Sample processing obscures cancer-specific alterations in leukemic transcriptomes Heidi Dvingea,b, Rhonda E. Riesc, Janine O. Ilagana,b, Derek L. Stirewaltc, Soheil Meshinchic,d, and Robert K. Bradleya,b,1 aComputational Biology Program, Public Health Sciences Division, bBasic Sciences Division, and cClinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109; and dDivision of Pediatric Hematology/Oncology, School of Medicine, University of Washington, Seattle, WA 98195 Edited* by Robert N. Eisenman, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved October 14, 2014 (received for review July 14, 2014) Substantial effort is currently devoted to identifying cancer-associ- cause globally decreased/increased transcription or accelerated ated alterations using genomics. Here, we show that standard RNA degradation, depending upon conditions (8–15). blood collection procedures rapidly change the transcriptional and Genomic studies of leukemias may be particularly susceptible posttranscriptional landscapes of hematopoietic cells, resulting in to sample-intrinsic artifacts in comparison with similar profiling biased activation of specific biological pathways; up-regulation of of solid tumors. For many solid tumors, patient-matched normal pseudogenes, antisense RNAs, and unannotated coding isoforms; samples can be acquired from adjacent uninvolved tissue, and and RNA surveillance inhibition. Affected genes include common the matched tumor/normal samples can be subsequently handled mutational targets and thousands of other genes participating in similarly. In contrast, the circulating nature of leukemic cells ren- processes such as chromatin modification, RNA splicing, T- and B-cell ders the acquisition of patient-matched controls less straightfor- activation, and NF-κB signaling. The majority of published leukemic ward, so samples from unrelated healthy donors are typically used transcriptomes exhibit signals of this incubation-induced dysregula- to generate reference normal transcriptomes (Fig. 1A). Leukemic tion, explaining up to 40% of differences in gene expression and samples are commonly collected by the treating physician and then alternative splicing between leukemias and reference normal tran- shipped for a variable length of time as whole blood or bone scriptomes. The effects of sample processing are particularly evident marrow to a research center for subsequent processing (Fig. 1B). in pan-cancer analyses. We provide biomarkers that detect pro- In contrast, control samples are collected expressly for research longed incubation of individual samples and show that keeping GENETICS rather than clinical use and therefore may be more rapidly blood on ice markedly reduces changes to the transcriptome. In processed by the collecting research institution or company. addition to highlighting the potentially confounding effects of tech- The substantial literature documenting transcriptional changes nical artifacts in cancer genomics data, our study emphasizes the caused by ex vivo incubation suggests that comparing the tran- need to survey the diversity of normal as well as neoplastic cells when characterizing tumors. scriptomes of differentially handled leukemic and normal samples is likely problematic. However, the impact of ex vivo incubation on leukemia | RNA splicing | nonsense-mediated decay | batch effects the transcriptome has not been systematically assessed with high- throughput sequencing, and the potential extent of incubation- induced artifacts within large-scale leukemia studies has not ecent years have seen rapid growth in the large-scale char- been measured. Here, we identify transcriptional and post- acterization of tumors by consortia such as The Cancer R transcriptional changes caused by ex vivo sample incubation Genome Atlas (TCGA), the International Cancer Genome Con- and search for signs of these artifacts in published leukemia sortium, and the Therapeutically Applicable Research to Gener- studies (Table S1). Our results show that widespread biological ate Effective Treatments project. Although the high-throughput changes caused by ex vivo sample incubation can confound the assays used by these consortia are precise, analyses of the resulting identification of cancer-specific alterations in many leukemia data can be confounded by artifacts arising from both biological genomics studies. and technical variability. Cancer studies are susceptible to both “sample-intrinsic” (arising from sample handling itself, including specimen collection, sample transfer and storage, and isolation Significance of material) and “assay-intrinsic” (associated with specific assays, such as microarrays or high-throughput sequencing, or differences An important goal of cancer biology is to identify molecular dif- in equipment, reagents, or personnel) artifacts. Sample-intrinsic ferences between normal and cancer cells. Accordingly, many artifacts depend on the tissue assayed, may distinguish otherwise large-scale initiatives to characterize both solid and liquid tumor similar biological specimens, and typically cannot be eliminated samples with genomics technologies are currently underway. without discarding affected biospecimens. In contrast, assay- Here, we show that standard blood collection procedures cause intrinsic artifacts are agnostic to the biological specimen and rapid changes to the transcriptomes of hematopoietic cells. The frequently can be experimentally or statistically mitigated once resulting transcriptional and posttranscriptional artifacts are visi- recognized (1). ble in most published leukemia genomics datasets and hinder the Although assay-intrinsic artifacts have received widespread identification and interpretation of cancer-specific alterations. attention in the context of genomics data (1–5), the impact of Author contributions: H.D., R.E.R., S.M., and R.K.B. designed research; H.D. and J.O.I. sample-intrinsic artifacts on high-throughput studies has been performed research; D.L.S. contributed new reagents/analytic tools; H.D., R.E.R., S.M., less well explored. Nonetheless, previous reports suggest that and R.K.B. analyzed data; and H.D. and R.K.B. wrote the paper. sample-intrinsic artifacts are likely important to consider when The authors declare no conflict of interest. interpreting genomics data. For example, blood collection pro- *This Direct Submission article had a prearranged editor. cedures and the choice of anticoagulant can impact subsequent Freely available online through the PNAS open access option. clinical chemistry assays or affect hematological parameters such Data deposition: The RNA-sequencing data reported in this paper have been deposited in as cell number and morphology (6, 7). Similarly, blood shipping the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession nos. temperature and the timing of sample processing can affect levels GSE58335 and GSE61410). of protein-based biomarkers and other analytes in plasma or se- 1To whom correspondence should be addressed. Email: [email protected]. IL8 IL10 CCR2 SOCS2 rum, alter the transcription (e.g., , , , ,or This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. JUN) or splicing (e.g., NF1, PTEN,orATM) of specific genes, or 1073/pnas.1413374111/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1413374111 PNAS Early Edition | 1of6 Downloaded by guest on September 27, 2021 Solid Liquid A B Blood draw C 10 0h 4h 8 Sample transfer 8h 24h 6 Isolate cell 48h 4 Other non-coding RNA subpopulation Donor 1 (cryopreserved) Donor 2 (cryopreserved) 2 Antisense RNACassette exons Donor 3 Pseudogenes Cryopreservation Donor 4 Coding RNA RNA integrity number 0 Tumor sample Tumor sample lncRNA Matched normal Un-matched normal RNA extraction 04 8 24 48 D Time (h) E Coding RNA Non-coding RNA Cassette exons 2 up-regulated down-regulated () 2 -1 0 1 Log Gene expression and splicing down-regulated up-regulated Time (h) vs. 0h vs vs. 0h FGNOTCH2 (chr1:120,484-120,506 bp) PBMC PBMC vs 0h 30 20 4h 10 8h 0 24h lap with PBMCs 30 48h ver 20 LEF1 (chr4:109,000-109,005 bp) PHF20 (chr20:34,360-34,390 bp) 10 Increase Decrease Percent o 0 0h 4h BreastColon Prostate 8h 24h 48h Fig. 1. Ex vivo blood incubation causes rapid transcriptional and posttranscriptional changes. (A) Biospecimen collection for solid and liquid tumors. (B) Blood sample processing. Whole blood samples frequently incur an ex vivo incubation between collection and processing, which we mimicked in the indicated time series. (C) RNA integrity numbers (RINs) from four individual healthy PBMC donors. For the cryopreserved samples (donors 1 and 2), the first time point was at 1 h rather than 0 h. (D) Numbers of differentially expressed protein-coding and noncoding transcripts and differentially spliced cassette exons relative to the first (0 or 1 h) time point. Legend is as in C.(E)Log2 ratio of numbers of up- vs. down-regulated transcripts or cassette exons with increased vs. decreased inclusion at 48 h. Legend is as in C.(F) RNA-seq coverage along excerpts of NOTCH2, LEF1, and PHF20 (donor 4). Introns are truncated at the vertical dashed lines. The inclusion of specific exons or introns (orange boxes) is time-dependent. (G) Overlap between differential gene expression or splicing in PBMCs (0 vs. 48 h; intersection of donors 3 and 4) and tumors vs. normal controls. Solid lines, median overlap per dataset; shading, first and third quartiles of the overlap; dashed lines, median across all solid tumors. Differential