Prediction of treatment response using gene expression profiles.
This paper concerns prediction of clinical outcome from gene expression profiles using work in a different area, nonlinear system identification. In particular, the approach can predict long-term treatment response from data of a landmark article by Golub et al. (Golub, T. R.; Slonim, D. K.; Tamayo, P.; Huard, C.; Gaasenbeek, M.; Mesirov, J. P. et al. Science 1999, 286, 531-537) that has not previously been achieved with these data. The present paper shows that, for these data, gene expression profiles taken at time of diagnosis of acute myeloid leukemia contain information predictive of eventual response to chemotherapy. This was not evident in previous work; indeed, the Golub et al. article did not find a set of genes strongly correlated with clinical outcome. However, the present approach can accurately predict outcome class of gene expression profiles even when the genes do not have large differences in expression levels between the classes.
Korenberg
Michael J
MJ
Department of Electrical and Computer Engineering Queen's University, Kingston, Ontario K7L 3N6, Canada. korenber@post.queensu.ca
D000903:Antibiotics, Antineoplastic / Q000627:therapeutic use; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D003561:Cytarabine / Q000627:therapeutic use; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis / Q000188:drug therapy* / Q000235:genetics* / Q000378:metabolism; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D016896:Treatment Outcome
D000903:Antibiotics, Antineoplastic; D003561:Cytarabine