 6 2004

Keywords

  • Blood test
  • Clonal blood disease
  • Disease diagnosis and prediction
  • Fourier transform-infrared spectroscopy

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Models of granulocyte DNA structure are highly predictive of myelodysplastic syndrome'. Together they form a unique fingerprint.

Cite this