ArticleProceedings of the National Academy of Sciences of the United States of America2014
Sample processing obscures cancer-specific alterations in leukemic transcriptomes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed, 88 citations in OpenAlex.
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- Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia.Haematologica · 2023Article
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- Molecular map of chronic lymphocytic leukemia and its impact on outcome.Nature genetics · 2022Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Substantial effort is currently devoted to identifying cancer-associated alterations using genomics. Here, we show that standard blood collection procedures rapidly change the transcriptional and posttranscriptional landscapes of hematopoietic cells, resulting in biased activation of specific biological pathways; up-regulation of pseudogenes, antisense RNAs, and unannotated coding isoforms; and RNA surveillance inhibition. Affected genes include common mutational targets and thousands of other genes participating in processes such as chromatin modification, RNA splicing, T- and B-cell activation, and NF-κB signaling. The majority of published leukemic transcriptomes exhibit signals of this incubation-induced dysregulation, explaining up to 40% of differences in gene expression and alternative splicing between leukemias and reference normal transcriptomes. The effects of sample processing are particularly evident in pan-cancer analyses. We provide biomarkers that detect prolonged incubation of individual samples and show that keeping blood on ice markedly reduces changes to the transcriptome. In addition to highlighting the potentially confounding effects of technical artifacts in cancer genomics data, our study emphasizes the need to survey the diversity of normal as well as neoplastic cells when characterizing tumors.
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