Translating RNA sequencing into clinical diagnostics: Opportunities and challenges

Nature Reviews Genetics

Volumn 17, Issue 5, 2016, Pages 257-271

  Byron, Sara A ^a   Van Keuren Jensen, Kendall R ^a   Engelthaler, David M ^a   Carpten, John D ^a   Craig, David W ^a  

^a TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LONG UNTRANSLATED RNA; MESSENGER RNA; MICRORNA; PIWI INTERACTING RNA; RNA; SMALL NUCLEAR RNA; SMALL NUCLEOLAR RNA; TRANSFER RNA; UNTRANSLATED RNA;

ALLELE; ALTERNATIVE RNA SPLICING; CLINICAL PRACTICE; DNA SEQUENCE; GENE EXPRESSION; GENE FUSION; HUMAN; INFECTION; NEXT GENERATION SEQUENCING; PRIORITY JOURNAL; REVIEW; RNA SEQUENCE; RNA TRANSLATION; STANDARD; X CHROMOSOME INACTIVATION; BIOLOGY; DIAGNOSTIC TEST; DISEASES; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; PROCEDURES; SEQUENCE ANALYSIS;

COMPUTATIONAL BIOLOGY; DIAGNOSTIC TESTS, ROUTINE; DISEASE; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; RNA; SEQUENCE ANALYSIS, RNA;

EID: 84961262989     PISSN: 14710056     EISSN: 14710064     Source Type: Journal    
DOI: 10.1038/nrg.2016.10     Document Type: Review

References (161)

1. Mortazavi A, Williams B. A, McCue K, Schaeffer L, & Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods. 5, 621-628 (2008).
2. Doebele R. C, et al. An oncogenic NTRK fusion in a soft tissue sarcoma patient with response to the tropomyosin-related kinase (TRK) inhibitor LOXO 101. Cancer Discov. 5, 1049-1057 (2015).
3. Sonu R. J, Jonas B. A, Dwyre D. M, Gregg J. P, & Rashidi H. H. Optimal molecular methods in detecting p190 (BCR-ABL) fusion variants in hematologic malignancies: a case report and review of the literature. Case Rep. Hematol. 2015, 458052 (2015).
4. Cech T. R, & Steitz J. A. The noncoding RNA revolution-Trashing old rules to forge new ones. Cell. 157, 77-94 (2014).
5. Wang Z, Gerstein M, & Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63 (2009).
6. Wilhelm B. T, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 453, 1239-1243 (2008).
7. Velculescu V. E, Zhang L, Vogelstein B, & Kinzler K. W. Serial analysis of gene expression. Science. 270, 484-487 (1995).
8. Marioni J. C, Mason C. E, Mane S. M, Stephens M, & Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509-1517 (2008).
9. Sultan M, et al A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 321, 956-960 (2008).
10. Senkus E, et al. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 26, v8-v30 (2015).
11. Coates A. S, et al. Tailoring therapies-improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann. Oncol. 26, 1533-1546 (2015).
12. Sparano J. A, et al. Prospective validation of a 21 gene expression assay in breast cancer. N. Engl. J. Med. 373, 2005-2014 (2015).
13. Fumagalli D, et al. Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology. BMC Genomics. 15, 1008 (2014).
14. Zhang W, et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 16, 133 (2015).
15. Deng M. C, et al. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am. J. Transplant. 6, 150-160 (2006).
16. Starling R. C, et al. Molecular testing in the management of cardiac transplant recipients: initial clinical experience. J. Heart Lung Transplant. 25, 1389-1395 (2006).
17. Van Allen E. M, et al. Genomic correlates of response to CTLA 4 blockade in metastatic melanoma. Science. 350, 207-211 (2015).
18. Vardiman J. W, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 114, 937-951 (2009).
19. Font-Tello A, et al. Association of ERG and TMPRSS2 ERG with grade, stage, and prognosis of prostate cancer is dependent on their expression levels. Prostate. 75, 1216-1226 (2015).
20. Druker B. J, et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031-1037 (2001).
21. OBrien S, et al. Chronic Myelogenous Leukemia, version 1.2014. J. Natl Compr. Canc. Netw. 11, 1327-1340 (2013).
22. Cavelier L, et al. Clonal distribution of BCR ABL1 mutations and splice isoforms by single-molecule long-read RNA sequencing. BMC Cancer. 15, 45 (2015).
23. Leighl N. B, et al. Molecular testing for selection of patients with lung cancer for epidermal growth factor receptor and anaplastic lymphoma kinase tyrosine kinase inhibitors: American Society of Clinical Oncology endorsement of the College of American Pathologists/International Association for the study of lung cancer/association for molecular pathology guideline. J. Clin. Oncol. 32, 3673-3679 (2014).
24. Wynes M. W, et al. An international interpretation study using the ALK IHC antibody D5F3 and a sensitive detection kit demonstrates high concordance between ALK IHC and ALK FISH and between evaluators. J. Thorac. Oncol. 9, 631-638 (2014).
25. Lindeman N. I, et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. Arch. Pathol. Lab. Med. 137, 828-860 (2013).
26. Wang Y, Wu N, Liu J, Wu Z, & Dong D. FusionCancer: a database of cancer fusion genes derived from RNA-seq data. Diagn. Pathol. 10, 131 (2015).
27. Magri F, et al. Clinical and molecular characterization of a cohort of patients with novel nucleotide alterations of the Dystrophin gene detected by direct sequencing. BMC Med. Genet. 12, 37 (2011).
28. Liu F, & Gong C. X. Tau exon 10 alternative splicing and tauopathies. Mol. Neurodegener. 3, 8 (2008).
29. La Cognata V, DAgata V, Cavalcanti F, & Cavallaro S. Splicing: is there an alternative contribution to Parkinsons disease?. Neurogenetics. 16, 245-263 (2015).
30. Chen J, & Weiss W. A. Alternative splicing in cancer: implications for biology and therapy. Oncogene. 34, 1-14 (2015).
31. Dehm S. M, Schmidt L. J, Heemers H. V, Vessella R. L, & Tindall D. J. Splicing of a novel androgen receptor exon generates a constitutively active androgen receptor that mediates prostate cancer therapy resistance. Cancer Res. 68, 5469-5477 (2008).
32. Antonarakis E. S, et al. AR V7 and resistance to enzalutamide and abiraterone in prostate cancer. N. Engl. J. Med. 371, 1028-1038 (2014).
33. Sugawa N, Ekstrand A. J, James C. D, & Collins V. P. Identical splicing of aberrant epidermal growth factor receptor transcripts from amplified rearranged genes in human glioblastomas. Proc. Natl Acad. Sci. USA. 87, 8602-8606 (1990).
34. Reardon D. A, et al. 107 ReACT: overall survival from a randomized Phase II study of rindopepimut (CDX 110) plus Bevacizumab in relapsed glioblastoma. Neurosurgery 62 (c), 198-199 (2015).
35. Gambino G, Tancredi M, Falaschi E, Aretini P, & Caligo M. A. Characterization of three alternative transcripts of the BRCA1 gene in patients with breast cancer and a family history of breast and/or ovarian cancer who tested negative for pathogenic mutations. Int. J. Mol. Med. 35, 950-956 (2015).
36. Massah S, Beischlag T. V, & Prefontaine G. G. Epigenetic events regulating monoallelic gene expression. Crit. Rev. Biochem. Mol. Biol. 50, 337-358 (2015).
37. Valle L, et al. Germline allele-specific expression of TGFBR1 confers an increased risk of colorectal cancer. Science. 321, 1361-1365 (2008).
38. de la Chapelle A. Genetic predisposition to human disease: allele-specific expression and low-penetrance regulatory loci. Oncogene. 28, 3345-3348 (2009).
39. Lossie A. C, et al. Distinct phenotypes distinguish the molecular classes of Angelman syndrome. J. Med. Genet. 38, 834-845 (2001).
40. Li G, et al. Identification of allele-specific alternative mRNA processing via transcriptome sequencing. Nucleic Acids Res. 40, e104 (2012).
41. Klimpe S, et al. Evaluating the effect of spastin splice mutations by quantitative allele-specific expression assay. Eur. J. Neurol. 18, 99-105 (2011).
42. Wood D. L. A, et al. Recommendations for accurate resolution of gene and isoform allele-specific expression in RNA-seq data. PLoS ONE. 10, e0126911-e0126927 (2015).
43. Rivas M. A, et al. Human genomics. Effect of predicted protein-Truncating genetic variants on the human transcriptome. Science. 348, 666-669 (2015).
44. Larson N. B, et al. Comprehensively evaluating cis-regulatory variation in the human prostate transcriptome by using gene-level allele-specific expression. Am. J. Hum. Genet. 96, 869-882 (2015).
45. Pickrell J. K, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 464, 768-772 (2010).
46. Szelinger S, et al. Characterization of X chromosome inactivation using integrated analysis of whole-exome and mRNA sequencing. PLoS ONE. 9, e113036 (2014).
47. Degner J. F, et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 25, 3207-3212 (2009).
48. Castel S. E, Levy-Moonshine A, Mohammadi P, Banks E, & Lappalainen T. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16, 195 (2015).
49. Valadi H, et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 9, 654-659 (2007).
50. Arroyo J. D, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc. Natl Acad. Sci. USA. 108, 5003-5008 (2011).
51. Michael A, et al. Exosomes from human saliva as a source of microRNA biomarkers. Oral Dis. 16, 34-38 (2010).
52. Skog J, et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 10, 1470-1476 (2008).
53. Koh W, et al. Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. Proc. Natl Acad. Sci. USA. 111, 7361-7366 (2014).
54. Goetzl E. J, et al. Altered lysosomal proteins in neural-derived plasma exosomes in preclinical Alzheimer disease. Neurology. 85, 40-47 (2015).
55. Shi M, et al. Plasma exosomal a-synuclein is likely CNS-derived and increased in Parkinsons disease. Acta Neuropathol. 128, 639-650 (2014).
56. Brock G, Castellanos-Rizaldos E, Hu L, Coticchia C, & Skog J. Liquid biopsy for cancer screening, patient stratification and monitoring. Translat. Cancer Res. 4, 280-290 (2015).
57. Tsui N. B, et al. Maternal plasma RNA sequencing for genome-wide transcriptomic profiling and identification of pregnancy-Associated transcripts. Clin. Chem. 60, 954-962 (2014).
58. Ainsztein A. M, et al. The NIH Extracellular RNA Communication Consortium. J. Extracell. Vesicles. 4, 27493 (2015).
59. Quinn J. F, et al. Extracellular RNAs: development as biomarkers of human disease. J. Extracell. Vesicles. 4, 27495 (2015).
60. Laurent L. C, et al. Meeting report: discussions and preliminary findings on extracellular RNA measurement methods from laboratories in the NIH Extracellular RNA Communication Consortium. J. Extracell. Vesicles. 4, 26533 (2015).
61. Mestdagh P, et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat. Methods. 11, 809-815 (2014).
62. Kelly H, et al. Cross Platform standardisation of an experimental pipeline for use in the identification of dysregulated human circulating miRNAs. PLoS ONE. 10, e0137389 (2015).
63. Kozomara A, & Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68-73 (2014).
64. Chan P. P, & Lowe T. M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93-D97 (2009).
65. Salzman J, Gawad C, Wang P. L, Lacayo N, & Brown P. O. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE. 7, e30733 (2012).
66. Memczak S, Papavasileiou P, Peters O, & Rajewsky N. Identification and characterization of circular RNAs as a new class of putative biomarkers in human blood. PLoS ONE. 10, e0141214 (2015).
67. Goodarzi H, et al. Endogenous tRNA-derived fragments suppress breast cancer progression via YBX1 displacement. Cell. 161, 790-802 (2015).
68. Sutter D. E, et al. Performance of five FDA-Approved rapid antigen tests in the detection of 2009 H1N1 influenza A virus. J. Med. Virol. 84, 1699-1702 (2012).
69. Santiago G. A, et al. Analytical and clinical performance of the CDC real time RT PCR assay for detection and typing of dengue virus. PLoS Negl. Trop. Dis. 7, e2311 (2013).
70. Styer L. M, Miller T. T, & Parker M. M. Validation and clinical use of a sensitive HIV 2 viral load assay that uses a whole virus internal control. J. Clin. Virol. 58 (Suppl. 1), e127-e133 (2013).
71. Pinsky B. A, et al. Analytical performance characteristics of the Cepheid GeneXpert Ebola Assay for the detection of Ebola virus. PLoS ONE. 10, e0142216 (2015).
72. Fischer N, et al. Evaluation of unbiased next-generation sequencing of RNA (RNA-seq) as a diagnostic method in influenza virus-positive respiratory samples. J. Clin. Microbiol. 53, 2238-2250 (2015
73. Gire S. K, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 345, 1369-1372 (2014).
74. Gregori J, et al. Ultra-deep pyrosequencing (UDPS) data treatment to study amplicon HCV minor variants. PLoS ONE. 8, e83361 (2013).
75. Ekici H, et al. Cost-efficient HIV 1 drug resistance surveillance using multiplexed high-Throughput amplicon sequencing: implications for use in low- And middle-income countries. J. Antimicrob. Chemother. 69, 3349-3355 (2014).
76. Wang K, et al. The complex exogenous RNA spectra in human plasma: an interface with human gut biota?. PLoS ONE. 7, e51009 (2012).
77. Beatty M, et al. Small RNAs from plants, bacteria and fungi within the order Hypocreales are ubiquitous in human plasma. BMC Genomics. 15, 933 (2014).
78. Ghosal A, et al. The extracellular RNA complement of Escherichia coli. Microbiologyopen http://dx.doi.org/.10.1002/mbo3.235 (2015).
79. Chen S. J, et al. Characterization of Epstein-Barr virus miRNAome in nasopharyngeal carcinoma by deep sequencing. PLoS ONE. 5, e12745 (2010).
80. Meshesha M. K, et al. The microRNA transcriptome of human cytomegalovirus (HCMV). Open Virol. J. 6, 38-48 (2012).
81. Cucher M, et al. High-Throughput characterization of Echinococcus spp. metacestode miRNomes. Int. J. Parasitol. 45, 253-267 (2015).
82. Fagnocchi L, et al. Global transcriptome analysis reveals small RNAs affecting Neisseria meningitidis bacteremia. PLoS ONE. 10, e0126325 (2015).
83. Latorre I, et al A novel whole-blood miRNA signature for a rapid diagnosis of pulmonary tuberculosis. Eur. Respir. J. 45, 1173-1176 (2015).
84. Ren N, et al. MicroRNA signatures from multidrugresistant Mycobacterium tuberculosis. Mol. Med. Rep. 12, 6561-6567 (2015).
85. Lie A. K, & Kristensen G. Human papillomavirus E6/E7 mRNA testing as a predictive marker for cervical carcinoma. Expert Rev. Mol. Diagn. 8, 405-415 (2008).
86. Kanwal F, Kramer J. R, Ilyas J, Duan Z, & El Serag H. B. HCV genotype 3 is associated with an increased risk of cirrhosis and hepatocellular cancer in a national sample of U. S. Veterans with HCV. Hepatol. 60, 98-105 (2014).
87. Mitchell A. M, et al. Transmitted/founder hepatitis C viruses induce cell-Type- And genotype-specific differences in innate signaling within the liver. MBio. 6, e02510 (2015).
88. Wu L. S, et al. Systematic expression profiling analysis identifies specific microRNA-gene interactions that may differentiate between active and latent tuberculosis infection. Biomed. Res. Int. 2014, 895179 (2014).
89. Barry S. E, et al. Identification of miR 93 as a suitable miR for normalizing miRNA in plasma of tuberculosis patients. J. Cell. Mol. Med. 19, 1606-1613 (2015).
90. Haddow J. E, & Palomaki G. E. in Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease. (eds Khoury M. J, Little J, & Burke W.) 217-233 (Oxford Univ. Press, 2004).
91. Hoen P. A, et al. Reproducibility of high-Throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015-1022 (2013).
92. Zhao W, et al. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics. 15, 419 (2014).
93. Mercer T. R, et al. Targeted sequencing for gene discovery and quantification using RNA CaptureSeq. Nat. Protoc. 9, 989-1009 (2014).
94. Cabanski C. R, et al. cDNA hybrid capture improves transcriptome analysis on low-input and archived samples. J. Mol. Diagn. 16, 440-451 (2014).
95. Cieslik M, et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. 25, 1372-1381 (2015).
96. Zhang Z. H, et al A Comparative study of techniques for differential expression analysis on RNA-seq data. PLoS ONE. 9, e103207-e103211 (2014).
97. Munro S. A, et al. Assessing technical performance in differential gene expression experiments with external spike in RNA control ratio mixtures. Nat. Commun. 5, 5125 (2014).
98. Jiang L, et al. Synthetic spike in standards for RNA-seq experiments. Genome Res. 21, 1543-1551 (2011).
99. Tembe W. D, et al. Open-Access synthetic spike in mRNA-seq data for cancer gene fusions. BMC Genomics. 15, 1-9 (2014).
100. Su Z, et al A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903-914 (2014).
101. Li S, et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915-925 (2014).
102. Wang C, et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32, 926-932 (2014).
103. Li S, et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888-895 (2014).
104. Risso D, Ngai J, Speed T. P, & Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896-902 (2014).
105. Garber M, Grabherr M. G, Guttman M, & Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods. 8, 469-477 (2011).
106. Genomes Project C, et al A map of human genome variation from population-scale sequencing. Nature. 467, 1061-1073 (2010).
107. Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 25, 2078-2079 (2009).
108. Handsaker R. E, Korn J. M, Nemesh J, & McCarroll S. A. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat. Genet. 43, 269-276 (2011).
109. Rehm H. L, et al. ACMG clinical laboratory standards for next-generation sequencing. Genet. Med. 15, 733-747 (2013).
110. Wang Y, Lu J, Yu J, Gibbs R. A, & Yu F. An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data. Genome Res. 23, 833-842 (2013).
111. DePristo M. A, et al A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491-498 (2011).
112. Zook J. M, et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246-251 (2014).
113. Di Tommaso P, et al. The impact of Docker containers on the performance of genomic pipelines. PeerJ. 3, e1273 (2015).
114. Kalari K. R, et al. MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing. BMC Bioinformatics. 15, 224 (2014).
115. Nasser S, et al. An integrated framework for reporting clinically relevant biomarkers from paired tumor/.normal genomic and transcriptomic sequencing data in support of clinical trials in personalized medicine. Pac. Symp. Biocomput. 56-67 (2015).
116. Alexander E. K, et al. Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. N. Engl. J. Med. 367, 705-715 (2012).
117. Alexander E. K, et al. Multicenter clinical experience with the Afirma gene expression classifier. J. Clin. Endocrinol. Metab. 99, 119-125 (2014).
118. US Department of Health & Human Services. Center for Devices and Radiological Health. FDA notification and medical device reporting for laboratory developed tests (LDTs) - draft guidance. [online] http://www.fda.gov/downloads/MedicalDevices/.DeviceRegulationandGuidance/GuidanceDocuments/.UCM416684.pdf (2014).
119. US Department of Health & Human Services. Center for Devices and Radiological Health. Framework for regulatory oversight of laboratory developed tests (LDTs) - draft guidance. [online] http://www.fda.gov/downloads/medicaldevices/.deviceregulationandguidance/guidancedocuments/.ucm416685.pdf (2014).
120. US Department of Health & Human Services. Optimizing FDA s regulatory oversight of next generation sequencing diagnostic tests - preliminary discussion paper. [online] http://www.fda.gov/.downloads/medicaldevices/newsevents/.workshopsconferences/ucm427869.pdf (2014).
121. Evans B. J, Burke W, & Jarvik G. P. The FDA and genomic tests-getting regulation right. N. Engl. J. Med. 372, 2258-2264 (2015).
122. Tazawa Y. Perspective for the development of companion diagnostics and regulatory landscape to encourage personalized medicine in Japan. Breast Cancer 23, 19-23 (2015).
123. Pignatti F, et al. Cancer drug development and the evolving regulatory framework for companion diagnostics in the European union. Clin. Cancer Res. 20, 1458-1468 (2014).
124. Matthijs G, et al. Guidelines for diagnostic next-generation sequencing. Eur. J. Hum. Genet. 24, 2-5 (2016).
125. European Commission. Proposal for a regulation of the European Parliament and of the council on in vitro diagnostic medical devices. [online] http://ec.europa.eu/.health/medical-devices/files/revision-docs/.proposal-2012-541-en.pdf (2012).
126. Rashid N. U, et al. Differential and limited expression of mutant alleles in multiple myeloma. Blood. 124, 3110-3117 (2014).
127. Andersson A. K, et al. The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias. Nat. Genet. 47, 330-337 (2015).
128. Chandrasekharappa S. C, et al. Massively parallel sequencing, aCGH, and RNA-Seq technologies provide a comprehensive molecular diagnosis of Fanconi anemia. Blood. 121, e138-e148 (2013).
129. De Vlaminck I, et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl Med. 6, 241ra77 (2014).
130. De Vlaminck I, et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl Acad. Sci. USA. 112, 13336-133341 (2015).
131. Heid C. A, Stevens J, Livak K. J, & Williams P. M. Real time quantitative PCR. Genome Res. 6, 986-994 (1996).
132. Wang W, et al. Design of multiplexed detection assays for identification of avian influenza a virus subtypes pathogenic to humans by SmartCycler real-Time reverse transcription-PCR. J. Clin. Microbiol. 47, 86-92 (2009).
133. Lockhart D. J, et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675-1680 (1996).
134. Mook S, Vant Veer L. J, Rutgers E. J, Piccart- Gebhart M. J, & Cardoso F. Individualization of therapy using Mammaprint: from development to the MINDACT Trial. Cancer Genom. Proteom. 4, 147-155 (2007).
135. Paik S, et al A multigene assay to predict recurrence of tamoxifen-Treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817-2826 (2004).
136. Cooperberg M. R, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J. Clin. Oncol. 31, 1428-1434 (2013).
137. Learn C. A, et al. Resistance to tyrosine kinase inhibition by mutant epidermal growth factor receptor variant III contributes to the neoplastic phenotype of glioblastoma multiforme. Clin. Cancer Res. 10, 3216-3224 (2004).
138. Robinson D, et al. Integrative clinical genomics of advanced prostate cancer. Cell. 161, 1215-1228 (2015).
139. Mody R. J, et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. JAMA. 314, 913-925 (2015).
140. Sekulic A, et al. Personalized treatment of Sezary syndrome by targeting a novel CTLA4: CD28 fusion. Mol. Genet. Genom. Med. 3, 130-136 (2015).
141. Craig D. W, et al. Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities. Mol. Cancer Ther. 12, 104-116 (2013).
142. Borad M. J, et al. Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma. PLoS Genet. 10, e1004135 (2014).
143. Greco F. A, Lennington W. J, Spigel D. R, & Hainsworth J. D. Molecular profiling diagnosis in unknown primary cancer: accuracy and ability to complement standard pathology. J. Natl Cancer Inst. 105, 782-790 (2013).
144. Paik S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 3726-3734 (2006).
145. Albain K. S, et al. Prognostic and predictive value of the 21 gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 11, 55-65 (2010).
146. Knezevic D, et al. Analytical validation of the Oncotype DX prostate cancer assay - A clinical RT PCR assay optimized for prostate needle biopsies. BMC Genomics. 14, 690 (2013).
147. Clark-Langone K. M, Sangli C, Krishnakumar J, & Watson D. Translating tumor biology into personalized treatment planning: analytical performance characteristics of the OncotypeDX Colon Cancer Assay. BMC Cancer. 10, 691 (2010).
148. Zhang Y, et al. Breast cancer index identifies early-stage estrogen receptor-positive breast cancer patients at risk for early- And late-distant recurrence. Clin. Cancer Res. 19, 4196-4205 (2013).
149. Wallden B, et al. Development and verification of the PAM50 based Prosigna breast cancer gene signature assay. BMC Med. Genom. 8, 54 (2015).
150. Salazar R, et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29, 17-24 (2011).
151. Erho N, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE. 8, e66855 (2013).
152. Meiri E, et al A second-generation microRNA-based assay for diagnosing tumor tissue origin. Oncologist. 17, 801-812 (2012).
153. Taubert H, et al. Expression of the stem cell self-renewal gene Hiwi and risk of tumour-related death in patients with soft-Tissue sarcoma. Oncogene. 26, 1098-1100 (2007).
154. Baraniskin A, et al. Circulating U2 small nuclear RNA fragments as a novel diagnostic biomarker for pancreatic and colorectal adenocarcinoma. Int. J. Cancer. 132, E48-E57 (2013).
155. Su J, et al. Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis. Oncotarget http://dx.doi.org/10.18632/oncotarget.4219 (2015).
156. Du Z, et al. Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer. Nat. Struct. Mol. Biol. 20, 908-913 (2013).
157. Li P, et al. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin. Chim. Acta. 444, 132-136 (2015).
158. Guo Y, et al. Transfer RNA detection by small RNA deep sequencing and disease association with myelodysplastic syndromes. BMC Genomics. 16, 727 (2015).
159. Westermann A. J, et al. Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions. Nature. 529, 496-501 (2016).
160. Patton J. G, et al. Biogenesis, delivery, and function of extracellular RNA. J. Extracell. Vesicles. 4, 27494 (2015).
161. Brinkmann K, et al. Exosomal RNA-based liquid biopsy detection of EML4-ALK in plasma from NSCLC patients. 16th World Conference on Lung Cancer Abstract 2591 (IASLC, 2015).