Gene expression profiles (GEPs) are becoming increasingly more important for diagnostic procedures, allowing clinical predictions including treatment response and outcome for various types of cancer. However, established gene signatures not always appear helpful in understanding underlying disease mechanisms. Therefore, we explored an alternative approach for analyzing GEPs of a group of t(4;11) positive infant acute lymphoblastic leukemia patients (n=15), a highly aggressive type of MLL rearranged leukemia. We developed a method that uses a relational database program in combination with a normalization approach and specific discriminators. For normalization, every GEP of a given t(4;11) positive ALL sample was compared with 3 GEPs of normal bone marrow aspirates derived from healthy donors. Using the GeneChip Analysis Suite 5.0 program for single comparison analysis, the resulting gene lists were then compared with each other and only consistently identified genes (present on all three gene lists) were used for further analysis. This noise reduction decreased the amount of potentially deregulated target genes to about 30–40%. The remaining gene lists represented highly significant target genes that were then incorporated into a relational database program using specific discriminators. These discriminators were:
upregulation of HOXA9 and HOXA10,
presence/absence of the AF4-MLL fusion transcripts in addition to the MLL-AF4 fusion, and
the localization of the genomic breakpoint within the MLL gene.
This pilot study led to promising results, surprisingly classifying individual t(4;11) positive ALL patients into two distinct subgroups. Both subgroups share about 80 target genes, but also display particular sets of subgroup specific target genes. Importantly, these identified target genes can directly be linked to biological properties of t(4;11) positive leukemia cells, and therefore, allow important novel insights into this aggressive type of leukemia in infants.
Disclosure: No relevant conflicts of interest to declare.