Diagnosis and genetic subtypes of leukemia combining gene expression and flow cytometry

Acute leukemia, defined as a genetic disease, is the most common cancer in children representing about one half of all cancers among persons younger than 15 years. Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) each represents a heterogeneous complex of disorders, with genetic abnormalities presenting in more than 80% of ALLs and more than 90% of AMLs. The diagnostic gold standard and classification of leukaemia involves various methods including morphology, cytochemistry, cytogenetics and molecular genetics, immunophenotyping, and molecular biology. These diagnostic methods are a prerequisite for individual treatment strategies and for the evaluation of treatment response especially considering that many distinct types of acute leukemia are known to carry predictable prognoses and warrant specific therapy. The quantification of gene expression is essential in determination of tailored therapeutic decisions. Microarray technology offers the possibility of quantifying thousands of genes in a single analysis, thus potentially becoming an essential tool for molecular classification to be used in routine leukaemia diagnostics. MLL+ leukaemia is a perfect example as to the exact correspondence between gene expression and protein expression evaluated by flow cytometry. Applying computational analysis to flow cytometry results, it is possible to distinguish the MLL+ acute leukemia from MLL- acute leukemia using as the top ranked antigen some top ranked genes described in the Microarray evaluation. Key markers discriminating different leukemia phenotypes can be identified by univariate hypothesis testing from a data set of immunophenotypic markers described by two variables, one reflecting the intensity of expression (MESF) and the other the pattern of distribution (CV). A current multi center study called Microarray Innovations in Leukemia (MILE Study) uses higher density gene chips providing nearly complete coverage of the human genome. The study which has analyzed thus far 1837 retrospective cases shows that each important leukemia subtype has a specific genetic fingerprint, meaning that different combinations of genes whose expression is linked to each subtype can be identified allowing for patient tailored therapy. Moreover, the study has achieved 97% diagnostic accuracy on samples from tested patients. Statistical analysis has shown a high concordance level between standard diagnostic procedures and those of the microarray technology--globally around 95.6%. Additionally it is possible to correctly classify some subgroups incorrectly identified using gold standard methods. Thus, from a technical viewpoint, gene expression profiling in tandem with flow cytometry should be a viable alternative to standard diagnostic approaches. Whether gene expression profiling will become a practical diagnostic alternative remains to be seen.