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BMC Cancer  2006 

Translating microarray data for diagnostic testing in childhood leukaemia

DOI: 10.1186/1471-2407-6-229

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We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort.We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups.Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team.Acute lymphoblastic leukaemia (ALL) is a heterogeneous disease characterized by the presence of several subtypes that are of prognostic relevance. These subtypes can be distinguished based on immunophenotype, differentiation status, as well as chromosomal and molecular abnormalities. The identification of different ALL subtypes, the characterization of prognostic features, and the finding that ALL subtypes differ in their response to t


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