Display Settings:


Send to:

Choose Destination
Bioinformatics. 2012 Mar 15;28(6):823-30. doi: 10.1093/bioinformatics/bts059. Epub 2012 Jan 31.

Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data.

Author information

  • 1Department of Microbiology, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu



Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome.


We developed a method that integrates gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging. Applying our integrated method to CML, we identified small sets of signature genes that are highly predictive of disease phases and that are more robust and stable than using expression data alone. The accuracy of our algorithm was evaluated using cross-validation on the gene expression data. We then tested the hypothesis that gene sets associated with advanced phase CML would predict relapse after allogeneic transplantation in 176 independent CP CML cases. Our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes.

[PubMed - indexed for MEDLINE]
Free PMC Article

Images from this publication.See all images (4)Free text

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk