Gene expression profiling in acute leukemias: New insights into biology and a global approach to the diagnosis of leukemia using microarray technology

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.