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Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients

Rapin, Nicolas; Bagger, Frederik Otzen; Jendholm, Johan; Mora-Jensen, Helena; Krogh, Anders; Kohlmann, Alexander; Thiede, Christian; Borregaard, Niels; Bullinger, Lars and Winther, Ole, et al. (2014) In Blood 123(6). p.894-904
Abstract
Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This... (More)
Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients. (Less)
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organization
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type
Contribution to journal
publication status
published
subject
in
Blood
volume
123
issue
6
pages
894 - 904
publisher
American Society of Hematology
external identifiers
  • wos:000335834600015
  • scopus:84897578401
ISSN
1528-0020
DOI
10.1182/blood-2013-02-485771
language
English
LU publication?
yes
id
c6ea9733-0010-4519-abb4-b45fd0b3c7d6 (old id 4470337)
date added to LUP
2014-07-01 07:38:38
date last changed
2017-08-20 03:12:28
@article{c6ea9733-0010-4519-abb4-b45fd0b3c7d6,
  abstract     = {Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.},
  author       = {Rapin, Nicolas and Bagger, Frederik Otzen and Jendholm, Johan and Mora-Jensen, Helena and Krogh, Anders and Kohlmann, Alexander and Thiede, Christian and Borregaard, Niels and Bullinger, Lars and Winther, Ole and Theilgaard-Moench, Kim and Porse, Bo T.},
  issn         = {1528-0020},
  language     = {eng},
  number       = {6},
  pages        = {894--904},
  publisher    = {American Society of Hematology},
  series       = {Blood},
  title        = {Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients},
  url          = {http://dx.doi.org/10.1182/blood-2013-02-485771},
  volume       = {123},
  year         = {2014},
}