Advertisement

Using RNA Sequencing to Characterize the Tumor Microenvironment

  • C. C. Smith
  • L. M. Bixby
  • K. L. Miller
  • S. R. Selitsky
  • D. S. Bortone
  • K. A. Hoadley
  • B. G. Vincent
  • J. S. SerodyEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)

Abstract

RNA sequencing (RNA-seq) is an integral tool in immunogenomics, allowing for interrogation of the transcriptome of a tumor and its microenvironment. Analytical methods to deconstruct the genomics data can then be applied to infer gene expression patterns associated with the presence of various immunocyte populations. High quality RNA-seq is possible from formalin-fixed, paraffin-embedded (FFPE), fresh-frozen, and fresh tissue, with a wide variety of sequencing library preparation methods, sequencing platforms, and downstream bioinformatics analyses currently available. Selection of an appropriate library preparation method is largely determined by tissue type, quality of RNA, and quantity of RNA. Downstream of sequencing, many analyses can be applied to the data, including differential gene expression analysis, immune gene signature analysis, gene pathway analysis, T/B-cell receptor inference, HLA inference, and viral transcript quantification. In this chapter, we will describe our workflow for RNA-seq from bulk tissue to evaluable data, including extraction of RNA, library preparation methods, sequencing of libraries, alignment and quality assurance of data, and initial downstream analyses of RNA-seq data to extract relevant immunogenomics features. Systems biology methods that draw additional insights by integrating these features are covered further in Chapters  28 30.

Key words

RNA-seq Immunogenomics RNA extraction Library preparation Next-generation sequencing Alignment Quantification 

References

  1. 1.
    Newman AM, Liu CL, Green MR et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457.  https://doi.org/10.1038/nmeth.3337CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Li B, Severson E, Pignon JC et al (2016) Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17.  https://doi.org/10.1186/s13059-016-1028-7
  3. 3.
    Cancer Genome Atlas Research Network (2015) Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163:506–519.  https://doi.org/10.1016/j.cell.2015.09.033CrossRefGoogle Scholar
  4. 4.
    Saito R, Smith CC, Utsumi T et al (2018) Molecular subtype-specific immunocompetent models of high-grade urothelial carcinoma reveal differential neoantigen expression and response to immunotherapy. Cancer Res 78:3954–3968.  https://doi.org/10.1158/0008-5472.CAN-18-0173CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Hellmann MD, Callahan MK, Awad MM et al (2018) Tumor mutational burden and efficacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer Cell 33:853–861.e4.  https://doi.org/10.1016/j.ccell.2018.04.001CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Smith CC et al (2018) Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma. J Clin InvestGoogle Scholar
  7. 7.
    Castle JC, Kreiter S, Diekmann J et al (2012) Exploiting the mutanome for tumor vaccination. Cancer Res 72:1081–1091.  https://doi.org/10.1158/0008-5472.CAN-11-3722CrossRefPubMedGoogle Scholar
  8. 8.
    Matsushita H, Vesely MD, Koboldt DC et al (2012) Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482:400–404.  https://doi.org/10.1038/nature10755CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Simpson AJG, Caballero OL, Jungbluth A et al (2005) Cancer/testis antigens, gametogenesis and cancer. Nat Rev Cancer 5:615–625CrossRefGoogle Scholar
  10. 10.
    Coulie PG, Van Den Eynde BJ, Van Der Bruggen P, Boon T (2014) Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat Rev Cancer 14:135–146CrossRefGoogle Scholar
  11. 11.
    Illumina (2017) bcl2fastq2 Software v2.19.1 Release NotesGoogle Scholar
  12. 12.
    Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. Available at: www.bioinformatics.babraham.ac.uk/projects/fastqc/. In: FastQC a qual. Control tool high throughput Seq. data. Available www.bioinformatics.babraham.ac.uk/projects/fastqc/
  13. 13.
    Bushnell, Brian (2014) BBMap: a fast, accurate, splice-aware aligner. Conf. 9th Annu. Genomics Energy Environ. MeetGoogle Scholar
  14. 14.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21.  https://doi.org/10.1093/bioinformatics/bts635CrossRefGoogle Scholar
  15. 15.
    Qiagen (2018) CLC assembly cell user manualGoogle Scholar
  16. 16.
    Hercus C, Albertyn Z (2012) Novoalign. Novocr TechnolGoogle Scholar
  17. 17.
    Wu TD, Reeder J, Lawrence M et al (2016) GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. In: Methods in molecular biology, pp 283–334Google Scholar
  18. 18.
    Patro R, Duggal G, Love MI et al (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14:417–419.  https://doi.org/10.1038/nmeth.4197CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Patro R, Mount SM, Kingsford C (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32:462–464.  https://doi.org/10.1038/nbt.2862CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527.  https://doi.org/10.1038/nbt.3519CrossRefGoogle Scholar
  21. 21.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079.  https://doi.org/10.1093/bioinformatics/btp352CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Broad Institute (2016) Picard tools. http://broadinstitute.github.io/picard/
  23. 23.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(550).  https://doi.org/10.1186/s13059-014-0550-8
  24. 24.
    Smyth GK (2005) Limma: linear models fro microarray data. In: Bioinformatics and computational biology solutions using R and bioconductor p 397–420Google Scholar
  25. 25.
    Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15.  https://doi.org/10.1186/gb-2014-15-2-r29CrossRefGoogle Scholar
  26. 26.
    Tarazona S, Furió-Tarí P, Turrà D et al (2015) Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res 43.  https://doi.org/10.1093/nar/gkv711
  27. 27.
    Robinson MD, McCarthy DJ, Smyth GK (2009) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140.  https://doi.org/10.1093/bioinformatics/btp616CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Mootha VK, Lindgren CM, Eriksson KF et al (2003) PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34:267–273.  https://doi.org/10.1038/ng1180CrossRefPubMedGoogle Scholar
  29. 29.
    Subramanian P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JPAT (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550.  https://doi.org/10.1073/pnas.0506580102CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Barbie DA, Tamayo P, Boehm JS et al (2009) Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108–112.  https://doi.org/10.1038/nature08460CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14.  https://doi.org/10.1186/1471-2105-14-7CrossRefGoogle Scholar
  32. 32.
    Hou JP, Ma J (2014) DawnRank: discovering personalized driver genes in cancer. Genome Med 6.  https://doi.org/10.1186/s13073-014-0056-8
  33. 33.
    Bolotin DA, Poslavsky S, Mitrophanov I et al (2015) MiXCR: software for comprehensive adaptive immunity profiling. Nat Methods 12:380–381CrossRefGoogle Scholar
  34. 34.
    Mose LE, Selitsky SR, Bixby LM et al (2016) Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V’DJer. Bioinformatics 32:3729–3734.  https://doi.org/10.1093/bioinformatics/btw526CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Bai Y, Wang D, Fury W (2018) PHLAT: inference of high-resolution HLA types from RNA and whole exome sequencing. In: Methods in molecular biology, pp 193–201Google Scholar
  36. 36.
    Buchkovich ML, Brown CC, Robasky K et al (2017) HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data. Genome Med 9.  https://doi.org/10.1186/s13073-017-0473-6
  37. 37.
    Jurtz V, Paul S, Andreatta M et al (2017) NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol. ji1700893.  https://doi.org/10.4049/jimmunol.1700893CrossRefGoogle Scholar
  38. 38.
    Andreatta M, Karosiene E, Rasmussen M et al (2015) Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics 67:641–650.  https://doi.org/10.1007/s00251-015-0873-yCrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Kim S, Kim HS, Kim E et al (2018) Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information. Ann Oncol 29:1030–1036.  https://doi.org/10.1093/annonc/mdy022CrossRefPubMedGoogle Scholar
  40. 40.
    Hundal J, Carreno BM, Petti AA et al (2016) Abstract 3995: pVAC-Seq: a genome-guided in silico approach to identify tumor neoantigens for personalized immunotherapy. Cancer Res 76:3995–3995.  https://doi.org/10.1158/1538-7445.AM2016-3995CrossRefGoogle Scholar
  41. 41.
    Zhang J, Mardis ER, Maher CA (2017) INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. Bioinformatics 33:555–557.  https://doi.org/10.1093/bioinformatics/btw674CrossRefPubMedGoogle Scholar
  42. 42.
    Selitsky SR, David M, Lisle M, Parker Joel S, Dittmer DP (2018) Epstein-Barr virus-positive cancers show altered B-cell Clonality. mSystems 3(5)Google Scholar
  43. 43.
    Ali N, Rampazzo RDCP, Costa ADT, Krieger MA (2017, 2017) Current nucleic acid extraction methods and their implications to point-of-care diagnostics. Biomed Res IntGoogle Scholar
  44. 44.
    Escobar MD, Hunt JL (2017) A cost-effective RNA extraction technique from animal cells and tissue using silica columns. J Biol Methods 4:72.  https://doi.org/10.14440/jbm.2017.184CrossRefGoogle Scholar
  45. 45.
    Chirgwin JM, Przybyla AE, MacDonald RJ, Rutter WJ (1979) Isolation of biologically active ribonucleic acid from sources enriched in ribonuclease. Biochemistry 18:5294–5299.  https://doi.org/10.1021/bi00591a005CrossRefPubMedGoogle Scholar
  46. 46.
    Farrell RE (2010) RNA methodologies: laboratory guide for isolation and characterizationCrossRefGoogle Scholar
  47. 47.
    Amini P, Ettlin J, Opitz L et al (2017) An optimised protocol for isolation of RNA from small sections of laser-capture microdissected FFPE tissue amenable for next-generation sequencing. BMC Mol Biol 18.  https://doi.org/10.1186/s12867-017-0099-7
  48. 48.
    Kresse SH, Namløs HM, Lorenz S et al (2018) Evaluation of commercial DNA and RNA extraction methods for high-throughput sequencing of FFPE samples. PLoS One 13:e0197456.  https://doi.org/10.1371/journal.pone.0197456CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Bonin S, Hlubek F, Benhattar J et al (2010) Multicentre validation study of nucleic acids extraction from FFPE tissues. Virchows Arch 457:309–317.  https://doi.org/10.1007/s00428-010-0917-5CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Patel PG, Selvarajah S, Guérard KP et al (2017) Reliability and performance of commercial RNA and DNA extraction kits for FFPE tissue cores. PLoS One 12.  https://doi.org/10.1371/journal.pone.0179732CrossRefGoogle Scholar
  51. 51.
    Patel PG, Selvarajah S, Boursalie S et al (2016) Preparation of formalin-fixed paraffin-embedded tissue cores for both RNA and DNA extraction. J Vis Exp:1–10.  https://doi.org/10.3791/54299
  52. 52.
    Nielsen H (2011) RNA methods and protocolsGoogle Scholar
  53. 53.
    Schroeder A, Mueller O, Stocker S et al (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7.  https://doi.org/10.1186/1471-2199-7-3CrossRefGoogle Scholar
  54. 54.
    Mueller O, Schroeder A (2004) RNA integrity number (RIN) – standardization of RNA quality control application. Nano 1(8).  https://doi.org/10.1101/gr.189621.115.7
  55. 55.
    Illumina (2016) Evaluating RNA quality from FFPE samplesGoogle Scholar
  56. 56.
    Baruzzo G, Hayer KE, Kim EJ et al (2017) Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods 14:135–139.  https://doi.org/10.1038/nmeth.4106CrossRefPubMedGoogle Scholar
  57. 57.
    Engström PG, Steijger T, Sipos B et al (2013) Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods 10:1185–1191.  https://doi.org/10.1038/nmeth.2722CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Wang K, Singh D, Zeng Z et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38.  https://doi.org/10.1093/nar/gkq622CrossRefGoogle Scholar
  59. 59.
    McCall MN, Murakami PN, Lukk M et al (2011) Assessing affymetrix GeneChip microarray quality. BMC Bioinformatics 12.  https://doi.org/10.1186/1471-2105-12-137CrossRefGoogle Scholar
  60. 60.
    Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12.  https://doi.org/10.1186/1471-2105-12-323
  61. 61.
    Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc Protoc 7:562–578.  https://doi.org/10.1038/nprot.2012.016CrossRefGoogle Scholar
  62. 62.
    Zhang C, Zhang B, Lin LL, Zhao S (2017) Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18.  https://doi.org/10.1186/s12864-017-4002-1
  63. 63.
    Li X, Brock GN, Rouchka EC et al (2017) A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data. PLoS One 12:e0176185.  https://doi.org/10.1371/journal.pone.0176185CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Costa-Silva Juliana AND, Domingues DANDLFM (2017) RNA-Seq differential expression analysis: an extended review and a software tool. PLoS One 12:1–18.  https://doi.org/10.1371/journal.pone.0190152CrossRefGoogle Scholar
  65. 65.
    Schurch NJ, Schofield P, Gierliński M et al (2016) How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use. RNA 22:839–851.  https://doi.org/10.1261/rna.053959.115CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Fan C, Oh DS, Wessels L et al (2006) Concordance among gene-expression– based predictors for breast cancer. N Engl J Med 355:560–569CrossRefGoogle Scholar
  67. 67.
    Palmer C, Diehn M, Alizadeh AA, Brown PO (2006) Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 7.  https://doi.org/10.1186/1471-2164-7-115CrossRefGoogle Scholar
  68. 68.
    Schmidt M, Böhm D, Von Törne C et al (2008) The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 68:5405–5413.  https://doi.org/10.1158/0008-5472.CAN-07-5206CrossRefPubMedGoogle Scholar
  69. 69.
    Beck AH, Espinosa I, Edris B et al (2009) The macrophage colony-stimulating factor 1 response signature in breast carcinoma. Clin Cancer Res 15:778–787.  https://doi.org/10.1158/1078-0432.CCR-08-1283CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Rody A, Holtrich U, Pusztai L et al (2009) T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res 11.  https://doi.org/10.1186/bcr2234
  71. 71.
    Chan KS, Espinosa I, Chao M et al (2009) Identification, molecular characterization, clinical prognosis, and therapeutic targeting of human bladder tumor-initiating cells. Proc Natl Acad Sci U S A 106:14016–14021.  https://doi.org/10.1073/pnas.0906549106CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Prat A, Parker JS, Karginova O et al (2010) Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res 12:R68.  https://doi.org/10.1186/bcr2635CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Fan C, Prat A, Parker JS et al (2011) Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Med Genet 4.  https://doi.org/10.1186/1755-8794-4-3
  74. 74.
    Rody A, Karn T, Liedtke C et al (2011) A clinically relevant gene signature in triple negative and basal-like breast cancer. Breast Cancer Res 13:R97.  https://doi.org/10.1186/bcr3035CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Bindea G, Mlecnik B, Tosolini M et al (2013) Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39:782–795.  https://doi.org/10.1016/j.immuni.2013.10.003CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Iglesia MD, Vincent BG, Parker JS et al (2014) Prognostic B-cell signatures using mRNA-seq in patients with subtype-specific breast and ovarian cancer. Clin Cancer Res 20:3818–3829.  https://doi.org/10.1158/1078-0432.CCR-13-3368CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Kardos J, Chai S, Mose LE et al (2016) Claudin-low bladder tumors are immune infiltrated and actively immune suppressed. JCI Insight 1.  https://doi.org/10.1172/jci.insight.85902
  78. 78.
    Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262.  https://doi.org/10.1016/j.celrep.2016.12.019CrossRefGoogle Scholar
  79. 79.
    Thorsson V, Gibbs DL, Brown SD et al (2018) The immune landscape of cancer. Immunity 48:812–830.e14.  https://doi.org/10.1016/j.immuni.2018.03.023CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Liberzon A, Subramanian A, Pinchback R et al (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics 27:1739–1740.  https://doi.org/10.1093/bioinformatics/btr260CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Tomfohr J, Lu J, Kepler TB (2005) Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6.  https://doi.org/10.1186/1471-2105-6-225CrossRefGoogle Scholar
  82. 82.
    Hulsegge I, Kommadath A, Smits MA (2009) Globaltest and GOEAST: two different approaches for gene ontology analysis. BMC Proc 3:S10.  https://doi.org/10.1186/1753-6561-3-s4-s10CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Tarca AL, Draghici S, Bhatti G, Romero R (2012) Down-weighting overlapping genes improves gene set analysis. BMC Bioinformatics 13(136).  https://doi.org/10.1186/1471-2105-13-136
  84. 84.
    Tarca AL, Bhatti G, Romero R (2013) A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PLoS One 8.  https://doi.org/10.1371/journal.pone.0079217CrossRefGoogle Scholar
  85. 85.
    Bolotin DA, Shugay M, Mamedov IZ et al (2013) MiTCR: software for T-cell receptor sequencing data analysis. Nat Methods 10:813–814CrossRefGoogle Scholar
  86. 86.
    Li B, Li T, Wang B et al (2017) Ultrasensitive detection of TCR hypervariable-region sequences in solid-tissue RNA-seq data. Nat Genet 49:483–484CrossRefGoogle Scholar
  87. 87.
    Bolotin DA, Poslavsky S, Davydov AN et al (2017) Antigen receptor repertoire profiling from RNA-seq data. Nat Biotechnol 35:908–911.  https://doi.org/10.1038/nbt.3979CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Weimer ET, Montgomery M, Petraroia R et al (2016) Performance characteristics and validation of next-generation sequencing for human leucocyte antigen typing. J Mol Diagnostics 18.  https://doi.org/10.1016/j.jmoldx.2016.03.009CrossRefGoogle Scholar
  89. 89.
    Nariai N, Kojima K, Saito S et al (2015) HLA-VBSeq: accurate HLA typing at full resolution from whole-genome sequencing data. BMC Genomics 16.  https://doi.org/10.1186/1471-2164-16-S2-S7
  90. 90.
    Major E, Rigó K, Hague T et al (2013) HLA typing from 1000 genomes whole genome and whole exome illumina data. PLoS One 8.  https://doi.org/10.1371/journal.pone.0078410CrossRefGoogle Scholar
  91. 91.
    Greytak SR, Engel KB, Zmuda E, Casas-Silva E, Guan P, Hoadley KA, Mungall AJ, Wheeler DA, Doddapaneni HV, Moore H (2018) National cancer institute biospecimen evidence-based practices: harmonizing procedures for nucleic acid extraction from formalin-fixed, paraffin-embedded tissue. Biopreserv Biobank 16:247–250.  https://doi.org/10.1089/bio.2018.0046CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Zhao W, He X, Hoadley KA et al (2014) Comparison of RNA-Seq by poly (a) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics 15.  https://doi.org/10.1186/1471-2164-15-419CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • C. C. Smith
    • 1
    • 2
  • L. M. Bixby
    • 2
  • K. L. Miller
    • 2
  • S. R. Selitsky
    • 3
  • D. S. Bortone
    • 3
  • K. A. Hoadley
    • 2
    • 4
  • B. G. Vincent
    • 1
    • 3
    • 5
    • 6
  • J. S. Serody
    • 1
    • 2
    • 5
    Email author
  1. 1.Department of Microbiology and ImmunologyUNC School of MedicineChapel HillUSA
  2. 2.Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillUSA
  5. 5.Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  6. 6.Curriculum in Bioinformatics and Computational BiologyUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations