Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine

Biomarkers in Medicine

Volumn 9, Issue 11, 2015, Pages 1265-1272

  Zheng, Yuanting ^a   Qing, Tao ^a   Song, Yunjie ^a   Zhu, Jinhang ^a   Yu, Ying ^b   Shi, Weiwei ^c   Pusztai, Lajos ^c   Shi, Leming ^a,b  

^a FUDAN UNIVERSITY   (China)

^b FUDAN UNIVERSITY   (China)

^c YALE CANCER CENTER   (United States)

Author keywords

biomarker; genomics; MAQC; microarray; next generation sequencing; phenomics; precision medicine; SEQC

Indexed keywords

BIOLOGICAL MARKER;

ANALYTIC METHOD; DATA ANALYSIS; GENE TECHNOLOGY; GENOMICS; MEDICAL RESEARCH; MICROARRAY ANALYSIS; NEXT GENERATION SEQUENCING; PROCESS DEVELOPMENT; QUALITY CONTROL; REPRODUCIBILITY; REVIEW; RNA TRANSCRIPTION; SEQUENCE ANALYSIS; STANDARDIZATION; DNA MICROARRAY; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLISM; PERSONALIZED MEDICINE; PROCEDURES; STANDARD; STANDARDS; TRANSLATIONAL RESEARCH;

BIOMARKERS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRECISION MEDICINE; REFERENCE STANDARDS; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84947802253     PISSN: 17520363     EISSN: 17520371     Source Type: Journal    
DOI: 10.2217/bmm.15.99     Document Type: Review

References (52)

1. Ashley EA. The precision medicine initiative: a new national effort. JAMA doi:10. 1001/jama. 2015. 3595 (2015) (Epub ahead of print).
2. Collins FS, Varmus H. A new initiative on precision medicine. N. Engl. J. Med. 372(9), 793-795 (2015).
3. The White House. The Precision Medicine Initiative. https://www. whitehouse. gov/precision-medicine
4. LaDuca H, Stuenkel AJ, Dolinsky JS et al. Utilization of multigene panels in hereditary cancer predisposition testing: analysis of more than 2,000 patients. Genet. Med. 16(11), 830-837 (2014).
5. Bellocq JP, Luporsi E, Barriere J et al. [uPA/PAI-1, Oncotype DXTM, MammaPrint (R). Prognosis and predictive values for clinical utility in breast cancer management]. Ann. Pathol. 34(5), 349-351 (2014).
6. Slodkowska EA, Ross JS. MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert. Rev. Mol. Diagn. 9(5), 417-422 (2009).
7. Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature 505(7485), 612-613 (2014).
8. Freedman LP, Gibson MC. The impact of preclinical irreproducibility on drug development. Clin. Pharmacol. Ther. 97(1), 16-18 (2015).
9. McNutt M. Reproducibility. Science 343(6168), 229 (2014).
10. Frantz S. An array of problems. Nat. Rev. Drug. Discov. 4(5), 362-363 (2005).
11. Ioannidis JP. Microarrays and molecular research: noise discovery? Lancet 365(9458), 454-455 (2005).
12. Kohlmann A, Grossmann V, Nadarajah N, Haferlach T. Next-generation sequencing-feasibility and practicality in haematology. Br. J. Haematol. 160(6), 736-753 (2013).
13. Nekrutenko A, Taylor J. Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nat. Rev. Genet. 13(9), 667-672 (2012).
14. Nature Biotechnology. www. nature. com/nbt/focus/maqc/
15. Guo L, Lobenhofer EK, Wang C et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 24(9), 1162-1169 (2006).
16. MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24(9), 1151-1161 (2006).
17. Canales RD, Luo Y, Willey JC et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24(9), 1115-1122 (2006).
18. Shippy R, Fulmer-Smentek S, Jensen RV et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 24(9), 1123-1131 (2006).
19. Tong W, Lucas AB, Shippy R et al. Evaluation of external RNA controls for the assessment of microarray performance. Nat. Biotechnol. 24(9), 1132-1139 (2006).
20. Patterson TA, Lobenhofer EK, Fulmer-Smentek SB et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat. Biotechnol. 24(9), 1140-1150 (2006).
21. nature. com. The Microarray Quality control II. www. nature. com/focus/maqc2/
22. Shi L, Campbell G, Jones WD et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28(8), 827-838 (2010).
23. Fan X, Lobenhofer EK, Chen M et al. Consistency of predictive signature genes and classifiers generated using different microarray platforms. Pharmacogenomics J. 10(4), 247-257 (2010).
24. Oberthuer A, Juraeva D, Li L et al. Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients. Pharmacogenomics J. 10(4), 258-266 (2010).
25. Huang J, Shi W, Zhang J et al. Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J. 10(4), 267-277 (2010).
26. Luo J, Schumacher M, Scherer A et al. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics J. 10(4), 278-291 (2010).
27. Parry RM, Jones W, Stokes TH et al. k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics J. 10(4), 292-309 (2010).
28. Shi W, Bessarabova M, Dosymbekov D et al. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. Pharmacogenomics J. 10(4), 310-323 (2010).
29. Miclaus K, Chierici M, Lambert C et al. Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies. Pharmacogenomics J. 10(4), 324-335 (2010).
30. Miclaus K, Wolfinger R, Vega S et al. Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array. Pharmacogenomics J. 10(4), 336-346 (2010).
31. Zhang L, Yin S, Miclaus K et al. Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease. Pharmacogenomics J. 10(4), 347-354 (2010).
32. Chierici M, Miclaus K, Vega S, Furlanello C. An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm. Pharmacogenomics J. 10(4), 355-363 (2010).
33. Hong H, Shi L, Su Z et al. Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples. Pharmacogenomics J. 10(4), 364-374 (2010).
34. nature. com. SEQC. www. nature. com/nbt/collections/seqc/
35. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32(9), 903-914 (2014).
36. Li S, Tighe SW, Nicolet CM et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF nextgeneration sequencing study. Nat. Biotechnol. 32(9), 915-925 (2014).
37. Wang C, Gong B, Bushel PR et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926-932 (2014).
38. Li S, ?abaj PP, Zumbo P et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32(9), 888-895 (2014).
39. Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32(9), 896-902 (2014).
40. Munro SA, Lund SP, Pine PS et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5(5125), 2014.
41. Yu Y, Fuscoe JC, Zhao C et al. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat. Commun. 5(3230), 2014.
42. Baker SC, Bauer SR, Beyer RP et al. The External RNA Controls Consortium: a progress report. Nat. Methods 2(10), 731-734 (2005).
43. Zook JM, Chapman B, Wang J et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32(3), 246-51 (2014).
44. BioPlanet. com. http://www. bioplanet. com/gcat
45. Gargis AS, Kalman L, Berry MW et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat. Biotechnol. 30(11), 1033-1036 (2012).
46. Bilder RM, Sabb FW, Cannon TD et al. Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 164(1), 30-42 (2009).
47. Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat. Rev. Genet. 11(12), 855-866 (2010).
48. Montague E, Stanberry L, Higdon R et al. MOPED 2. 5-an integrated multi-omics resource: multi-omics profiling expression database now includes transcriptomics data. OMICS 18(6), 335-343 (2014).
49. Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genet Med. 9(10), 665-674 (2007).
50. Vayena E, Salathe M, Madoff LC, Brownstein JS. Ethical challenges of big data in public health. PLoS Comput. Biol. 11(2), e1003904 (2015).
51. Global Alliance for Genomics and Health. Framework for Responsible Sharing of Genomic and Health-Related Data. http://genomicsandhealth. org
52. NIH. NIH issues finalized policy on genomic data sharing. http://www. nih. gov/news/health/aug2014/od-27. htm