Abstract

The application of global gene expression profiling allows to obtain detailed molecular fingerprints of underlying gene expression in any cell of interest. In this work gene expression profiles were generated from a comprehensive cohort of leukemia patients and healthy donors referred to and diagnosed in the Laboratory for Leukemia Diagnostics, Munich, Germany, which is a nation-wide reference center for the diagnosis of hematologic malignancies. Thoroughly characterized clinical samples were analyzed by high-density microarrays interrogating the expression status of more than 33,000 transcripts. In one specific aspect of this work the potential application of gene expression signatures for the prediction and classification of specific leukemia subtypes was assessed. Today the diagnosis and subclassification of leukemias is based on a controlled application of various techniques including cytomorphology, cytogenetics, fluorescence in situ hybridization, multiparameter flow cytometry, and PCR-based methods. The diagnostic procedure is performed according to a specific algorithm, but is time-consuming, cost-intensive, and requires expert knowledge. Based on a very low number of candidate genes it is demonstrated in this work that prognostically relevant acute leukemia subtypes can be classified using microarray technology. Moreover, in an expanded analysis including 937 patient samples representing 12 distinct clinically relevant acute and chronic leukemia subtypes and healthy, non-leukemia bone marrow specimens a diagnostic prediction accuracy of ~95% was achieved. Thus, given these results it can be postulated that the occurring patterns in gene expression would be so robust that they would allow to predict the leukemia subtype using global gene expression profiling technology. This finding is further substantiated through the demonstration that reported differentially expressed genes from the literature, namely pediatric gene expression signatures representing various acute lymphoblastic leukemia (ALL) subtypes, can be used to independently predict the corresponding adult ALL subtypes. Furthermore, it could be demonstrated that microarrays both confirm and reproduce data from standard diagnostic procedures, but also provide very robust results. Parameters such as partial RNA degradation, shipment time of the samples, varying periods of storage of the samples, or target preparations at different time points from either bone marrow or peripheral blood specimens by different operators did not dramatically influence the diagnostic gene expression signatures. In another major aspect of this work gene expression signatures were examined in detail to obtain new insights into the underlying biology of acute promyelocytic leukemia (APL) and t(11q23)/MLL leukemias. In APL, microarrays led to a deeper understanding of morphological and clinical characteristics. Firstly, genes which have a functional relevance in blood coagulation were found to be differentially expressed when APL was compared to other acute myeloid leukemia (AML) subtypes. Secondly, a supervised pairwise comparison between the two different APL phenotypes, M3 and its variant M3v, for the first time revealed differentially expressed genes encoding for biological functions and pathways such as granulation and maturation. With respect to 11q23 leukemias it could be demonstrated that leukemias with rearrangements of the MLL gene are characterized by a common specific gene expression signature. Additionally, in unsupervised and supervised data analysis algorithms ALL and AML cases with t(11q23)/MLL segregated according to the lineage, i.e., myeloid or lymphoid, respectively. This segregation could be explained by a highly differing transcriptional program. Through the use of biological network analyses essential regulators of early B cell development, PAX5 and EBF, were shown to be associated with a clear B-lineage commitment in lymphoblastic t(11q23)/MLL leukemias. Also, the influence of the different MLL translocation partners on the transcriptional program was directly assessed. But interestingly, gene expression profiles did not reveal a clear distinct pattern associated with one of the analyzed partner genes. Taken together, the identified molecular expression pattern of MLL fusion gene samples and biological networks revealed new insights into the aberrant transcriptional program in t(11q23)/MLL leukemias. In addition, a series of analyses was targeted to obtain new insights into the underlying biology in heterogeneous B-lineage leukemias not positive for BCR/ABL or MLL gene rearrangements. It could be demonstrated that the genetically more heterogeneous precursor B-ALL samples intercalate with BCR/ABL-positive cases, but their profiles were clearly distinct from T-ALL and t(11q23)/MLL cases. In conclusion, various unsupervised and supervised data analysis strategies demonstrated that defined leukemia subtypes can be characterized on the basis of distinct gene expression signatures. Specific gene expression patterns reproduced the taxonomy of this hematologic malignancy, provided new insights into different disease subtypes, and identified critical pathway components that might be considered for future therapeutic intervention. Based on these results it is now further possible to develop a one-step diagnostic approach for the diagnosis of leukemias using a customized microarray.