RNA sequencing (RNA-seq) has the potential to bridge tumour genotypes (for example, mutations) and their phenotypic consequences (for example, cancer molecular subtypes).
The field of transcriptomics has matured thanks to lockstep developments in experimental protocols, algorithms and databases.
Methodological and algorithmic advances continue to enable clinical applications of transcriptome profiling.
Detection of gene fusions is the most immediate application of RNA-seq.
Gene expression signatures have demonstrated prognostic and predictive value.
Transcriptome profiling will be essential for immuno-oncology.
Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.
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Velculescu, V. E. et al. Characterization of the yeast transcriptome. Cell 88, 243–251 (1997).
Carninci, P. et al. The transcriptional landscape of the mammalian genome. Science 309, 1559–1563 (2005). This is the first study to show the transcriptional complexity of a mammalian genome.
Frye, M., Jaffrey, S. R., Pan, T., Rechavi, G. & Suzuki, T. RNA modifications: what have we learned and where are we headed? Nat. Rev. Genet. 17, 365–372 (2016).
Johnson, J. M. et al. Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302, 2141–2144 (2003).
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Shoemaker, D. D. et al. Experimental annotation of the human genome using microarray technology. Nature 409, 922–927 (2001).
Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).
Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016). This is an excellent and complementary Review on the clinical applications of RNA-seq.
Chang, J. C. et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362, 362–369 (2003). This study demonstrates the feasibility of predicting the therapeutic response from microarray data obtained from breast cancer biopsy samples.
Staunton, J. E. et al. Chemosensitivity prediction by transcriptional profiling. Proc. Natl Acad. Sci. USA 98, 10787–10792 (2001). This study demonstrates the feasibility of chemosensitivity prediction from microarray data obtained from cell lines.
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
Dudley, J. T., Tibshirani, R., Deshpande, T. & Butte, A. J. Disease signatures are robust across tissues and experiments. Mol. Syst. Biol. 5, 307 (2009).
Ma'ayan, A. Colliding dynamical complex network models: biological attractors versus attractors from material physics. Biophys. J. 103, 1816–1817 (2012).
Costello, J. C. et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202–1212 (2014).
Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Gerstein, M. & Jansen, R. The current excitement in bioinformatics-analysis of whole-genome expression data: how does it relate to protein structure and function? Curr. Opin. Struct. Biol. 10, 574–584 (2000).
Goya, R. et al. SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics 26, 730–736 (2010).
Maher, C. A. et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc. Natl Acad. Sci. USA 106, 12353–12358 (2009).
van Dijk, E. L., Auger, H., Jaszczyszyn, Y. & Thermes, C. Ten years of next-generation sequencing technology. Trends Genet. 30, 418–426 (2014).
Kapranov, P. et al. RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488 (2007).
Lu, C. et al. Elucidation of the small RNA component of the transcriptome. Science 309, 1567–1569 (2005).
Gall, J. G. & Pardue, M. L. Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc. Natl Acad. Sci. USA 63, 378–383 (1969).
Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).
Alwine, J. C., Kemp, D. J. & Stark, G. R. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc. Natl Acad. Sci. USA 74, 5350–5354 (1977).
Bell, G. I. et al. Nucleotide sequence of a cDNA clone encoding human preproinsulin. Nature 282, 525–527 (1979).
Nakanishi, S. et al. Nucleotide sequence of cloned cDNA for bovine corticotropin-β-lipotropin precursor. Nature 278, 423–427 (1979).
Fiddes, J. C. & Goodman, H. M. Isolation, cloning and sequence analysis of the cDNA for the alpha-subunit of human chorionic gonadotropin. Nature 281, 351–356 (1979).
Okubo, K. et al. Large scale cDNA sequencing for analysis of quantitative and qualitative aspects of gene expression. Nat. Genet. 2, 173–179 (1992).
Chiang, P. W. et al. Use of a fluorescent-PCR reaction to detect genomic sequence copy number and transcriptional abundance. Genome Res. 6, 1013–1026 (1996).
Gibson, U. E., Heid, C. A. & Williams, P. M. A novel method for real time quantitative RT-PCR. Genome Res. 6, 995–1001 (1996).
Heid, C. A., Stevens, J., Livak, K. J. & Williams, P. M. Real time quantitative PCR. Genome Res. 6, 986–994 (1996).
Higuchi, R., Fockler, C., Dollinger, G. & Watson, R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology 11, 1026–1030 (1993).
Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).
Lockhart, D. J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680 (1996).
Sutcliffe, J. G., Milner, R. J., Bloom, F. E. & Lerner, R. A. Common 82-nucleotide sequence unique to brain RNA. Proc. Natl Acad. Sci. USA 79, 4942–4946 (1982).
Velculescu, V. E., Zhang, L., Vogelstein, B. & Kinzler, K. W. Serial analysis of gene expression. Science 270, 484–487 (1995).
Hanriot, L. et al. A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome. BMC Genomics 9, 418 (2008).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Carninci, P. et al. High-efficiency full-length cDNA cloning by biotinylated CAP trapper. Genomics 37, 327–336 (1996).
Dias Neto, E. et al. Shotgun sequencing of the human transcriptome with ORF expressed sequence tags. Proc. Natl Acad. Sci. USA 97, 3491–3496 (2000).
de Souza, S. J. et al. Identification of human chromosome 22 transcribed sequences with ORF expressed sequence tags. Proc. Natl Acad. Sci. USA 97, 12690–12693 (2000).
Brenner, S. et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat. Biotechnol. 18, 630–634 (2000).
Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).
Bainbridge, M. N. et al. Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genomics 7, 246 (2006).
Nielsen, K. L., Høgh, A. L. & Emmersen, J. DeepSAGE — digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples. Nucleic Acids Res. 34, e133 (2006).
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).
Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).
Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012).
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
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).
Cabanski, C. R. et al. cDNA hybrid capture improves transcriptome analysis on low-input and archived samples. J. Mol. Diagn. 16, 440–451 (2014).
Mercer, T. R. et al. Targeted sequencing for gene discovery and quantification using RNA CaptureSeq. Nat. Protoc. 9, 989–1009 (2014).
Git, A. et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 16, 991–1006 (2010).
Yamamoto, T., Jay, G. & Pastan, I. Unusual features in the nucleotide sequence of a cDNA clone derived from the common region of avian sarcoma virus messenger RNA. Proc. Natl Acad. Sci. USA 77, 176–180 (1980).
Zhang, L. et al. Gene expression profiles in normal and cancer cells. Science 276, 1268–1272 (1997).
Brentani, H. et al. The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags. Proc. Natl Acad. Sci. USA 100, 13418–13423 (2003).
DeRisi, J. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14, 457–460 (1996).
Alon, U. et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA 96, 6745–6750 (1999).
Consortium, T. E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015).
Hamm, G. H. & Cameron, G. N. The EMBL data library. Nucleic Acids Res. 14, 5–9 (1986).
Burks, C. et al. The GenBank nucleic acid sequence database. Comput. Appl. Biosci. 1, 225–233 (1985).
Boguski, M. S., Lowe, T. M. J. & Tolstoshev, C. M. dbEST — database for 'expressed sequence tags'. Nat. Genet. 4, 332–333 (1993).
Lal, A. et al. A public database for gene expression in human cancers. Cancer Res. 59, 5403–5407 (1999).
Smith, T. F. & Waterman, M. S. Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981).
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Pearson, W. R. & Lipman, D. J. Improved tools for biological sequence comparison. Proc. Natl Acad. Sci. USA 85, 2444–2448 (1988).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Hubbard, T. et al. The Ensembl genome database project. Nucleic Acids Res. 30, 38–41 (2002).
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI Reference Sequence (Refseq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).
Iyer, M. K. et al. The landscape of long noncoding RNAs in the human transcriptome. Nat. Genet. 47, 199–208 (2015).
Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).
Brazma, A. et al. ArrayExpress — a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).
Chen, Y., Dougherty, E. R. & Bittner, M. L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Opt. 2, 364–374 (1997).
Smyth, G., Yang, Y. & Speed, T. in Functional Genomics (eds Brownstein, M. & Khodursky, A.) 111–136 (Humana Press, 2003).
Tomlins, S. A. et al. Integrative molecular concept modeling of prostate cancer progression. Nat. Genet. 39, 41–51 (2007).
Coletta, A. et al. InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor. Genome Biol. 13, R104 (2012).
Qu, K. et al. Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace. Nat. Methods 13, 245–247 (2016).
Burrell, R. A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).
Onder, T. T. et al. Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. Cancer Res. 68, 3645–3654 (2008).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Chen, J.-J., Knudsen, S., Mazin, W., Dahlgaard, J. & Zhang, B. A. 71-gene signature of TRAIL sensitivity in cancer cells. Mol. Cancer Ther. 11, 34–44 (2012).
Rosenwald, A. et al. The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 3, 185–197 (2003).
Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38, 1043–1048 (2006). This paper shows that aneuploidy is associated with a gene expression signature that is associated with poor clinical outcomes.
Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33, 49–54 (2003). This study reports a signature of cancer with high metastatic potential.
Bild, A. H. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006).
Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235 (2000).
Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).
Singer, G. A. C. et al. Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array. BMC Genomics 9, 349 (2008).
Nacu, S. et al. Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples. BMC Med. Genom. 4, 11 (2011).
Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).
Davuluri, R. V., Suzuki, Y., Sugano, S., Plass, C. & Huang, T. H.-M. The functional consequences of alternative promoter use in mammalian genomes. Trends Genet. 24, 167–177 (2008).
Wiesner, T. et al. Alternative transcription initiation leads to expression of a novel ALK isoform in cancer. Nature 526, 453–457 (2015).
Liu, J. et al. Integrated exome and transcriptome sequencing reveals ZAK isoform usage in gastric cancer. Nat. Commun. 5, 3830 (2014).
Keene, J. D. RNA regulons: coordination of post-transcriptional events. Nat. Rev. Genet. 8, 533–543 (2007).
Bahn, J. H. et al. Accurate identification of A-to-I RNA editing in human by transcriptome sequencing. Genome Res. 22, 142–150 (2012).
Dominissini, D., Moshitch-Moshkovitz, S., Salmon-Divon, M., Amariglio, N. & Rechavi, G. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protoc. 8, 176–189 (2013).
Burns, M. B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013).
Parkin, D. M. The global health burden of infection-associated cancers in the year 2002. Int. J. Cancer 118, 3030–3044 (2006).
Abreu, A. L. P., Souza, R. P., Gimenes, F. & Consolaro, M. E. L. A review of methods for detect human Papillomavirus infection. Virol. J. 9, 262 (2012).
Li, J.-W. et al. ViralFusionSeq: accurately discover viral integration events and reconstruct fusion transcripts at single-base resolution. Bioinformatics 29, 649–651 (2013).
Piskol, R., Ramaswami, G. & Li, J. B. Reliable identification of genomic variants from RNA-seq data. Am. J. Hum. Genet. 93, 641–651 (2013).
Kim, K.-T. et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 16, 127 (2015).
Paul, M. R. et al. Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA. BMC Genomics 17, 263 (2016).
Rubinsteyn, A. et al. Computational pipeline for the PGV-001 neoantigen vaccine trial. Preprint at bioRxiv http://dx.doi.org/10.1101/174516 (2017).
Sheng, Q., Zhao, S., Li, C.-I., Shyr, Y. & Guo, Y. Practicability of detecting somatic point mutation from RNA high throughput sequencing data. Genomics 107, 163–169 (2016).
Tang, X. et al. The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data. Nucleic Acids Res. 42, e172 (2014).
Lopez-Maestre, H. et al. SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence. Nucleic Acids Res. 44, e148 (2016).
Deelen, P. et al. Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels. Genome Med. 7, 30 (2015).
Wilkerson, M. D. et al. Integrated RNA and DNA sequencing improves mutation detection in low purity tumors. Nucleic Acids Res. 42, e107 (2014).
Maher, C. A. et al. Transcriptome sequencing to detect gene fusions in cancer. Nature 458, 97–101 (2009). This study shows that gene fusions can be detected from RNA-seq data.
MacDonald, J. W. & Ghosh, D. COPA — cancer outlier profile analysis. Bioinformatics 22, 2950–2951 (2006).
Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).
Romani, A., Guerra, E., Trerotola, M. & Alberti, S. Detection and analysis of spliced chimeric mRNAs in sequence databanks. Nucleic Acids Res. 31, e17 (2003).
Gröschel, S. et al. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell 157, 369–381 (2014).
Kalyana-Sundaram, S. et al. Gene fusions associated with recurrent amplicons represent a class of passenger aberrations in breast cancer. Neoplasia 14, 702–708 (2012).
Duro, D. et al. Inactivation of the P16INK4/MTS1 gene by a chromosome translocation t(9;14)(p21–22;q11) in an acute lymphoblastic leukemia of B-cell type. Cancer Res. 56, 848–854 (1996).
Coyaud, E. et al. Wide diversity of PAX5 alterations in B-ALL: a Groupe Francophone de Cytogénétique Hématologique study. Blood 115, 3089–3097 (2010).
Sun, Z., Bhagwate, A., Prodduturi, N., Yang, P. & Kocher, J.-P. A. Indel detection from RNA-seq data: tool evaluation and strategies for accurate detection of actionable mutations. Brief. Bioinform. https://academic.oup.com/bib/article/18/6/973/2562816 (2016).
Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).
DeVeale, B., van der Kooy, D. & Babak, T. Critical evaluation of imprinted gene expression by RNA–Seq: a new perspective. PLoS Genet. 8, e1002600 (2012).
Babak, T. et al. Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse. Nat. Genet. 47, 544–549 (2015).
Reddy, T. E. et al. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression. Genome Res. 22, 860–869 (2012).
Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
Tuch, B. B. et al. Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS ONE 5, e9317 (2010).
Anwar, S. L. et al. Loss of imprinting and allelic switching at the DLK1-MEG3 locus in human hepatocellular carcinoma. PLoS ONE 7, e49462 (2012).
Burgess, M. R. et al. KRAS allelic imbalance enhances fitness and modulates MAP kinase dependence in cancer. Cell 168, 817–829.e15 (2017).
Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).
Nilsson, J. et al. Prostate cancer-derived urine exosomes: a novel approach to biomarkers for prostate cancer. Br. J. Cancer 100, 1603–1607 (2009).
Best, M. G. et al. RNA-Seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics. Cancer Cell 28, 666–676 (2015).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Benes, V., Blake, J. & Doyle, K. Ribo-Zero Gold Kit: improved RNA-seq results after removal of cytoplasmic and mitochondrial ribosomal RNA. Nat. Methods 8 (2011).
Yi, H. et al. Duplex-specific nuclease efficiently removes rRNA for prokaryotic RNA-seq. Nucleic Acids Res. 39, e140 (2011).
Armour, C. D. et al. Digital transcriptome profiling using selective hexamer priming for cDNA synthesis. Nat. Methods 6, 647–649 (2009).
Linsen, S. E. V. et al. Limitations and possibilities of small RNA digital gene expression profiling. Nat. Methods 6, 474–476 (2009).
Raabe, C. A., Tang, T.-H., Brosius, J. & Rozhdestvensky, T. S. Biases in small RNA deep sequencing data. Nucleic Acids Res. 42, 1414–1426 (2014).
Valen, E. et al. Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE. Genome Res. 19, 255–265 (2009).
The FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
Zhernakova, D. V. et al. DeepSAGE reveals genetic variants associated with alternative polyadenylation and expression of coding and non-coding transcripts. PLoS Genet. 9, e1003594 (2013).
Sigurgeirsson, B., Emanuelsson, O. & Lundeberg, J. Sequencing degraded RNA addressed by 3′ tag counting. PLoS ONE 9, e91851 (2014).
Langevin, S. A. et al. Peregrine: a rapid and unbiased method to produce strand-specific RNA-Seq libraries from small quantities of starting material. RNA Biol. 10, 502–515 (2013).
Parkhomchuk, D. et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123 (2009).
Hafner, M. et al. Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44, 3–12 (2008).
Levin, J. Z. et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol. 10, R115 (2009). This is the first study to introduce the concept of capture RNA-seq.
Archer, S. K., Shirokikh, N. E. & Preiss, T. Selective and flexible depletion of problematic sequences from RNA-seq libraries at the cDNA stage. BMC Genomics 15, 401 (2014).
Eikrem, O. et al. Transcriptome sequencing (RNAseq) enables utilization of formalin-fixed, paraffin-embedded biopsies with clear cell renal cell carcinoma for exploration of disease biology and biomarker development. PLoS ONE 11, e0149743 (2016).
Beltran, H. et al. Impact of therapy on genomics and transcriptomics in high-risk prostate cancer treated with neoadjuvant docetaxel and androgen deprivation therapy. Clin. Cancer Res. http://dx.doi.org/10.1158/1078-0432.CCR-17-1034 (2017).
Core, L. J., Waterfall, J. J. & Lis, J. T. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008).
Hah, N. et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622–634 (2011).
Kim, Y. J. et al. HDAC inhibitors induce transcriptional repression of high copy number genes in breast cancer through elongation blockade. Oncogene 32, 2828–2835 (2013).
Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010).
Spitale, R. C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015).
Kwok, C. K., Marsico, G., Sahakyan, A. B., Chambers, V. S. & Balasubramanian, S. rG4-seq reveals widespread formation of G-quadruplex structures in the human transcriptome. Nat. Methods 13, 841–844 (2016).
Chu, C., Qu, K., Zhong, F. L., Artandi, S. E. & Chang, H. Y. Genomic maps of long noncoding RNA occupancy reveal principles of RNA-chromatin interactions. Mol. Cell 44, 667–678 (2011).
Zhao, J. et al. Genome-wide identification of polycomb-associated RNAs by RIP-seq. Mol. Cell 40, 939–953 (2010).
Engreitz, J. M. et al. RNA-RNA interactions enable specific targeting of noncoding rnas to nascent pre-mRNAs and chromatin sites. Cell 159, 188–199 (2014).
Hermann, T. & Westhof, E. RNA as a drug target: chemical, modelling, and evolutionary tools. Curr. Opin. Biotechnol. 9, 66–73 (1998).
Arnold, C. D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).
Wang, N. et al. UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples. Bioinformatics 31, 137–139 (2015).
Li, S. et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888–895 (2014).
Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).
Smyth, G. K. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S.) 397–420 (Springer, 2005).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). This study introduces a simple normalization method for RNA-seq data that made it possible to use standard linear model tools for analysis.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Frasor, J. et al. Profiling of estrogen up- and down-regulated gene expression in human breast cancer cells: insights into gene networks and pathways underlying estrogenic control of proliferation and cell phenotype. Endocrinology 144, 4562–4574 (2003).
Frazee, A. C. et al. Ballgown bridges the gap between transcriptome assembly and expression analysis. Nat. Biotechnol. 33, 243–246 (2015).
Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).
Ackermann, M. & Strimmer, K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics 10, 47 (2009).
Mitrea, C. et al. Methods and approaches in the topology-based analysis of biological pathways. Front. Physiol. 4, 278 (2013).
Majeti, R. et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc. Natl Acad. Sci. USA 106, 3396–3401 (2009).
de la Fuente, A. From 'differential expression' to 'differential networking' — identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326–333 (2010).
Woo, J. H. et al. Elucidating compound mechanism of action by network perturbation analysis. Cell 162, 441–451 (2015).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Rhodes, D. R. et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1–6 (2004).
Xiao, Y. et al. Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes. Sci. Rep. 5, 10889 (2015).
Lynn, D. J. et al. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol. Syst. Biol. 4, 218 (2008).
Ulloa-Montoya, F. et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J. Clin. Orthod. 31, 2388–2395 (2013).
Saal, L. H. et al. Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity. Proc. Natl Acad. Sci. USA 104, 7564–7569 (2007).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).
Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010).
Witt, H. et al. Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. Cancer Cell 20, 143–157 (2011).
Bayliss, J. et al. Lowered H3K27me3 and DNA hypomethylation define poorly prognostic pediatric posterior fossa ependymomas. Sci. Transl Med. 8, 366ra161 (2016).
Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).
Roberts, K. G. et al. Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N. Engl. J. Med. 371, 1005–1015 (2014).
van ' t Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002). This study demonstrates the use of microarrays to prognosticate and distinguish cancers with BRCA1 or BRCA2 mutations.
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).
Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).
Yeoh, E.-J. et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133–143 (2002). This study discovers subtypes of ALL that differ in biology, outcomes and response to therapy.
Verhaak, R. G. W. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98 (2010).
Anghel, C. V. et al. ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles. BMC Bioinformatics 16, 156 (2015).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
Quon, G. et al. Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med. 5, 29 (2013).
Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).
Choi, H. et al. Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep. 10, 1187–1201 (2015).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
Giustacchini, A. et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat. Med. 23, 692–702 (2017).
Chin, K. et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529–541 (2006).
Kuijjer, M. L. et al. Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data. Genes Chromosomes Cancer 51, 696–706 (2012).
Kristensen, V. N. et al. Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling. Proc. Natl Acad. Sci. USA 109, 2802–2807 (2012).
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).
Michailidou, K. et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat. Genet. 47, 373–380 (2015).
Bojesen, S. E. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat. Genet. 45, 371–384 (2013).
Masica, D. L. & Karchin, R. Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res. 71, 4550–4561 (2011).
Kristensen, V. N. et al. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 14, 299–313 (2014).
Ramaswamy, S. et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA 98, 15149–15154 (2001).
Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).
Torrente, A. et al. Identification of cancer related genes using a comprehensive map of human gene expression. PLoS ONE 11, e0157484 (2016).
Anaya, J., Reon, B., Chen, W.-M., Bekiranov, S. & Dutta, A. A pan-cancer analysis of prognostic genes. PeerJ 3, e1499 (2015).
Tang, K.-W., Alaei-Mahabadi, B., Samuelsson, T., Lindh, M. & Larsson, E. The landscape of viral expression and host gene fusion and adaptation in human cancer. Nat. Commun. 4, 2513 (2013).
Yoshihara, K. et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 34, 4845–4854 (2015).
Xia, Z. et al. Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3′-UTR landscape across seven tumour types. Nat. Commun. 5, 5274 (2014).
Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
Mody, R. J. et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. JAMA 314, 913–925 (2015). This is one of the first studies to demonstrate the feasibility and utility of RNA-seq in the real-time management of paediatric tumours.
Oberg, J. A. et al. Implementation of next generation sequencing into pediatric hematology-oncology practice: moving beyond actionable alterations. Genome Med. 8, 133 (2016).
Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017). This is the first study to demonstrate the broad utility of transcriptomic data in characterizing metastatic tumours.
Cheng, D. T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).
Shukla, S. et al. Identification and validation of PCAT14 as prognostic biomarker in prostate cancer. Neoplasia 18, 489–499 (2016).
Yang, L. et al. Analyzing somatic genome rearrangements in human cancers by using whole-exome sequencing. Am. J. Hum. Genet. 98, 843–856 (2016).
Hutchins, G. et al. Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. J. Clin. Oncol. 29, 1261–1270 (2011).
Meng, X., Huang, Z., Teng, F., Xing, L. & Yu, J. Predictive biomarkers in PD-1/PD-L1 checkpoint blockade immunotherapy. Cancer Treat. Rev. 41, 868–876 (2015).
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
Cardoso, F. et al. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375, 717–729 (2016). This is a large-scale, multi-institutional study to evaluate the clinical utility of MammaPrint.
CRUK Lung Cancer Centre of Excellence. TRACERx. CRUK Lung Cancer Centre of Excellence http://www.cruklungcentre.org/Research/TRACERx (2017).
MD Anderson Cancer Center. APOLLO. MD Anderson Cancer Center https://www.mdanderson.org/cancermoonshots/research_platforms/apollo.html (2017).
Wei, I. H., Shi, Y., Jiang, H., Kumar-Sinha, C. & Chinnaiyan, A. M. RNA-Seq accurately identifies cancer biomarker signatures to distinguish tissue of origin. Neoplasia 16, 918–927 (2014).
Feng, H., Zhang, X. & Zhang, C. mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data. Nat. Commun. 6, 7816 (2015).
Karmakar, S. et al. Organocatalytic removal of formaldehyde adducts from RNA and DNA bases. Nat. Chem. 7, 752–758 (2015).
Fernando, M. R., Norton, S. E., Luna, K. K., Lechner, J. M. & Qin, J. Stabilization of cell-free RNA in blood samples using a new collection device. Clin. Biochem. 45, 1497–1502 (2012).
Alhasan, A. A. et al. Circular RNA enrichment in platelets is a signature of transcriptome degradation. Blood 127, e1–e11 (2016).
Li, Y. et al. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 25, 981–984 (2015).
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).
Huang, X. et al. Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics 14, 319 (2013).
Chen, X. Q. et al. Telomerase RNA as a detection marker in the serum of breast cancer patients. Clin. Cancer Res. 6, 3823–3826 (2000).
Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).
Kong-Beltran, M. et al. Somatic mutations lead to an oncogenic deletion of met in lung cancer. Cancer Res. 66, 283–289 (2006).
Zhang, J., Mardis, E. R. & Maher, C. A. INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. Bioinformatics 33, 555–557 (2016).
Mehra, R. et al. Biallelic alteration and dysregulation of the Hippo pathway in mucinous tubular and spindle cell carcinoma of the kidney. Cancer Discov. 6, 1258–1266 (2016).
van Rhee, F. et al. NY-ESO-1 is highly expressed in poor-prognosis multiple myeloma and induces spontaneous humoral and cellular immune responses. Blood 105, 3939–3944 (2005).
Ludwig, J. A. & Weinstein, J. N. Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 5, 845–856 (2005).
Kulasingam, V. & Diamandis, E. P. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat. Clin. Pract. Oncol. 5, 588–599 (2008).
Topalian, S. L., Taube, J. M., Anders, R. A. & Pardoll, D. M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 16, 275–287 (2016).
Aran, D. et al. Widespread parainflammation in human cancer. Genome Biol. 17, 145 (2016).
Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).
Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248–262 (2017).
Chen, P.-L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016). This is one of the first longitudinal studies involving RNA-seq profiling.
Roh, W. et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci. Transl Med. 9, eaah3560 (2017).
Paluch, B. E. et al. Robust detection of immune transcripts in FFPE samples using targeted RNA sequencing. Oncotarget 8, 3197–3205 (2017).
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Carreno, B. M. et al. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).
Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29, 1083–1085 (2013).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
Mose, L. E. et al. Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer. Bioinformatics 32, 3729–3734 (2016).
Seqc/Maqc-Iii Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).
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).
Zhang, W. et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 16, 133 (2015).
Schurch, N. J. et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22, 839–851 (2016).
Thierry-Mieg, D. & Thierry-Mieg, J. AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol. 7 (Suppl. 1), S12 (2006).
Ermolaeva, O. et al. Data management and analysis for gene expression arrays. Nat. Genet. 20, 19–23 (1998).
Shiraki, T. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl Acad. Sci. USA 100, 15776–15781 (2003).
Strausberg, R. L. Cancer Genome Anatomy Project. eLS http://dx.doi.org/10.1038/npg.els.0006070 (2006).
Hon, C.-C. et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nature 543, 199–204 (2017).
Searle, S. et al. The GENCODE human gene set. Genome Biol. 11 (Suppl. 1), P36 (2010).
Subramanian, A., Kuehn, H., Gould, J., Tamayo, P. & Mesirov, J. P. GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics 23, 3251–3253 (2007).
Hsu, F. et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).
Mitelman, F., Johansson, B., & Mertens, F. Mitelman database of chromosome aberrations in cancer. National Cancer Institute https://cgap.nci.nih.gov/Chromosomes/Mitelman (2001).
Frohman, M. A., Dush, M. K. & Martin, G. R. Rapid production of full-length cDNAs from rare transcripts: amplification using a single gene-specific oligonucleotide primer. Proc. Natl Acad. Sci. USA 85, 8998–9002 (1988).
Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).
Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).
Lash, A. E. et al. SAGEmap: a public gene expression resource. Genome Res. 10, 1051–1060 (2000).
Patro, R., Mount, S. M. & Kingsford, C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat. Biotechnol. 32, 462–464 (2014).
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130 (2009).
Niknafs, Y. S., Pandian, B., Iyer, H. K., Chinnaiyan, A. M. & Iyer, M. K. TACO produces robust multisample transcriptome assemblies from RNA-seq. Nat. Methods 14, 68–70 (2017).
Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nat. Methods 7, 909–912 (2010).
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
Goldman, M. et al. The UCSC Xena system for integrating and visualizing functional genomics [abstract]. Cancer Res. 76 (Suppl.), 5270 (2016).
Mitelman, F., Johansson, B. & Mertens, F. Fusion genes and rearranged genes as a linear function of chromosome aberrations in cancer. Nat. Genet. 36, 331–334 (2004).
Forbes, S. A. et al. The Catalogue of Somatic Mutations in Cancer (COSMIC). Curr. Protoc. Hum. Genet. 57, 10.11 (2008).
Hartley, S. W. & Mullikin, J. C. QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics 16, 224 (2015).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2013).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Nicorici, D. et al. FusionCatcher — a tool for finding somatic fusion genes in paired-end RNA-sequencing data. Preprint at bioRxiv http://dx.doi.org/10.1101/011650 (2014).
Kim, D. & Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011).
Grossman, R. L., Heath, A. P., Ferreti, V., Varmus, H. E., Lowy, D. R., Kibbe, W. A. & Staudt, L. M. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
The authors thank S. Ellison for assistance in writing, editing and preparing this manuscript. A.M.C. is a Howard Hughes Medical Institute investigator and American Cancer Society professor. M.C. is a Prostate Cancer Foundation Young Investigator.
The authors declare no competing financial interests.
- RNA sequencing
(RNA-seq). An encompassing term for all cDNA profiling techniques using high-throughput sequencing.
DNA molecules obtained through reverse transcription of RNAs.
- Expressed sequence tags
(ESTs). Short fragments of a cDNA sequence that identify (tag) a transcript.
A method of cDNA profiling through hybridization and fluorescent labelling.
- Serial analysis of gene expression
(SAGE). An economical technique for sequencing very short tags (11 nucleotides) from multiple cDNAs in one Sanger sequencing run.
- Digital gene expression
A high-throughput, low-cost technique for expression profiling that involves sequencing short tags rather than the whole transcript.
- Unique molecular identifiers
(UMIs). Sequences that are unique to each reverse-transcribed cDNA. PCR duplicates share the same UMI.
A barcoding-based and imaging-based technique for the detection and quantification of hundreds of transcripts.
The study of biochemical modifications of RNA molecules.
- Passenger mutations
Mutations that have no measurable effect on the growth of a clone.
- Allele-specific expression
(ASE). The analysis of differences in the expression from both alleles, that is, expression variation between the two haplotypes. Also known as allelic imbalance.
- Cap analysis of gene expression
(CAGE). A molecular technique to sequence the 5′ end of transcripts.
Prediction analysis of microarray 50. A gene expression signature to classify breast cancer into intrinsic subtypes.
- Driver mutations
Mutations that provide the cancer with a strong selective advantage, that is, mutations that result in the clonal growth of mutant cells.
- Clinical utility
Whether a test has a substantial effect on the diagnosis, prognosis or treatment of a patient.
- Allelic dropout
When a sample is sequenced and one or more alleles are not detected.
- Analytical validity
The ability to accurately detect and measure the biomarker of interest.
- Clinical validity
The clinical performance of a test, that is, how well the test is able to identify the clinical variable of interest (for example, disease status).
Antigens, herein short peptides, not previously recognized by the immune system. They can be formed by somatic mutations during tumorigenesis.
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Cieślik, M., Chinnaiyan, A. Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet 19, 93–109 (2018). https://doi.org/10.1038/nrg.2017.96
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