An international comparability study on quantification of mRNA gene expression ratios: CCQM-P103.1

Biomolecular Detection and Quantification

Volumn 8, Issue , 2016, Pages 15-28

  Devonshire, Alison S ^a   Sanders, Rebecca ^a   Whale, Alexandra S ^a   Nixon, Gavin J ^a   Cowen, Simon ^a   Ellison, Stephen L R ^a   Parkes, Helen ^a   Pine, P Scott ^b   Salit, Marc ^b   McDaniel, Jennifer ^b   Munro, Sarah ^b   Lund, Steve ^b   Matsukura, Satoko ^c   Sekiguchi, Yuji ^c   Kawaharasaki, Mamoru ^c,j   Granjeiro, José Mauro ^d   Falagan Lotsch, Priscila ^d   Saraiva, Antonio Marcos ^d   Couto, Paulo ^d   Yang, Inchul ^e   Kwon, Hyerim ^e   Park, Sang Ryoul ^e   Demšar, Tina ^f   Žel, Jana ^f   Blejec, Andrej ^f   Milavec, Mojca ^f   Dong, Lianhua ^g   Zhang, Ling ^g   Sui, Zhiwei ^g   Wang, Jing ^g   Viroonudomphol, Duangkamol ^h   Prawettongsopon, Chaiwat ^h   Partis, Lina ⁱ   Baoutina, Anna ⁱ   Emslie, Kerry ⁱ   Takatsu, Akiko ^j   Akyurek, Sema ^k   Akgoz, Muslum ^k   Vonsky, Maxim ^l,m   Konopelko, L A ^l   Cundapi, Edna Matus ⁿ   Urquiza, Melina Pérez ⁿ   Huggett, Jim F ^a   Foy, Carole A ^a   more..

^a LGC   (United Kingdom)

^b NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

^c NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^d Chemical Metrology Division (Dquim)   (Brazil)

^e Korea Research Institute of Standards and Science   (South Korea)

^f NATIONAL INSTITUTE OF BIOLOGY   (Slovenia)

^g NATIONAL INSTITUTE OF METROLOGY   (China)

^h National Institute of Metrology   (Thailand)

ⁱ NATIONAL MEASUREMENT INSTITUTE   (Australia)

^j NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^k NATIONAL METROLOGY INSTITUTE   (Turkey)

^l D I MENDELEYEV INSTITUTE FOR METROLOGY   (Russian Federation)

^m INSTITUTE OF CYTOLOGY   (Russian Federation)

ⁿ CENTRO NACIONAL DE METROLOGÍA   (Mexico)

more..

Author keywords

Biomarker identification and validation; Cancer; Diagnostics; Gene expression; Molecular diagnostic; Normalisation; RNA copy number ratio; RT qPCR; Standardisation; Transcriptomics

Indexed keywords

EXCITATORY AMINO ACID TRANSPORTER 2; MATRIX METALLOPROTEINASE; MESSENGER RNA; NESTIN;

ARTICLE; COPY NUMBER VARIATION; GENE EXPRESSION; GENETIC ANALYSIS; HUMAN; HUMAN CELL; IN VITRO STUDY; INFECTION; NON COMMUNICABLE DISEASE; PRIORITY JOURNAL; PROGNOSIS; REAL TIME POLYMERASE CHAIN REACTION; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION; STATISTICAL ANALYSIS; STRATIFICATION;

EID: 84973137565     PISSN: None     EISSN: 22147535     Source Type: Journal    
DOI: 10.1016/j.bdq.2016.05.003     Document Type: Article

References (47)

1. Baker S.C., et al. The external RNA controls consortium: a progress report. Nat. Methods 2005, 2(10):731-734.
2. External RNA Controls Consortium Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 2005, 6:150.
3. Devonshire A.S., Elaswarapu R., Foy C.A. Evaluation of external RNA controls for the standardisation of gene expression biomarker measurements. BMC Genomics 2010, 11:662.
4. Jiang L., et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011, 21(9):1543-1551.
5. Blomquist T.M., et al. Targeted RNA-sequencing with competitive multiplex-PCR amplicon libraries. PLoS One 2013, 8(11):e79120.
6. Ding B., et al. Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics 2015, 31(13):2225-2227.
7. White H.E., et al. Establishment and validation of analytical reference panels for the standardization of quantitative BCR-ABL1 measurements on the international scale. Clin. Chem. 2013, 59(6):938-948.
8. Kim C., Paik S. Gene-expression-based prognostic assays for breast cancer. Nat. Rev. Clin. Oncol. 2010, 7(6):340-347.
9. Kos Z., Nielsen T. Developing a new generation of breast cancer clinical gene expression tests. Breast Cancer Res. 2014, 16(4):103.
10. Sveen A., et al. Anticipating the clinical use of prognostic gene expression-based tests for colon cancer stage II and III: is godot finally arriving?. Clin. Cancer Res. 2013, 19(24):6669-6677.
11. Herman L., et al. Utility of a genomic-based: personalized medicine test in patients presenting with symptoms suggesting coronary artery disease. J. Am. Board Fam. Med. 2014, 27(2):258-267.
12. Wazir U., Mokbel K. Emerging gene-based prognostic tools in early breast cancer: first steps to personalised medicine. World J. Clin. Oncol. 2014, 5(5):795-799.
13. Denkert C., et al. Decentral gene expression analysis for ER+/Her2- breast cancer: results of a proficiency testing program for the EndoPredict assay. Virchows Arch. 2012, 460(3):251-259.
14. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001, 25(4):402-408.
15. Denkert C., et al. Molecular pathology for breast cancer: importance of the gene expression profile. Pathologe 2015, 36(2):145-153.
16. (JCGM), J.C.f.G.i.M., International Vocabulary of Metrology-Basic and General Concepts and Associated Terms (VIM 3rd edition), 2012.
17. ISO, ISO/WD 15193 In vitro diagnostic medical devices - measurement of quantities in samples of biological origin - presentation of reference measurement procedures (2005).
18. Joint Committee for Guides in Metrology (JCGM), Evaluation of measurement data-Guide to the expression of uncertainty in measurement JCGM 100, 2008 (2008).
19. Cronin M., et al. Analytical validation of the oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative: estrogen receptor-positive breast cancer. Clin. Chem. 2007, 53(6):1084-1091.
20. Vogelstein B., Kinzler K.W. Digital PCR. Proc. Natl. Acad. Sci. U. S. A. 1999, 96(16):9236-9241.
21. Sykes P.J., et al. Quantitation of targets for PCR by use of limiting dilution. Biotechniques 1992, 13(3):444-449.
22. Warren L., et al. Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc. Natl. Acad. Sci. U. S. A. 2006, 103(47):17807-17812.
23. Beer N.R., et al. On-chip: real-time, single-copy polymerase chain reaction in picoliter droplets. Anal. Chem. 2007, 79(22):8471-8475.
24. Cloonan N., et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 2008, 5(7):613-619.
25. R Core Team R: A Language and Environment for Statistical Computing 2013, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
26. Fox J., Weisberg S. An R Companion to Applied Regression 2011, Sage, Thousand Oaks CA. second ed.
27. J. Pinheiro et al., nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-102 (2011).
28. Vandesompele J., et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3(7). RESEARCH0034.
29. Staudte R.G., Sheather S.J. Robust Estimation and Testing 1990, John Wiley and Sons, New York.
30. Analytical Methods Committee Robust statistics-how not to reject outliers, part 1, basic concepts. Analyst 1989, 114:1693-1698.
31. DerSimonian R., Laird N. Meta-analysis in clinical trials. Control Clin. Trials 1986, 7(3):177-188.
32. Joint Committee for Guides in Metrology (JCGM/WG 1), Section 5.2 Correlated input quantities, in Evaluation of measurement data -Guide to the expression of uncertainty in measurement, JCGM, 100 (2008).
33. Pfaffl M.W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29(9):e45.
34. Devonshire A.S., et al. Highly reproducible absolute quantification of mycobacterium tuberculosis complex by digital PCR. Anal. Chem. 2015, 87(7):3706-3713.
35. Pinheiro L.B., et al. Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal. Chem. 2012, 84(2):1003-1011.
36. White H., et al. A certified plasmid reference material for the standardisation of BCR-ABL1 mRNA quantification by real-time quantitative PCR. Leukemia 2015, 29(2):369-376.
37. Bhat S., et al. Single molecule detection in nanofluidic digital array enables accurate measurement of DNA copy number. Anal. Bioanal. Chem. 2009, 394(2):457-467.
38. Sanders R., et al. Evaluation of digital PCR for absolute DNA quantification. Anal. Chem. 2011, 83(17):6474-6484.
39. Hindson C.M., et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods 2013, 10(10):1003-1005.
40. Costa V., et al. Uncovering the complexity of transcriptomes with RNA-Seq. J. Biomed. Biotechnol. 2010, 2010:853916.
41. Harati S., Phan J.H., Wang M.D. Investigation of factors affecting RNA-seq gene expression calls. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014, 2014:5232-5235.
42. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24(9):1151-1161.
43. Malentacchi F., et al. SPIDIA-RNA: second external quality assessment for the pre-analytical phase of blood samples used for RNA based analyses. PLoS One 2014, 9(11):e112293.
44. Pazzagli M., et al. SPIDIA-RNA: first external quality assessment for the pre-analytical phase of blood samples used for RNA based analyses. Methods 2013, 59(1):20-31.
45. Ramsden S.C., et al. EQUAL-quant: an international external quality assessment scheme for real-time PCR. Clin. Chem. 2006, 52(8):1584-1591.
46. Tichopad A., et al. Design and optimization of reverse-transcription quantitative PCR experiments. Clin. Chem. 2009, 55(10):1816-1823.
47. NICE NICE guidance recommends new test to guide breast cancer treatment decisions 2013, http://www.nice.org.uk/newsroom/pressreleases/NICERecommendsTestBreastCancerTreatmentDecisions.jsp.