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Profound impact of sample processing delay on gene expression of multiple myeloma plasma cells

Meißner, Tobias ; Seckinger, Anja ; Hemminki, Kari ; Bertsch, Uta ; Försti, Asta ; Hänel, Mathias ; Duering, Jan ; Salwender, Hans ; Goldschmidt, Hartmut ; Morgan, Gareth J ; Hose, Dirk ; Weinhold, Niels

In: BMC Medical Genomics, 8 (2015), Nr. 85. pp. 1-8. ISSN 1755-8794

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Download (1MB) | Lizenz: Creative Commons LizenzvertragProfound impact of sample processing delay on gene expression of multiple myeloma plasma cells by Meißner, Tobias ; Seckinger, Anja ; Hemminki, Kari ; Bertsch, Uta ; Försti, Asta ; Hänel, Mathias ; Duering, Jan ; Salwender, Hans ; Goldschmidt, Hartmut ; Morgan, Gareth J ; Hose, Dirk ; Weinhold, Niels underlies the terms of Creative Commons Attribution 3.0 Germany

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Abstract

Background: Gene expression profiling (GEP) has significantly contributed to the elucidation of the molecular heterogeneity of multiple myeloma plasma cells (MMPC) and only recently it has been recommended for risk stratification. Prior to GEP MMPC need to be enriched resulting in an inability to immediately freeze bone marrow aspirates or use RNA stabilization reagents. As a result in multi-center MM trials sample processing delay due to shipping may be an important confounder of molecular analyses and risk stratification based on GEP data. Results: We compared GEP data of 145 in-house and 246 shipped samples and detected 3301 down-regulated and 3501 up-regulated genes in shipped samples. For 3994 genes we confirmed differential expression in an independent set of 85 in-house and 97 shipped samples. Differentially expressed genes were enriched in processes like ribosome biogenesis, cell cycle, and apoptosis. Among GEP based risk predictors the IFM-15 seemed to underestimate high risk in shipped samples, whereas the GEP70 and the EMC-92 gene signatures were more robust. In order to provide a tool to assess the “shipping effect” in public repositories, we generated a 17-gene predictor for shipped samples with a 10-fold cross validation error rate of 0.06 for the training set and an error rate of 0.15 for the validation set. Conclusion: Sample processing delay significantly influences GEP of MMPC, implying it should be avoided if samples were used for risk stratification.

Item Type: Article
Journal or Publication Title: BMC Medical Genomics
Volume: 8
Number: 85
Publisher: BioMed Central
Place of Publication: London
Date Deposited: 15 Feb 2016 14:17
Date: 2015
ISSN: 1755-8794
Page Range: pp. 1-8
Faculties / Institutes: Service facilities > German Cancer Research Center (DKFZ)
Medizinische Fakultät Heidelberg > Medizinische Universitäts-Klinik und Poliklinik
Subjects: 610 Medical sciences Medicine
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