Whole-Transcriptome profiling of formalin-fixed, paraffin-embedded renal cell carcinoma by RNA-seq

BMC Genomics

Hyung L Kim²  Hao Zhang³  Andrew Conley¹  Ping Li² 

[1] Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA;Department of Surgery, Cedars-Sinai Medical Center, 8635 West Third Street #1070W, Los Angeles, CA 90048, USA;Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA

关键词: Gene expression;    RNA-seq;    Renal cell carcinoma (RCC);    qPCR;    Formalin-fixed paraffin-embedded (FFPE);   

Others  :  1127437
DOI  :  10.1186/1471-2164-15-1087

 received in 2014-07-02, accepted in 2014-11-26,  发布年份 2014

【 摘 要 】

Background

Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can be linked to clinical outcomes with long-term follow-up. However, FFPE tissues have degraded RNA which poses challenges for analyzing gene expression. Next-generation sequencing (NGS) is rapidly becoming accepted as an effective tool for measuring gene expressions for research and clinical use. However, the feasibility of NGS has not been firmly established when using FFPE tissue.

Results

We optimized strategies for whole transcriptome sequencing (RNA-seq) using FFPE tissue. Ribosomal RNA (rRNA) was successfully depleted by competitive hybridization using the Ribo-zero™ Kit (Epicentre Biotechnologies), and rRNA sequence content was less than one percent for each library. Gene expression measured by FFPE RNA-seq was compared to two different standards: RNA-seq from fresh frozen (FF) tissue and quantitative PCR (qPCR). Both FF and FFPE tumors were sequenced on an Illumina Genome Analyzer IIX with an average of 10 million reads. The distribution of FPKMs (fragments per kilobase of exon per million fragments mapped) and number of detected genes were similar between FFPE and FF. RNA-seq expressions from FF and FFPE samples from the same renal cell carcinoma (RCC) correlated highly (r = 0.919 for tumor 1 and r = 0.954 for tumor 2). On hierarchical cluster analysis, samples clustered by patient identity rather than method of preservation. TaqMan qPCR of 424 RCC-related genes correlated highly with FFPE RNA-seq expressions (r = 0.775 for FFPE tumor 1, r = 0.803 for FFPE tumor 2). Expression fold changes were considered, to assess biologic relevance of gene expressions. Expression fold changes between FFPE tumors (tumor 1/tumor 2) correlated well when comparing qPCR and RNA-seq (r = 0.890). Expression fold changes between tumors from different risk groups (our high risk RCC/The Cancer Genome Atlas, TCGA, low risk RCC) also correlated well when comparing RNA-seq from FF and FFPE tumors (r = 0.887).

Conclusions

FFPE RNA-seq provides reliable genes expression data, comparable to that obtained from fresh frozen tissue. It represents a useful tool for discovery and validation of biomarkers.

【 授权许可】

   
2014 Li et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 1.

Figure 5.

Figure 6.

【 参考文献 】

• [1]Mackay IM, Arden KE, Nitsche A: Real-time PCR in virology. Nucleic Acids Res 2002, 30:1292-1305.
• [2]van’t Veer LJ, Bernards R: Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008, 452:564-570.
• [3]Arya M, Shergill IS, Williamson M, Gommersall L, Arya N, Patel HR: Basic principles of real-time quantitative PCR. Expert Rev Mol Diagn 2005, 5:209-219.
• [4]Wilhelm J, Pingoud A: Real-time polymerase chain reaction. Chembiochem 2003, 4:1120-1128.
• [5]Wong ML, Medrano JF: Real-time PCR for mRNA quantitation. Biotechniques 2005, 39:75-85.
• [6]Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63.
• [7]Cancer Genome Atlas Research Network: Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013, 499:43-49.
• [8]Tuch BB, Laborde RR, Xu X, Gu J, Chung CB, Monighetti CK, Stanley SJ, Olsen KD, Kasperbauer JL, Moore EJ, Broomer AJ, Tan R, Brzoska PM, Muller MW, Siddiqui AS, Asmann YW, Sun Y, Kuersten S, Barker MA, De La Vega FM, Smith DI: Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One 2010, 5:e9317.
• [9]Guo G, Sun X, Chen C, Wu S, Huang P, Li Z, Dean M, Huang Y, Jia W, Zhou Q, Tang A, Yang Z, Li X, Song P, Zhao X, Ye R, Zhang S, Lin Z, Qi M, Wan S, Xie L, Fan F, Nickerson ML, Zou X, Hu X, Xing L, Lv Z, Mei H, Gao S, Liang C, et al.: Whole-genome and whole-exome sequencing of bladder cancer identifies frequent alterations in genes involved in sister chromatid cohesion and segregation. Nat Genet 2013, 45:1459-1463.
• [10]Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, Sam L, Barrette T, Palanisamy N, Chinnaiyan AM: Transcriptome sequencing to detect gene fusions in cancer. Nature 2009, 458:97-101.
• [11]Sinicropi D, Qu K, Collin F, Crager M, Liu ML, Pelham RJ, Pho M, Dei Rossi A, Jeong J, Scott A, Ambannavar R, Zheng C, Mena R, Esteban J, Stephans J, Morlan J, Baker J: Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue. PLoS One 2012, 7:e40092.
• [12]Beck AH, Weng Z, Witten DM, Zhu S, Foley JW, Lacroute P, Smith CL, Tibshirani R, van de Rijn M, Sidow A, West RB: 3’-end sequencing for expression quantification (3SEQ) from archival tumor samples. PLoS One 2010, 5:e8768.
• [13]Xiao YL, Kash JC, Beres SB, Sheng ZM, Musser JM, Taubenberger JK: High-throughput RNA sequencing of a formalin-fixed, paraffin-embedded autopsy lung tissue sample from the 1918 influenza pandemic. J Pathol 2013, 229:535-545.
• [14]Hedegaard J, Thorsen K, Lund MK, Hein AM, Hamilton-Dutoit SJ, Vang S, Nordentoft I, Birkenkamp-Demtröder K, Kruhøffer M, Hager H, Knudsen B, Andersen CL, Sørensen KD, Pedersen JS, Ørntoft TF, Dyrskjøt L: Next-generation sequencing of RNA and DNA isolated from paired fresh-frozen and formalin-fixed paraffin-embedded samples of human cancer and normal tissue. PLoS One 2014, 9(5):e98187.
• [15]Li P, Grigorenko E, Funari V, Enright E, Zhang H, Kim HL: Evaluation of a high-throughput, microfluidics platform for performing TaqMan™ qPCR using formalin-fixed paraffin-embedded tumors. Bioanalysis 2013, 5:1623-1633.
• [16]Glenn ST, Jones CA, Liang P, Kaushik D, Gross KW, Kim HL: Expression profiling of archival renal tumors by quantitative PCR to validate prognostic markers. Biotechniques 2007, 43:639-640. 642-643, 647
• [17]Thompson E, Burt AD, Barker CE, Kirby JA, Brain JG: Development of a robust protocol for gene expression analysis using formalin-fixed, paraffin-embedded liver transplant biopsy specimens. J Clin Pathol 2013, 66:815-818.
• [18]Takahashi M, Rhodes DR, Furge KA, Kanayama H, Kagawa S, Haab BB, Teh BT: Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc Natl Acad Sci U S A 2001, 98:9754-9759.
• [19]Zhao H, Ljungberg B, Grankvist K, Rasmuson T, Tibshirani R, Brooks JD: Gene expression profiling predicts survival in conventional renal cell carcinoma. PLoS Med 2006, 3:e13.
• [20]Vasselli JR, Shih JH, Iyengar SR, Maranchie J, Riss J, Worrell R, Torres-Cabala C, Tabios R, Mariotti A, Stearman R, Merino M, Walther MM, Simon R, Klausner RD, Linehan WM: Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor. Proc Natl Acad Sci U S A 2003, 100:6958-6963.
• [21]Brannon AR, Reddy A, Seiler M, Arreola A, Moore DT, Pruthi RS, Wallen EM, Nielsen ME, Liu H, Nathanson KL, Ljungberg B, Zhao H, Brooks JD, Ganesan S, Bhanot G, Rathmell WK: Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 2010, 1:152-163.
• [22]Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, Shimamura T, Sato-Otsubo A, Nagae G, Suzuki H, Nagata Y, Yoshida K, Kon A, Suzuki Y, Chiba K, Tanaka H, Niida A, Fujimoto A, Tsunoda T, Morikawa T, Maeda D, Kume H, Sugano S, Fukayama M, Aburatani H, Sanada M, Miyano S, Homma Y, Ogawa S: Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 2013, 45:860-867.
• [23]Glenn ST, Head KL, Teh BT, Gross KW, Kim HL: Maximizing RNA yield from archival renal tumors and optimizing gene expression analysis. J Biomol Screen 2010, 15:80-85.