3'-end Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2010 Jan 26

PMID 20098735

Citations 72

Authors

Andrew H Beck

Ziming Weng

Daniela M Witten

Shirley Zhu

Joseph W Foley

Phil Lacroute

Cheryl L Smith

Robert Tibshirani

Matt van de Rijn

Arend Sidow

Robert B West

Affiliations

Soon will be listed here.

Abstract

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3'-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (approximately 9.6K genes) and FFPET (approximately 8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (approximately 4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.

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References

Segal E, Friedman N, Koller D, Regev A . A module map showing conditional activity of expression modules in cancer. Nat Genet. 2004; 36(10):1090-8. DOI: 10.1038/ng1434. View

Wheeler D, Srinivasan M, Egholm M, Shen Y, Chen L, McGuire A . The complete genome of an individual by massively parallel DNA sequencing. Nature. 2008; 452(7189):872-6. DOI: 10.1038/nature06884. View

Huang D, Sherman B, Lempicki R . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57. DOI: 10.1038/nprot.2008.211. View

Shendure J, Porreca G, Church G . Overview of DNA sequencing strategies. Curr Protoc Mol Biol. 2008; Chapter 7:Unit 7.1. DOI: 10.1002/0471142727.mb0701s81. View

Luo L, Salunga R, Guo H, Bittner A, Joy K, Galindo J . Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat Med. 1999; 5(1):117-22. DOI: 10.1038/4806. View

Simon R . Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005; 23(29):7332-41. DOI: 10.1200/JCO.2005.02.8712. View

Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T . Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods. 2007; 4(8):651-7. DOI: 10.1038/nmeth1068. View

Bullinger L, Dohner K, Bair E, Frohling S, Schlenk R, Tibshirani R . Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med. 2004; 350(16):1605-16. DOI: 10.1056/NEJMoa031046. View

Valouev A, Johnson D, Sundquist A, Medina C, Anton E, Batzoglou S . Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods. 2009; 5(9):829-34. PMC: 2917543. DOI: 10.1038/nmeth.1246. View

10.

Linton K, Hey Y, Saunders E, Jeziorska M, Denton J, Wilson C . Acquisition of biologically relevant gene expression data by Affymetrix microarray analysis of archival formalin-fixed paraffin-embedded tumours. Br J Cancer. 2008; 98(8):1403-14. PMC: 2361698. DOI: 10.1038/sj.bjc.6604316. View

11.

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M . A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351(27):2817-26. DOI: 10.1056/NEJMoa041588. View

12.

Ross D, Scherf U, Eisen M, Perou C, Rees C, Spellman P . Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000; 24(3):227-35. DOI: 10.1038/73432. View

13.

Vant Veer L, Bernards R . Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature. 2008; 452(7187):564-70. DOI: 10.1038/nature06915. View

14.

Oshlack A, Wakefield M . Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009; 4:14. PMC: 2678084. DOI: 10.1186/1745-6150-4-14. View

15.

Alizadeh A, Eisen M, Davis R, Ma C, Lossos I, Rosenwald A . Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000; 403(6769):503-11. DOI: 10.1038/35000501. View

16.

Mohr S, Leikauf G, Keith G, Rihn B . Microarrays as cancer keys: an array of possibilities. J Clin Oncol. 2002; 20(14):3165-75. DOI: 10.1200/JCO.2002.12.073. View

17.

J van t Veer L, Dai H, van de Vijver M, He Y, Hart A, Mao M . Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002; 415(6871):530-6. DOI: 10.1038/415530a. View

18.

Steigen S, Schaeffer D, West R, Nielsen T . Expression of insulin-like growth factor 2 in mesenchymal neoplasms. Mod Pathol. 2009; 22(7):914-21. DOI: 10.1038/modpathol.2009.48. View

19.

Lakhani S, Ashworth A . Microarray and histopathological analysis of tumours: the future and the past?. Nat Rev Cancer. 2002; 1(2):151-7. DOI: 10.1038/35101087. View

20.

Jilong Y, Jian W, Xiaoyan Z, Xiaoqiu L, Xiongzeng Z . Analysis of APC/beta-catenin genes mutations and Wnt signalling pathway in desmoid-type fibromatosis. Pathology. 2007; 39(3):319-25. DOI: 10.1080/00313020701329823. View