Maximizing the Utility of Cancer Transcriptomic Data

Review

Maximizing the Utility of Cancer Transcriptomic Data Yu Xiang,1,3 Youqiong Ye,1,3 Zhao Zhang,1 and Leng Han

1,2,

*

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

Highlights The explosive volume of cancer transcriptomic data provides opportunities to understand transcriptomic events in the genome. Advances in mining cancer transcriptomic data enable us to understand the complexity of the transcriptome. Transcriptomic events, including noncoding RNA, post-transcriptional regulation, exogenous RNA, and transcribed genetic variants, inform the discovery of novel biomarkers and therapeutic targets.

The Explosive Volume of Cancer Transcriptomic Data RNA sequencing (RNA-seq), one of the most popular applications of high-throughput sequencing technology, is used to measure gene expression levels across the transcriptome. More than 200 000 human RNA-seq samples have been deposited in a public data repository, the Sequence Read Archivei, and the number is increasing continuously (Figure 1). Among these, more than 60 000 are cancer-related samples. RNA-seq is a series of related protocols, including poly(A)-selected RNA-seq, total RNA-seq, small RNA-seq, and 50 or 30 ends enriched RNA-seq [1]. Poly(A)-selected RNA-seq is one of the most popular and cost effective protocols. Several cancer genomic projects have released large-scale high-quality poly(A)-selected RNAseq data, including The Cancer Genome Atlas (TCGA)ii (10 000 samples) [2], International Cancer Genome Consortium (ICGC)iii (12 000 samples) [3], Cancer Cell Line Encyclopedia (CCLE)iv (1000 samples) [4], St. Jude Pediatric Cancerv (4000 samples) [5], and MiTranscriptome (6500 samples) [6]. Comprehensive analyses of these transcriptomes provide unique opportunities to understand oncogenic processes [7] and signaling pathways [8]. Deep mining of cancer transcriptomic data provide the opportunity to develop new strategies for cancer treatment [1]. These new discoveries are being translated into the clinic for diagnostic and prognostic purposes [9].

Noncoding RNA The human genome encodes approximately 20 000 protein-coding genes and a large number of noncoding RNAs [10]. These noncoding RNAs have emerged as biomarkers and therapeutic targets [11]. Most types of noncoding RNAs can be captured by RNA-seq, including long noncoding RNA (lncRNA) (see Glossary), pseudogenes, enhancer RNA (eRNA), small nucleolar RNA (snoRNA), and circular RNA (circRNA) (Figure 2A).

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1

Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 2 Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 3 These authors contributed equally

*Correspondence: [email protected] (L. Han).

https://doi.org/10.1016/j.trecan.2018.09.009 © 2018 Elsevier Inc. All rights reserved.

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Glossary

# of RNA-seq samples in SRA

300 000

100 000

30 000

10 000

3000

1000 2015

St. Jude PeCan

2014

2016

2017

2018

ConsorƟum

2013

MiTranscriptome

2012

TARGET

2011

CCLE

ICGC

TCGA

2010

Key: All samples

Cancer-related samples

Figure 1. The Explosive Volume of Cancer Transcriptomic Data. The number of RNA-seq samples deposited in the public data repository (upper panel), and large-scale cancer genomic projects (bottom panel). CCLE, Cancer Cell Line Encyclopedia; ICGC, International Cancer Genome Consortium; SRA, Sequence Read Archive; TARGET, Therapeutically Applicable Research To Generate Effective Treatments; TCGA, The Cancer Genome Atlas.

lncRNA lncRNAs are transcripts, >200 nucleotides in length and without protein coding potential [12]. Emerging evidence demonstrated significant roles of lncRNAs in cancer through interaction with proteins, other RNA, and lipids [13]. They are also promising clinical biomarkers and therapeutic targets [14]. Tremendous efforts have been made to annotate lncRNAs in the human genome, such as ENCODEvi [15]. In particular, LNCat (lncRNA atlas) has collected 24 lncRNA annotation resources that refer to >200 000 lncRNAs in over 50 tissues and cell lines [16]. Quantification of lncRNA expression based on existing annotations is straightforward: map RNA-seq reads to annotated regions of lncRNA and then calculate and normalize the expression level as for protein-coding genes. De novo identification of lncRNAs has required a rigorous computational pipeline for ab initio transcriptome assemblies, followed by assessment of the coding potential [6]. Through this effort, MiTranscriptome curated a large amount of RNA-seq data from tumors, normal tissues, and cell lines from 25 independent studies, and provides the landscape of lncRNAs in the cancer transcriptome [6]. TANRIC provides an interactive, open platform for exploring the expression landscape of lncRNAs across multiple cancer types [17]

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Allele-specific expression (ASE): differential abundance of multiple alleles of a transcript in a diploid organism. Alternative polyadenylation (APA): RNA 30 end processing that generates distinct 30 termini. Alternative splicing (AS): a process by which identical pre-mRNA molecules are spliced in different ways. Circular RNA (circRNA): a novel RNA molecule that forms a covalently closed circular structure. Enhancer RNA (eRNA): noncoding RNAs transcribed from enhancer regions. Exogenous RNA: RNA originating from exogenous species. Expression quantitative trait locus (eQTL): genomic locus that accounts for all or a fraction of the changes in mRNA expression levels. Gene fusion: a process by which hybrid genes form from two previously independent genes. Long noncoding RNAs (lncRNAs): noncoding RNAs longer than 200 nucleotides that lack protein-coding potential. Pseudogene: dysfunctional copies of protein-coding genes that have lost protein-coding ability. RNA editing: a post-transcriptional mechanism that confers nucleotide changes in RNA transcripts. Single-nucleotide variant (SNV): an alteration of a single nucleotide in a DNA sequence. Small nucleolar RNAs (snoRNAs): noncoding RNAs predominantly found in the nucleolus and which primarily guide chemical modifications of other RNAs.

(A) Noncoding RNA

IncRNA

Pseudogene

eRNA

snoRNA

(B) Post-transcripƟonal regulaƟon

circRNA

(C) Exogenous

PAS PAS PAS

Viral intergraƟon Viral RNA

AlternaƟve polyadenylaƟon

AlternaƟve splicing

A I RNA ediƟng

Bacterial RNA

(D) Transcribed geneƟc variant

Gene fusion

A

C SNV

T

eQTL

ASE

C

A

Figure 2. Overview of Transcriptomic Events Captured by RNA-seq. (A) Noncoding RNAs, including long noncoding RNA (lncRNA), pseudogene, enhancer RNA (eRNA), small nucleolar RNA (snoRNA), and circular RNA (circRNA). (B) Post-transcriptional regulation, including alternative splicing, alternative polyadenylation, and RNA editing. (C) Exogenous RNAs, including viral RNA and bacterial RNA. (D) Transcribed genetic variants, including gene fusion, single-nucleotide variation (SNV), allele-specific expression (ASE), and expression quantitative trait loci (eQTL). PAS, Polyadenylation site.

and theoretically allows users to query the expression level of any novel transcript. Analysis based on expression profiles from TANRIC showed that lncRNA expression could define a novel subtype, thus demonstrating potential roles of lncRNAs in cancer pathogenesis and biomarker development. Several databases have been developed to study lncRNAs in cancer: Lnc2Cancer and Lnc2Catlas provide interactive interfaces to examine associations between lncRNAs and human cancers [18,19], while LnCaNet provides a pan-cancer coexpression network for lncRNAs and cancer genes in TCGA samples [20] (Table 1). Using these existing resources, further studies have systematically characterized the functional alterations of lncRNAs across multiple cancer types. For example, lncRNAs are altered in cancers at the

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Table 1. Summary of Databases for Cancer-Related Transcriptomic Events Database

Description

URL

Refs

MiTranscriptome

A database of human lncRNA from over 6000 samples spanning diverse cancer and tissue types

http://www.mitranscriptome.org

[6]

TANRIC

An interactive open platform to explore the function of lncRNAs in cancer

https://bioinformatics.mdanderson.org/main/ TANRIC:Overview

[17]

lnc2Cancer

A manually curated database that provides experimentally supported associations between lncRNA and cancer

http://www.bio-bigdata.com/lnc2cancer

[19]

Lnc2Catlas

An atlas of long noncoding RNAs associated with risk of cancers

https://lnc2catlas.bioinfotech.org

[18]

lnCaNet

A comprehensive regulatory network resource for lncRNA and cancer genes

http://lncanet.bioinfo-minzhao.org

[20]

Pseudogene expression profile across multiple cancer types

https://www.synapse.org/#!Synapse: syn1732077/files

[26]

A data portal to explore snoRNAs in cancer

http://bioinfo.life.hust.edu.cn/SNORic

[44]

A cancer-specific circRNA database

http://gb.whu.edu.cn/CSCD

[57]

A data portal to explore the alternative splicing of TCGA tumors

http://projects.insilico.us.com/TCGASpliceSeq

[66]

PolyASite

A repository for 30 end sequencing data

http://www.polyasite.unibas.ch

[81]

PolyA_DB

A database cataloging cleavage and polyadenylation sites (PASs) in several genomes

http://www.polya-db.org/v3

[82]

APADB

A database of vertebrate polyadenylation sites determined by 30 end sequencing

http://tools.genxpro.net/apadb

[83]

APASdb

A web-accessible database of APA sites to visualize the precise map and usage quantification of different APA isoform

http://genome.bucm.edu.cn/utr

[84]

TC3A

Comprehensive resource of APA usage in TCGA samples

http://tc3a.org

[85]

CCLE APA

TCGA and CCLE APA data on Synapse: syn7888354

https://www.synapse.org/#!Synapse: syn7888354

[80]

TCGA RNA editing data on Synapse: syn2374375

https://www.synapse.org/#!Synapse: syn2374375

[102]

Dr.VIS

A database of human disease-related viral integration sites

http://www.bioinfo.org/drvis

[111]

MicroView

A database for analysis and visualization presence of bacterial in TCGA data

http://microview.igs.umaryland.edu/tcga_v1

[113]

ChimerDB 3.0

An enhanced database for gene fusions from cancer transcriptome and literature data mining

http://ercsb.ewha.ac.kr/fusiongene

[122]

COSMIC fusion

Manually curated gene fusions from peer reviewed publications

https://cancer.sanger.ac.uk/cosmic/fusion

[123]

Long noncoding RNA

Pseudogene TCGA_PseudoGene Small nucleolar RNA SNORic Circular RNA CSCD Alternative splicing TCGASpliceSeq Alternative polyadenylation

RNA editing TCGA_RNAediting Exogenous RNA

Gene fusion

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Table 1. (continued) Database

Description

URL

Refs

FusionCancer

Annotated information of gene fusions from 591 recently published RNA-seq datasets across 15 cancer types

http://donglab.ecnu.edu.cn/databases/ FusionCancer

[124]

Tumorfusions

A resource for cancer-associated transcript fusions from TCGA

http://www.tumorfusions.org

[125]

Systematic identification of cis-eQTLs and trans-eQTLs in TCGA

http://bioinfo.life.hust.edu.cn/PancanQTL

[149]

eQTLs PancanQTL

transcriptional, genomic, and epigenetic levels [21] and can synergistically dysregulate cancer pathways in both a tumor-specific manner and in multiple tumor contexts [22].

Pseudogene Pseudogenes are dysfunctional copies of protein-coding genes that have lost the ability to produce proteins through the accumulation of deleterious mutations [23]. Pseudogenes play significant roles in cancer, likely through the regulation of their wild-type cognate genes by sequestering microRNAs (miRNAs) such as PTENP1 and KRASP1 [24]. Tremendous efforts have been undertaken to annotate pseudogenes in the human genome through the ENCODE project [15] and the Yale pseudogene databasevii [25]. To accurately quantify pseudogene expression from RNA-seq, a remaining challenge is to distinguish between a pseudogene and its wild-type gene, due to their high level of sequence similarity. One way to address the potential issue of cross-mapping is to retain reads with high alignability [26]. Another way is to filter out RNA-seq reads that are likely to have originated from coding genes [27]. The first systematic analysis of pseudogenes across multiple cancer types demonstrated their functional effects [28]. A further study in a TCGA dataset showed that tumor subtypes defined by pseudogene expression are highly concordant with subtypes defined by other molecular data, and that the pseudogene expression subtypes may provide additional prognostic power [26]. The expression profile of pseudogenes across multiple cancer types was released through Synapse (syn1732077) for further analysis (Table 1).

eRNA Enhancers, which are noncoding genomic regions that act spatially with targeted promoters to regulate downstream genes [29], show tight association with disease, including cancer [30]. The identification and prediction of enhancers have required comprehensive analysis of genomic features, transcription factor binding features, and epigenetic features [31]. Current annotations of enhancers include ENCODE [15], FANTOMviii [32], and EnhancerAtlasix [33]. Enhancers have been found to be broadly transcribed, leading to the production of enhancerderived RNAs, also known as eRNAs [34]. Expression of eRNAs is considered an essential signature of enhancer activation, thus making it significant to study enhancer activities by RNAseq [34]. For example, androgen receptor (AR)-induced eRNA may regulate the expression of AR-dependent oncogenes in prostate cancer, which influences androgen deprivation therapy [35]. P53-induced eRNAs are involved in the P53-dependent cell cycle arrest process across multiple cancer cell lines [36]. A recent study utilized high-quality expressed enhancer annotations from FANTOM and characterized the expressed enhancers across 9000 samples from

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TCGA. This study revealed a positive correlation between global enhancer activation and aneuploidy. The study further identified enhancers as key regulators for therapeutic targets, including PD-L1, which highlights the clinical implications of enhancers [37]. These findings opened a new door to study expressed enhancers in large-scale cancer transcriptomic data.

snoRNA snoRNAs are noncoding RNAs, 60–300 nucleotides in length, and predominantly found in the nucleolus [38]. They are associated with ribonucleoproteins (RNPs) to form snoRNP particles and guide the modification of ribosomal RNAs and spliceosomal RNAs [38]. Annotations of snoRNAs are collected in UCSC Genome Browserx [39] and GENCODEx [40]. Emerging evidence has revealed functional roles of snoRNAs in oncogenesis. For example, SNORD50A/B is deleted across multiple cancer types and the deletion is associated with poorer survival [41]. Unfortunately, only a few snoRNAs can be captured by RNA-seq, likely due to the lack of poly-A tails among snoRNAs [42]. SnoRNA-seq has been developed by restricting the insert size, ranging from 40 to 200 bp, for sequencing libraries [43]; however, this method has not been applied to a large number of cancer samples. Alternatively, miRNA-seq provides the opportunity to study the expression landscape of snoRNAs. miRNA-seq is not designed for a full snoRNA repertoire, but is probably the most appropriate way to quantify the snoRNA expression landscape from TCGA omic data [44]. One study revealed the regulation pattern of snoRNAs and identified 46 clinically relevant snoRNAs [44]. More importantly, the study released a data portal, snoRNA in cancer (SNORic), which allow users to query snoRNAs for further study [44] (Table 1).

circRNA circRNA is a novel noncoding RNA characterized by a covalently closed circular structure [45], which makes it unique compared with linear RNAs. circRNAs are generated in a ‘back-splicing’ process, in which a downstream 50 splicing site back-splices to an upstream 30 splice site. circRNAs play important roles in cancer by functioning as miRNA sponges [46]. Several computational tools have been developed to detect circRNAs. These tools either align reads directly against a reference genome, such as find_circxii [47], CIRCexplorerxiii [48], CIRIxiv [49], CIRI-ASxv [50], and circRNA_finderxvi [51], or align reads against a pseudo-reference built from genomic annotation to detect back-spliced junction reads, such as NCLscanxvii [52], KNIFExviii [53], and PTESFinderxix [54]. These tools adopt different strategies [55] with different resulting levels of performance [56]. Therefore, it might be more appropriate to combine multiple tools to reduce false positives [56]. To explore the landscape of circRNAs in cancer, a cancer-specific circRNA database (CSCD) was developed and collected >270 000 cancer-specific circRNAs, as well as their RNA regulatory binding sites and miRNA binding sites [57]. This valuable resource is the first database for cancer-specific circRNAs across a large number of samples (Table 1).

Post-transcriptional Regulation Human transcripts are under extensive post-transcriptional regulation [58], including alternative splicing (AS), alternative polyadenylation (APA), and RNA editing (Figure 2B). AS AS is the process by which identical pre-mRNA molecules are spliced in different ways [59]. Up to 94% of human multi-exon genes are alternatively spliced, contributing to complexity in the transcriptome and leading to protein diversity [60,61]. Aberrant splicing events or mutations in splice-site sequences frequently occur in cancer [62]. Specific isoforms of genes are 828

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associated with the hallmarks of cancer [59], rendering them as promising therapeutic targets in cancer treatments [62]. Dozens of AS detection tools have been developed [63], facilitating the exploration of AS patterns across large-scale cancer transcriptomic datasets. Mutation-mediated AS events have been identified to classify cancer patients according to tumor subtypes with distinct clinical features across 33 cancer types [64]. Analysis across 16 cancer types showed frequently retained introns in transcripts from tumor samples compared with matched normal samples, suggesting significant roles of novel peptides in cancer [65]. TCGASpliceSeq provides a comprehensive map for AS events across 33 cancer types from TCGA [66] (Table 1).

APA APA is widespread mRNA 30 end processing, which provides a means to regulate mRNA metabolism, including mRNA stability, translation efficiency, and intracellular localization [67,68]. Studies have highlighted the important roles of APA in human cancer. For example, proliferating cells tend to produce mRNAs with shortened 30 untranslated regions (UTRs) [69], and transcripts of several proto-oncogenes in tumor cells tend to have 30 UTR shortening [70]. In addition to the development of APA protocols to capture APA sites [71,72], several bioinformatic tools have been developed to identify APA events from standard RNA-seq data. These tools can be classified into two categories: one is based on prior annotation of the polyA site, and includes MISOxx [73], 3USSxxi [74], and QAPAxxii [75]; and the other uses de novo identification and quantification of dynamic APA events and thus is more powerful in cancer research. Tools in the second category include Daparsxxiii [76], APAtrapxxiv [77], and TAPASxxv [78]. TAPAS, in particular, can detect more than two APA sites, as well as the APA sites before the last exon [78]. With these powerful tools, genome-wide APA analysis using large-scale cancer transcriptomic datasets has revolutionized our understanding of important roles of APA in cancer. Recent studies have revealed global shortening of 30 UTRs in tumor samples compared with normal samples to avoid miRNA-mediated repression [76]. Further deep mining has shown surprising enrichment of 30 UTR shortening that is likely to function as competing-endogenous RNAs (ceRNAs) for tumor-suppressor genes [79]. Another study showed striking global 30 UTR shortening in cancer cell lines and identified an appreciable number of clinically relevant APA events [80]. More importantly, this study provided evidence that APA events could affect drug sensitivity, especially for drugs that target chromatin modifiers, suggesting clinical utility in cancer. Several databases provide resources for further analysis of APA events in cancer, including PolyASite [81], PolyA_DB [82], APADB [83], and APASdb [84], and have collected APA events detected from PolyA-seq in a limited number of cancer samples. APA events across 800 cancer cell lines can be accessed through Synapse [80]. In particular, TC3A provides the most comprehensive resource of APA usage for >10 000 cancer samples across 32 cancer types [85], which is a valuable resource for further APA analysis (Table 1).

RNA Editing RNA editing is a post-transcriptional event that modifies a single base change on RNA nucleotides without altering their genomic DNA [86]. RNA editing events can lead to modulation

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of AS in mRNA, missense codon changes, and modifications of noncoding RNAs [86], including both miRNAs [87] and lncRNAs [88]. RNA editing may also contribute to proteomic diversity in cancer [89]. Among the tools developed to identify RNA editing sites by comparing RNA-seq with DNA-seq are REDItoolsxxvi [90], JACUSAxxvii [91], and RES-Scannerxxviii [92]. A further study identified RNA editing sites from RNA-seq alone by taking advantage of the fact that the same editing sites are more likely to be present in different individuals than in rare singlenucleotide polymorphisms (SNPs) [93]. Similar approaches have been utilized in other tools, including GIREMIxxix [94], RDDpredxxx [95], RASERxxxi [96], RED-MLxxxii [97], and RNAEditorxxxiii [98], maximizing the power to identify RNA editing sites from enormous RNA-seq datasets. Advanced by these computational tools, databases with millions of high-confidence RNA editing sites have been established, including RADARxxxiv [99] and REDIportalxxxv [100]. These data resources provide the research community with good opportunities to study the global pattern of A-to-I editing in healthy individuals and/or patients. Using the cancer transcriptome to understand the role of RNA editing in cancer, a pilot study identified a novel recoding RNA editing of AZIN1 as a potential driver in human hepatocellular carcinoma [101]. Further study characterized the genomic landscape and clinical relevance of RNA editing using large-scale RNA-seq data from TCGA and suggested that globally altered RNA editing patterns are likely to be affected by ADAR1 [102]. That study identified an appreciable number of clinically relevant events and experimentally demonstrated the effects of RNA editing on drug sensitivity. More importantly, the editing level for each detectable RNA editing site across TCGA samples was deposited to Synapse (syn2374375) [102] (Table 1). Other than the functional effects of individual RNA editing sites, further study showed that A-to-I editing is the major source of mRNA variability in cancer, and that the global Alu editing index could predict patient survival [103].

Exogenous RNA Around 15% of human cancers are estimated to have links to microbial infections (e.g., viral and/or bacterial infection) [104] (Figure 2C). Microbes can contribute to carcinogenesis through multiple mechanisms, such as altering proliferation of the host cell and influencing the host’s metabolism and immune system [105]. Microbes may even affect cancer immunotherapy [106,107]. Therefore, the detection of viral and bacterial genetic material within a tumor is critical for cancer diagnosis and prognosis. PathSeqxxxvi proposed the first concept to identify non-human nucleic acids that may indicate candidate microbes by subtracting the alignment to human reference sequences and aligning the remaining reads to microbial reference sequences [108]. This idea has been applied in other computational tools, such as virusSeqxxxvii [109] and VirusFinderxxxviii [110]. Dr.VIS provides a database of oncogenic viral integration sites in various human diseases, including cancer [111]. Several studies have further explored the interplay between microbes and cancer by using TCGA RNA-seq data. A systematic screening of viral expression in 4433 tumor samples provided a comprehensive map of tumor-associated viruses and identified recurrent fusions between viral and host genes [112]. Another study suggested the possibility of identifying bacteria associated with particular tumor types by carefully considering all bacterial taxa present in cancer, their relative abundance, and batch effects. This study also provided an interactive website, MicroView, to examine the presence of bacteria in TCGA samples [113]. The microbiota is also associated with tumor expression profiles in patients with breast cancer, suggesting complicated interplay between the microbiota and human cancer [114].

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Transcribed Genetic Variant Genetic variants, including structural variants and single-nucleotide variants (SNVs), are generally detected by DNA-seq [115]. RNA-seq has been utilized to capture genetic variants that impact transcription, such as gene fusion, transcribed SNVs, allele-specific expression (ASE), and expression quantitative trait loci (eQTL) (Figure 2D). RNA-seq in particular provides additional insights into these genetic variants, especially for samples for which we lack DNA-seq data.

Gene Fusion Gene fusions are an important class of cancer drivers that are generated from genomic structural rearrangements, transcription read-throughs of neighboring genes, and/or splicing of mRNA molecules [116]. These events have the potential to generate chimeric proteins with altered functions, which can serve as biomarkers for the diagnosis of specific cancer types and are thus promising therapeutic targets [116]. For example, chronic myelogenous leukemia (CML) is characterized by BCR–ABL1 fusion, and the drug imatinib, an inhibitor of the tyrosinekinase activity of BCR–ABL, has been successfully used to treat patients with chronic-phase CML [117]. More than 30 bioinformatic algorithms have been developed to detect gene fusions [118]. These algorithms utilize split-reads (one read that aligns to different genes) or spanning reads (each read of paired reads aligns to different genes) to identify the fusion events [119]. The determination of gene fusions for the full cancer genome has further revealed that gene fusions play critical roles in oncogenesis. Novel and recurrent fusions have been shown to drive tumorigenesis through constitutive activation of kinases [120]. A systematic study using TCGA RNA-seq data identified a total of 25 664 gene fusions and revealed that gene fusions involving oncogenes tend to exhibit increased gene expression, whereas fusions involving tumor suppressors have the opposite effect on gene expression [121]. Several comprehensive databases for cancer-related gene fusions have been constructed (Table 1). For example, ChimerDB [122] and COSMIC Gene Fusion [123] provide manually curated gene fusions from previous publications. FusionCancer compiled fusion genes from 591 published human cancer RNA-seq datasets [124]. TumorFusions catalogued 20 731 gene fusions detected across 33 cancer types, using TCGA RNA-seq data [125]. These resources facilitate further studies of the functional effects of gene fusions in cancer.

SNV SNVs are alterations of a single nucleotide in a DNA sequence. Nonsynonymous SNVs (nsSNVs) may affect protein function, including expression, folding, binding affinity, and post-translational modification [126], and thus drive disease progression or contribute to phenotypic traits. TCGA consortium established the mutational landscape across 33 cancer types through the analysis of DNA-seq [115]. It is also possible to call SNVs for genes with relatively high expression levels from RNA-seq [127]. Dozens of tools have been developed to call variants through whole-genome sequencing or whole-exome sequencing, such as MuSExxxix [128]. Tools developed for DNA-seq can also accept RNA-seq data to call variants; these tools include VarScan2xl [129], RADIAxli [130], Seuratxlii [131], and VarDictxliii [132]. Several tools have been developed to specifically identify SNVs from RNA-seq, including eSNV-detectxliv [133], SNPiRxlv [134], and VaDiRxlvi [135]. However, due to the interference of RNA editing sites, high error rates for alignment near splicing junctions, read-depth differences between lowly and highly expressed genes, and error introduced through reverse transcription, it is still more accurate to call SNVs from DNA-seq [127]. Nonetheless, the

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enormous volume of RNA-seq data provides an alternative approach to identify transcribed SNVs, especially for samples for which we have no DNA-seq data.

ASE ASE refers to the unequal expression of multiple alleles of a transcript in a diploid organism. ASE may arise through X-chromosome inactivation, genetic imprinting, and uniparental disomy [136]. AS, protein-truncating mutations, and other post-transcriptional mechanisms may also cause ASE [137,138]. ASE plays important roles in human cancer; for example, ASE of the transforming growth factor beta receptor 1 (TGFBR1) is associated with the risk of colon cancer [139]. Accurate ASE quantification requires the use of personalized haplotype genome alignment, strict alignment quality control, and intragenic SNP aggregation [140]. Many tools have been developed to quantify ASE from RNA-seq and genotyping data [141], such as MMSEQxlvii [142]. It is also possible to quantify ASE from RNA-seq data alone. For example, MBASEDxlviii [143] aggregates information across multiple SNV loci to obtain a measurement of ASE at the gene level, and has been used to identify extensive ASE in cancer. Furthermore, SCALExlix [144] detects ASE from single-cell RNA-seq data with appropriate adjustment for technical variability.

eQTL The eQTL are genomic loci that account for all or a fraction of the changes in mRNA expression levels [145]. eQTL analysis is a powerful approach to identify candidate susceptibility genes for cancer risk [146–148] by linking genotype and expression data. PancanQTL is a comprehensive resource for cis- and trans-eQTL across 33 cancer types from TCGA, and has enabled researchers to further investigate the role of genetic variation in tumorigenesis [149] (Table 1).

Concluding Remarks It is still a technical challenge (e.g., batch effect, inconsistency of data format and preprocessing) to integrate various levels of transcriptomic data. Therefore, transcriptomic profiling has been applied to large-scale datasets, including TCGA, ICGC, and CCLE, which provide uniquely comprehensive opportunities for integrative analysis. Alterations at the gene, gene set, pathway, and network levels have been characterized through transcriptomic profiling [1]. For example, TCGA pan-cancer project characterized alterations of gene expression in multiple cancer signaling pathways [8], their effects on the immune landscape [150], and pharmacogenomic interactions with the circadian clock in cancer chronotherapy [151]. However, these analyses were underpowered to determine the full utility of cancer transcriptomic data. In the past decade, the research community has demonstrated the significance of deep mining of transcriptomic data and revealed the endless complexity of the transcriptome. For example, recent studies uncovered the diversity of noncoding RNAs [152], such as lncRNAs, pseudogenes, and circRNAs. Meanwhile, post-transcriptional regulation, including AS, APA, and RNA editing, have been shown to add another layer to this complexity. It will be powerful to further integrate transcriptomic data with other types of omics data to identify transcribed genetic variant, including ASE and eQTLs. To capture these transcriptomic events, different methods have been developed to fully consider the features of RNA molecules of interest [153]. Standard RNA-seq has limitations for the study of these transcriptomic events, but the enormous sample sizes and rigorous computational tools already available maximize the discovery power. Nevertheless, several challenges remain (see Outstanding Questions). (i) How do we efficiently store and process sample sizes of huge magnitude? (ii) How do we develop accurate algorithms to identify and/or quantify transcriptomic events by increasing both sensitivity and specificity? (iii) How do we 832

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Outstanding Questions How do we efficiently store and process sample sizes of huge magnitude? How do we develop accurate algorithms to identify and/or quantify transcriptomic events by increasing both sensitivity and specificity? How do we design appropriate experimental approaches to capture these transcriptomic events? Will breakthroughs in sequencing technologies (e.g., long-read sequencing) overcome the current limitations? Can these methods be applied to the single-cell level to further reveal heterogeneity?

design appropriate experimental approaches (e.g., GRO-seq) to capture these transcriptomic events? (iv) Will breakthroughs in sequencing technologies (e.g., long-read sequencing) overcome the current limitations? (v) Can these methods be applied to the single-cell level to further reveal heterogeneity? Cloud computing, shared pools of configurable computer systems resources (e.g., The Seven Bridges Cancer Genomics Cloud [154]), will be a solution for processing massive transcriptomic data. Advanced algorithms, including deep learning [155] (e.g., multilayer perceptron, convolutional neural net, and long short-term memory) and Bayesian approach [156], may increase the sensitivity and specificity to identify transcriptomic events, even to the single-cell level. Appropriate design of RNA-seq protocols (e.g., 50 -end RNA-sequencing) [157] and long-read sequencing (e.g., nanopore sequencing) [157] will enable novel discovery of transcriptomic events, Using the approaches, we have reviewed and more, the research community has identified novel biomarkers and therapeutic targets at a pivotal juncture in the big data era. The field of genomics has been revolutionized to extend far beyond quantifying the expression of protein-coding genes. More importantly, the approaches herein reviewed, which were primarily developed for cancer research, can be applied to other complex diseases, thereby greatly leveraging their impact. Acknowledgments We regret that page limitations have prevented us from including all the relevant methods and related work in this review. This work was supported by the Cancer Prevention & Research Institute of Texas (RR150085 to L.H.). We gratefully acknowledge the consortium, including TCGA, and research groups that have provided accessible data. We thank LeeAnn Chastain for editorial assistance.

Resources i

https://www.ncbi.nlm.nih.gov/sra/

ii

https://cancergenome.nih.gov/

iii

https://dcc.icgc.org/

iv v

https://portals.broadinstitute.org/ccle

https://pecan.stjude.cloud/home

vi

https://www.encodeproject.org/

vii

https://pseudogene.org/

viii ix x

https://fantom.gsc.riken.jp/

www.enhanceratlas.org/

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xi

https://www.gencodegenes.org

xii

https://github.com/marvin-jens/find_circ

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https://github.com/YangLab/CIRCexplorer

xiv

https://sourceforge.net/projects/ciri/

xv

https://sourceforge.net/projects/ciri/files/CIRI-AS/

xvi

https://github.com/orzechoj/circRNA_finder

xvii

https://github.com/TreesLab/NCLscan

xviii xix xx

https://github.com/lindaszabo/KNIFE

https://sourceforge.net/projects/ptesfinder-v1/

http://genes.mit.edu/burgelab/miso/

xxi

http://www.biocomputing.it/3uss_server

xxii

https://github.com/morrislab/qapa

xxiii

https://github.com/ZhengXia/dapars

xxiv xxv

https://sourceforge.net/projects/apatrap/

https://github.com/arefeen/TAPAS

xxvi

https://sourceforge.net/projects/reditools/

xxvii

https://github.com/dieterich-lab/JACUSA

xxviii

https://github.com/ZhangLabSZ/RES-Scanner

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xxix

https://github.com/zhqingit/giremi

xxx

http://epigenomics.snu.ac.kr/RDDpred/

xxxi

https://www.ibp.ucla.edu/research/xiao/RASER.html

xxxii

https://github.com/BGIRED/RED-ML

xxxiii

http://rnaeditor.uni-frankfurt.de/

xxxiv xxxv

http://rnaedit.com/

http://srv00.recas.ba.infn.it/atlas/

xxxvi

http://software.broadinstitute.org/pathseq/index.html

xxxvii

http://odin.mdacc.tmc.edu/xsu1/VirusSeq.html

xxxviii

https://bioinfo.uth.edu/VirusFinder/

xxxix xl

https://github.com/danielfan/MuSE

http://varscan.sourceforge.net

xli

https://github.com/aradenbaugh/radia

xlii

https://github.com/alexischr/seurat

xliii

https://github.com/AstraZeneca-NGS/VarDict

xliv xlv

http://bioinformaticstools.mayo.edu/research/esnv-detect/

http://lilab.stanford.edu/SNPiR/readme

xlvi

http://gigadb.org/dataset/100360

xlvii

https://github.com/eturro/mmseq

xlviii xlix

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