Abstract
Background:
Tyrosine kinase inhibitor (TKI)-based therapy is a recommended treatment for patients with chronic myeloid leukemia (CML). However, a considerable group of CML patients do not respond well to the TKI therapy. Challenging to overcome this problem, we tried to discover molecular signatures in gene expression profiles to discriminate the responders and non-responders of TKI therapy.
Methods:
We collected three microarray datasets of CML patients having total 73 responders and 38 non-responders. Statistical analysis was performed to identify differentially expressed genes (DEGs) as gene signature candidates from integrated microarray datasets. The classification performance of these genes and further selected discriminator gene sets was tested by using random forest and iterative backward variable selection methods.
Results:
We identified a set of genes including CTBP2, NADK, AZU1, CTSH, FSTL1, and HDLBP showing the highest accuracy more than 69.44 % to classify TKI response in CML patients. Interestingly, four genes of them are on the signaling pathway of cell proliferation. This set of genes showed much higher performance than the average performance of other genes in downstream signaling of TKI target, BCR-ABL.
Conclusions:
In this study, we could find a set of potential companion diagnostic markers for TKI treatment and, at the same time, the potential of gene expression analysis to enhance the coverage of companion diagnostics.
Keywords:
Chronic myeloid leukemia (CML); Gene expression signature; Meta-analysis; Random forest; Tyrosine kinase inhibitor (TKI).