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Identification and functional characterisation of
molecular risk factors in acute leukemias
Renate Kirschner1, Michaela Heide1, Peter Rhein1, Leonid Karawajew1, Matthias Nees2, Stephen Breit2,
Andreas Kulozik2, Christian Hagemeier1, Wolf-Dieter Ludwig1, Rainer Spang3, Karl Seeger1
1
Charité, Humboldt-Universität Berlin, 2 Universitäts-Kinderklinik Heidelberg, 3 Max-Planck-Institut für molekulare Genetik Berlin,
Childhood acute lymphoblastic leukemia (ALL) occurs with an incidence of approximately 600 patients per year in Germany. In general, up to
75% of children can be cured permanently by chemotherapy. ALL relapses (approximately 100 cases per year) are more resistant to treatment with
a cure rate of less than 50%. Therefore, novel approaches in terms of diagnosis and therapy are particularly needful for this group. In order to
identify novel prognostic factors and to unravel molecular mechanisms underlying clinical outcome, we aim to generate gene expression profiles of
initial and relapsed ALL of the Berlin-Frankfurt Münster (ALL-BFM and ALL-REZ BFM) study group by Affymetrix DNA microarray
technology.
Classification
Initial ALL
Relapsed ALL
(ALL-BFM trial)
(ALL-REZ BFM trial)
Retrospective Study:
Clinical Outcome
Event-free Survival
Event-free Survival
1. Relapse
( 3 years)
( 3 years)
2. Relapse
Non-Response
For a retrospective study patients are
classified by clinical outcome,
whereas for a prognostic study proven
risk factors as for example ‘minimal
residual disease’ - MRD will be used
to divide patients into subgroups.
MRD sensitively measures the
amount of leukemic cells that are still
present at certain time points during
therapy.
Prospective Study:
Therapy Response
Prednisone
Response1
Poor
Good
1
Dördelmann et al. 1999. Blood 94: 1209-1217;
3 Eckert et al. 2001. Lancet 358: 1239-1241
Minimal Residual
Disease (MRD)2
2
Negative
Positive
Minimal Residual
Disease (MRD)3
Negative
Bone marrow samples for gene
expression profiling are selected from
patients who have been treated
according to the protocols of the ALLBFM and ALL-REZ BFM study
groups for intial and relapsed acute
lymphoblastic leukemia, respectively.
Non-Response
Positive
van Dongen et al. 1989. Lancet 352: 1731-1738
Optimising Gene Expression Profiling
• RNA Preparation
Isolation of Minor Subpopulations from Heterogeneous Leukemic Samples
Bone marrow biopsies
sent from clinical centers
Lab of the study group:
Isolation of mononuclear cells
Cryopreservation
RNA Preparation
RNA Preparation
28 S
28 S
18 S
18 S
In order to perform retrospective studies, we isolated RNA from cryopreserved mononuclear cells (magenta).
In a significant number of samples loss of sufficient RNA quality and quantity was observed. RNA quality
was also evaluated on a Bioanalyzer™ and via hybridisation of Affymetrix Test3Arrays™ showing a high
degree of consistency. For prospective studies we therefore routinely prepare RNA directly from incoming
bone marrow biopsies with an optimized yet straight forward protocol (light blue). This should also
minimize changes of expression profiles due to cryopreservation. Retrospective studies can only be
performed on a limited number of samples (<25%).
• Evaluation and cross-validation of generated data sets with
published ALL expression profiles
cRNA
1. Pre-enrichment by magnetic cell sorting
(MACS™, Miltenyi Biotec)
2. Further isolation by flow sorting
(FACS Vantage™, Becton Dickinson)
Own data sets
facilitates and
improves
U95Av2 GeneChip™
Leukemic Cell
Sample
evaluation
Randomly selected,
high quality RNA
normaizing
profiles.
U133A GeneChip™
In the second part of the project, we focus on distinct, clinically relevant
subpopulations from initially heterogeneous leukemic cell samples. We are
especially interested on minor subpopulations of immature, progenitor-like
leukemic cells as well as on residual leukemic cell populations which have
escaped initial treatment and become more resistant to therapy. In order to
approach this issue experimentally, procedures for identification and
purification of rare leukemic blast cells based on flow cytometric analysis and
flow sorting are under development.
RNA-Extraction
Published data sets*
Databanks were created containing published genes found to be deregulated in large scale ALL profiling
studies based on Affymetrix U95Av2 GeneChips™ (eg *Yeoh et al. 2002. Cancer Cell 1: 133-143). Ongoing
co-hybridisation of 5 to 10 Probes to U95Av2 and U133A GeneChips™ will allow normalization of
expression profiles for comparing data sets created with “old” and “new” Affymetrix GeneChips. Databanks
and bioinformatic filtering tools can then be used to identify and select against unwanted signatures in
smaller test populations that would otherwise escape recognition. This part therefore aims at both, utilizing
and cross-validating existing data from different laboratories.
Linear amplification of cRNA
by in vitro transcription
Gene expression profiling
Real time PCR
(Candidate Genes)