Whole-Genome Sequencing in Cancerperspectivesinmedicine.cshlp.org/content/early/... · 5/28/2018 · Network2013),only6monthsaftertheﬁrsthu-man whole-genome sequence by next-genera-tion

Whole-Genome Sequencing in Cancer

Eric Y. Zhao, Martin Jones, and Steven J.M. Jones

Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver,British Columbia V5Z 4S6, Canada

Correspondence: [email protected]

Genome sequencing of cancer has fundamentally advanced our understanding of theunderlying biology of this disease, and more recently has provided approaches to character-ize and monitor tumors in the clinic, guiding and evaluating treatment. Although cancerresearch is relying more on whole-genome characterization, the clinical application ofgenomics is largely limited to targeted sequencing approaches, tailored to capture specificclinically relevant biomarkers. However, as sequencing costs reduce, and the tools to effec-tively analyze complex and large-scale data improve, the ability to effectively characterizewhole genomes at scale in a clinically relevant time frame is now being piloted. This abilityeffectively blurs the line between clinical cancer research and the clinicalmanagement of thedisease. This leads to a new paradigm in cancer management in which real-time analysis ofan individual’s disease can have a rapid and lasting impact on our understanding of howclinical practices need to change to exploit novel therapeutic rationales. In this article,wewilldiscuss howwhole-genome sequencing (WGS), often combinedwith transcriptome analysis,has been used to understand cancer and how this approach is uniquely positioned to providea comprehensive view of an evolving disease in response to therapy.

EARLY APPLICATION OF WHOLE-GENOMESEQUENCING IN ONCOLOGY

The application of whole-genome sequencing(WGS) to derive a more complete under-

standing of cancer has been a central goal ofcancer researchers since before the human ge-nome was first decoded in 2003 (Lander et al.2001). It would take a further 5 years and asea change in genome-sequencing technologybefore the first application of next-generationWGS to a cancer sample was described. Leyand colleagues reported the analysis of a cyto-genetically normal acute myeloid leukemia(AML) in 2008 (The Cancer Genome Atlas

Network 2013), only 6 months after the first hu-man whole-genome sequence by next-genera-tion technologies was published (Wheeler et al.2008). At that time, the bioinformatics toolsand genomic resources to facilitate the in-depthanalysis of whole-genome data could be con-sidered in their infancy compared with today’sstandards. Even so, the insights gained into boththe approach taken to sequencing the tumor andthe biology of the tumor itself were profoundwhen compared with the targeted sequencingapproaches commonly applied to cancer re-search at the time.

Today, the resources required forWGS anal-ysis have decreased substantially. Alongside a

Editors: W. Richard McCombie, Elaine R. Mardis, James A. Knowles, and John D. McPhersonAdditional Perspectives on Next-Generation Sequencing in Medicine available at www.perspectivesinmedicine.org

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steady reduction in the cost of WGS, there havebeen improvements in the technologies for gen-erating and processing quality raw data as well asthe tools and companion datasets that contex-tualize findings for biological and clinical inter-pretation. However, a majority of cancer geno-mics efforts remain focused around targeteddeep sequencing and whole-exome sequencing(WES) (Morris et al. 2017; Raphael et al. 2017).

LARGE-SCALE EFFORTS TO CHARACTERIZEGENOMIC EVENTS IN CANCER

Large-scale efforts using genome sequencing tocharacterize awide variety of adult and pediatriccancers began in earnest as early as 2005. Thisincluded projects such as The Cancer GenomeAtlas (TCGA; see cancergenome.nih.gov), theInternational Cancer Genome Consortium(ICGC; see icgc.org), Catalog of Somatic Muta-tions in Cancer (COSMIC; see cancer.sanger.ac.uk), and Therapeutically Applicable Researchto Generate Effective Treatments (TARGET;see ocg.cancer.gov/programs/target), to namebut a few. Not only have such efforts progressedour understanding of cancer as a genomic dis-ease, they also provide the data needed for de-veloping tools and resources that facilitate therapid detection and analysis of potentially rele-vant genomic events (Cerami et al. 2012; Gaoet al. 2013; Gonzalez-Perez et al. 2013; Rubio-Perez et al. 2015). However, because the bulk ofthe data produced is focused on the codingregion of the genome, the available data are un-derpowered to inform how untranslated, in-tronic, and intergenic regions might impact themolecular pathogenesis of disease (Nik-Zainalet al. 2016). In many cases, the data also lackcomprehensive clinical annotation, which is re-quired for linking genomic events to specific can-cer types, prognoses, and treatment responses(Robinson et al. 2017). Furthermore, themajorityof samples in these cohorts are from primaryuntreated disease and do not offer insight intohow tumors respond to often complex and dis-parate treatment regimens (Robinson et al. 2017).Additional cancer cohorts of samples from mul-tiple time points and biopsy sites that include richclinical information are therefore still required to

better define tumor biology and the relationshipto treatment history and response.

WHOLE-GENOME CHARACTERIZATIONOF CANCERS

The number of tumor whole-genome sequencesthat have been published and made publiclyavailable has steadily increased over the past10 years. These analyses have led to surprisinginsights into cancer biology, particularly fromthe analysis of structural variants (SVs) in tumorgenomes (Chong et al. 2017). They range inscope from characterization of cancer cell lines(Pleasance et al. 2010a,b) and N-of-One casereports with rich clinical detail (Ellis et al.2012), to ultradeep sequencing of a single tumorto uncover clonal heterogeneity (Griffith et al.2015). Larger scale efforts are also emerging,focused both at the characterization of the so-matic mutation, including noncoding and SVs(Banerji et al. 2012; Alexandrov et al. 2013b;Wang et al. 2014; Nik-Zainal et al. 2016) andgermline-specific analysis for the discovery ofpredisposing and pharmacogenomic informa-tion (Foley et al. 2015). For example, Nik-Zainalet al. (2016) sequenced the whole genomes of560 breast cancers, 260 supplemented with tran-scriptome sequencing. They showed how suchapproaches fill gaps in our understanding of thegenome between the exons and expand theknown repertoire of biological mechanisms un-derlying tumorigenesis with potential clinicaluse (Alexandrov et al. 2013b; Alexandrov andStratton 2014; Davies et al. 2017; Zolkind andUppaluri 2017).

WHOLE-GENOME CHARACTERIZATIONOF CANCERS IN THE CLINIC

The scope of sequencing and its applications hasalso broadened into the clinic. For specific can-cer types, the use of targeted genomic panels forboth germline susceptibility and known “action-able” somatic mutations is becoming routine inmany cancer centers (De Leeneer et al. 2011;Bosdet et al. 2013). The development and appli-cation of larger-scale gene panels is also seeingroutine use on a large number of patient samples

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(Zehir et al. 2017). These high-throughput ap-proaches drive discovery in the clinical contextand quantify the frequency and prevalence ofboth well-characterized and novel variants incancer-related genes. However, they capture atiny fraction of the genomic complexity thatcan exist in an individual tumor.

The first published description of an attemptto characterize a whole genome for a clinicalapplication was in 2010 (Jones et al. 2010). Inthis analysis, Jones et al. sequenced an adeno-carcinoma of the tongue, identifying genomicamplification and concurrent abundant expres-sion of the REToncogene as a potential driver ofthe disease. This analysis led to a personalizedtreatment approach for the patient using kinaseinhibitors targeting the RET protein. Subse-quent analysis of a posttreatment sample afterdisease progression provided comprehensive in-sight into how the tumor evolved to circumventthe treatment regimen in a way that a targetedapproach could not have achieved. The successof this study led to a pilot for the PersonalizedOncoGenomics clinical trial, now in its fifthyear, which aims to leverage whole-genomeanalysis with the intent-to-treat based on thegenomic information. Sequencing of the initial100 patients on this trial required developmentof pipelines and comprehensive interpreta-tion tools (Laskin et al. 2015) and provided theframework for a more unbiased approachto cancer type selection. One benefit of this ap-proach is in facilitating the sequencing of raretumor types thatmight not otherwise be profiledso extensively (Bose et al. 2015; Wrzeszczynskiet al. 2017). More recently, other whole-genomeclinical projects have been announced or areunderway (Wrzeszczynski et al. 2017) with per-haps themost ambitious project to date being the100,000 Genomes Project (see genomicsengland.co.uk), which is sequencing whole genomes forthe purpose of better understanding rare andinfectious diseases, and includes a common can-cer component.

SUMMARY

With an ongoing debate about the cost-effec-tiveness and analytical complexity of WGS for

characterizing cancer, the unique capabilities ofsuch an approach require consideration. Whatbenefits could these capabilities provide if intro-duced into standard clinical practice? This isparticularly timely given the insights over thepast few years into the potential clinical action-ability of mutation signatures that can elicitimmune responses in the presence of immunecheckpoint inhibitors (Rizvi et al. 2015). Here,we will highlight the unique opportunities(Fig. 1) for discovery from WGS, as they relateto the understanding of the underlying biologyof a tumor, the potential impact on drug discov-ery and success of clinical trials and how thosediscoveries can impact the treatment of an indi-vidual’s disease. We also consider the resourcesneeded to build the infrastructure for WGS inroutine clinical practice.

UNIQUE CAPABILITIES OF WGS

Toward a Digital Karyotype: High-ResolutionStructural Variation

Few images are more deeply enscribed in thehistory of human genetics than the karyotype,which emphasizes the functional importance ofgenomic organization. Over the years, a plethoraof technologies have enabled inspection of SVsand copy number variants (CNVs) in the ge-nome. Some, such as fluorescence in situ hybrid-ization, are precisely targeted, whereas others,like array-comparative genomic hybridization,are comprehensive at varying resolutions. WGSpromises to deliver precise and comprehensivecharacterization of both SVs and CNVs. Al-though many challenges stand in the way ofachieving a complete “digital karyotype” of can-cer, WGS has made dramatic advances towardthis goal.

The cancer genome frequently featurescomplex and interlocking patterns of somaticSVs, which expands the realm of possible can-cer-driving alterations. Since the discovery ofthe Philadelphia chromosome, the characteriza-tion of oncogenic fusions has been central tocancer diagnosis and treatment. Aside from fu-sions, SVs also modulate gene regulation by re-arranging the noncoding genome. Variants that


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impact the copy number or relative positioningof promoters and other regulatory elements canalter gene expression (Alaei-Mahabadi et al.2017). Last, the more recent discovery of com-plex SVs in cancer suggests that a broad reper-toire of variation exists, which has previouslygone unappreciated.

Paired-end WGS has become the standardfor comprehensively and precisely cataloguingSVs and CNVs. Although targeted arrays andWES can provide comparative read counts forCNV analysis, they lack the resolution to detectmicroamplifications and microdeletions andsuffer from sequencing depth bias. These chal-lenges, along with a need for computationalmethods to address them, limit the accuracy ofCNV calls and result in high false discovery rates(Zare et al. 2017).AlthoughWEScandetect genefusions (Chmielecki et al. 2013), itmisses fusions

affecting splice sites, promoters, and other func-tionally critical loci.

SV analysis methods fall broadly into fourcategories: read density, split reads, paired-end reads, and de novo assembly (Liu et al.2015; Tattini et al. 2015). Recent methods com-bine these strategies to capitalize on the uniquestrengths of each. For example, DELLY improvessensitivity by considering paired end reads whileenabling base-pair breakpoint calling precisionby examining split reads (Rausch et al. 2012). Apromising strategy, which has gained substantialrecent interest, is the use of genome-wide localassembly at candidate breakpoints (Chong et al.2017; Wala et al. 2017) to leverage some benefitsof de novo assembly at substantially lower com-putational cost.

A major challenge in digital karyotype con-struction is poor concordance between SVs and

Functional prediction

Small variants

Mutagenesis

Tumorbiology

Whole-genomesequencing

Genomicinterpretation

Genomic instability

CNV and LOH Structural variants

Mutation signatures Pathway integration

SOS2

NF1 NRAS

BRAF

MAP2K2MAP2K1

MAPK6

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CCCC

Therapeutic targets

Figure 1. Whole-genome sequencing (WGS) data reveal diverse forms of genomic alteration. Tumor genomesshow frequent mutation and genomic instability, which drive the hallmarks of cancer. WGS can catalog variousforms of genomic alteration, enabling integrative analyses of tumor biology.

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CNV breakpoints (Alaei-Mahabadi et al. 2017),which suggests mismatched accuracies andthresholds between modalities. This mismatchcauses further difficulty in reconstructing com-plex rearrangements such as chromothripsis(Stephens et al. 2011) and chromoplexy (Bacaet al. 2013). Without refined methods or costlyassembly-based approaches, clinically relevantcomplex mutations can be missed by conven-tional next-generation sequencing (NGS) meth-ods (Robertson et al. 2017). Graph-based meth-ods are an emerging approach, which may helpto align SV and CNV calls to better reconstructcomplex variants and even resolve the temporalordering of rearrangement events (Greenmanet al. 2012; Maciejowski and Imielinski 2016).

SVs and CNVs play important roles in thesomatic alteration of cancer genes and are bestcomprehensively characterized by WGS. Evolv-ing methodologies promise to move the fieldtoward increasingly accurate estimates of thedigital karyotype of cancer.

Genomic Instability

Genome instability and mutagenesis are hall-mark characteristics of cancers (Hanahan andWeinberg 2011). Endogenous and exogenousexposures coupled with failures of in-built DNArepair mechanisms give rise to rampant muta-tion and rearrangement. Total mutational bur-den (TMB) is a differentiating quality betweenand within tumor types. Analyses of somaticvariant frequencies across tumor-normal pairsreveal somatic mutation rates from 0.001 to1000 mutations per megabase (Alexandrov et al.2013a; Lawrence et al. 2013), which vary by can-cer type and individual tumor features. SVs alsoshow heterogeneous patterns of occurrence,with substantial differences in mutation burdenand distributions even between tumor subtypes(Waddell et al. 2015; Nik-Zainal et al. 2016).

The clinical implications of genomic insta-bility have received increased attention in recentyears. The advent of immunotherapy has bol-stered interest in TMB as a potential predictivebiomarker, especially as it relates to the emer-gence of candidate immunogenic neoantigens.Programmed death ligand 1 (PD-L1) inhibitor

immunotherapy response is also associated withmutagenesis arising from mismatch repair defi-ciency (MMRD), which has conventionallybeen detected by immunohistochemistry stain-ing of MMRD genes and sequencing to detectmicrosatellite instability (MSI) sites (Le et al.2015; Iyer et al. 2017). Homologous recombina-tion deficiency (HRD) is another source ofmutation with therapeutic promise. It involvesloss of BRCA1, BRCA2, RAD51, and other geneslinked to poly(ADP ribose) polymerase (PARP)inhibitor sensitivity (Robson et al. 2017).

The recent interest in genomic instability asa therapeutic target reflects substantial recentadvancement in the use of NGS to precisely cat-alog mutation. Beyond actionable drivers, WGScan detect somatic variants arising from specificmutagenicmechanisms. Because eachmutagen-ic source produces a characteristic mutationalpattern, unsupervised learning techniques candecipher signatures associated with distinctetiologies. Mutation signatures have also beenreferred to as “genomic scars,” a term suggestiveof lasting and detectable DNA damage. Thisapproach considers the somatic cancer genomeas a functional readout of its mutagenic expo-sures and the integrity of its DNA maintenanceand repair pathways (Helleday et al. 2014).

The discovery of mutation signatures ofsomatic single nucleotide variants (SNVs) hasrevealed the interacting roles of diverse carcino-genic etiologies. To date, 30 “consensus” sig-natures have been cataloged (see cancer.sanger.ac.uk/cosmic/signatures) thanks to large-scaleanalyses of publicly available cancer datasets(Alexandrov et al. 2013a). Although SNV signa-tures can be deciphered from targeted genesequencing (Zehir et al. 2017), broad coverageacross the whole genome provides the most pre-cise characterization (Alexandrov et al. 2013b).Signatures of SV have also been identified, whichrequire sensitive and specific SV capture and call-ing across the whole genome (Nik-Zainal et al.2016).Meanwhile, thedemand for genome-guid-ed cancer therapy has motivated the develop-ment of N-of-One approaches to mutation sig-nature characterization (Rosenthal et al. 2016).

Mutation signatures layer etiology-specificinformation atop TMB. The presence of signa-



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tures specific to certain cancer types (i.e., muta-tions associated with cigarette smoking, ultravi-olet radiation) may therefore help inform theclassification of cancers of uncertain primary.Mutation signatures have also begun to spurresearch into clinical applications. In the follow-ing subsections, we will describe two specificexamples of DNA repair defects associated withputative therapeutic implications: MMRD andHRD.

Mismatch Repair Deficiency

Mismatch repair (MMR) repairs erroneous basepairing and its deficiency produces cancer risk(Lynch syndrome) and distinct signatures ofmutation (Signature 6 and MSI). MMR repairsDNA lesions caused by the mispairing of bases,which can result from errors in replication ormutagenic DNA damage processes. Germlinemutations in MMR genes MSH2, MLH1,MSH6, and PMS2 give rise to Lynch syndrome.Genetic panel sequencing and immunohisto-chemistry are commonly used to assess the ge-notype and expression of these genes. MMRD isalso associated with MSI, the genome-wideshrinkage or expansion of short repetitive se-quences known as microsatellites. MSI is con-ventionally detected by polymerase chain reac-tion (PCR) amplification and electrophoresis atfive standard microsatellite regions to map thevariability in microsatellite length.

MMRD tumors are remarkable for theirimmunogenicity, possibly by expressing neoepi-topes resulting from base modifications. Thisfeature suggests a therapeutic link to immunecheckpoint inhibitors. Many recent studieshave shown improved checkpoint inhibitor re-sponse in patients with MMRD tumors acrossdiverse cancer types (Le et al. 2017). The abilityto assess both the genotypes andmolecular phe-notypes associated with MMRD is becoming acritical element of personalized tumor analysis.

NGS is informative both for genotypingMMR genes and quantifying their impacts ongenomic maintenance. Reads captured by NGScan be used to analyze microsatellite length dis-tributions. Some MSI detection strategies haveused targeted deep sequencing of clinically val-

idatedMSI loci (Gan et al. 2015), whereas othersscreen reads for MSI sites incidentally capturedduring sequencing (Niu et al. 2014; Salipanteet al. 2014). Both approaches yield resultssimilar to gold standard clinical PCR methods.Mutation signatures also provide insight intoMMR. Two out of 21 mutation signatures deci-phered from publicly available cancer genomesand exomes have been putatively linked toMMRD (Alexandrov et al. 2013a).

The analysis of MMR by NGS technologiesis becoming increasingly prevalent in precisiononcology initiatives. The recent MSK-IMPACTanalysis of 10,000 cancers by NGS found 102individuals with MMRD, concordantly classifiedby mutation signatures and MSI detection. Thiswork supported two recent conference abstractsreporting associations between MSI detected byNGS and response to immune checkpoint inhib-itors in urothelial and esophagogastric carcinoma(Iyer et al. 2017; Ku et al. 2017).

Homologous Recombination Deficiency

Homologous recombination (HR) refers to theexchange of similar or identical nucleotidesequences and has long been used as a labora-tory technique for site-directed mutagenesis.However, in cancer, HR facilitates error-freerepair of DNA double-strand breaks, as wellas inter- and intrastrand cross-links. The well-known cancer genes BRCA1 and BRCA2 cen-trally coordinate HR, and mutations in thesegenes confer an up to 85% lifetime risk of breastand ovarian cancer (see cancer.ca/en/cancer-information/cancer-101/what-is-a-risk-factor/genetic-risk/brca-gene-mutations and cancer.gov/cancertopics/factsheet/Risk/BRCA). OtherHR genes have also been associated with cancer,including ATM, RAD50, RAD51, PALB2,XRCC2/3, and the FANC gene family (Cerbin-skaite et al. 2012; Roy et al. 2012).

HRD has been identified as a promising tar-get for the administration of PARP inhibitors(Gelmon et al. 2011; Kaufman et al. 2013), aswell as DNA-damaging agents such as plati-num-based chemotherapy and anthracyclines(Kennedy et al. 2004; Farmer et al. 2005; Yanget al. 2011). This link is motivated by substantial

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recent evidence linking germline BRCA1 andBRCA2 variants with sensitivity to PARP inhib-itors and platinum-based chemotherapy inbreast and ovarian cancers (Byrski et al. 2010;Arun et al. 2011; Von Minckwitz et al. 2014;Sikov et al. 2015; Tutt et al. 2015).

The first mutation signatures of HRD dis-covered by NGS were characteristic large-scaleCNV patterns detected by hybrid-capturepanels with relatively evenly spaced probes.This approach yielded three quantifiable scores(HRD-LOH, HRD-TAI, and HRD-LST), whichcorrelated with BRCA1/2 mutation status(Timms et al. 2014) and response to platinum-containing neoadjuvant chemotherapy in pri-mary breast cancers (Telli et al. 2016). However,in the advanced breast cancer setting, HRDscores were not associated with platinumresponse based on the phase 3 TNT trial (Tuttet al. 2015).

More recently, the advent of SNV andSV mutation signature analyses has uncoveredadditional HRD-associated signatures (Alexan-drov et al. 2013b; Nik-Zainal et al. 2016). Over-lapping microhomology at deletions is anotherfeature observed in cancers withHRD (Stephenset al. 2012; Davies et al. 2017). By combiningSNV signatures, SV signatures, the HRD score,and overlapping microhomology, Davies et al.(2017) formulated a model capable of sensitiveand specific prediction of BRCA1/2 mutationstatus called HRDetect. This analysis improvesour understanding of the downstream muta-tional impacts of HRD and shows a clear linkwith its etiology. Moreover, a recent WGS studyin 93 advanced-stage breast cancers foundHRDetect to be associated with response to plat-inum-based chemotherapies (Zhao et al. 2017).

The detection of MMRD and HRD bothrepresent important advances in the under-standing of genomic instability and the applica-bility of NGS techniques. Although targetedsequencing and exome analysis have provensufficient for MMRD analysis, the latest HRDsignatures demand genome-wide capture ofSNVs, SVs, indels, and CNVs. WGS is well suit-ed for each of these tasks and can provide awholesale solution obviating the need for addi-tional assays.

Variation in Noncoding Regions

The Human Genome Project estimated thatprotein-coding exons comprise 1.2% of thehuman genome (Human Genome SequencingConsortium 2004). Interpreting the remain-ing noncoding DNA remains a challenge, butadvances in epigenetics have revealed exten-sive roles of DNA beyond protein coding. Find-ings from The Encyclopedia of DNA Elements(ENCODE) Project suggest that the majority ofthe genome is involved in biochemical interac-tions, such as protein binding or chromatin re-modeling (ENCODE Project Consortium 2012).

An example of the significance of intergenicDNA in cancer are variants in the promoterregion of telomerase reverse transcriptase(TERT), a gene central to the immortalizationof cancer cells (Horn et al. 2013; Huang et al.2013). These highly recurrent mutations are as-sociated with two- to fourfold increases of TERTtranscriptional activity and are found in multi-ple cancer types (Vinagre et al. 2013). Analysesfrom TCGA and others have identified addi-tional recurrent regulatory element mutationsin additional genes across multiple cancer types(Weinhold et al. 2014; Rheinbay et al. 2017),indicating that the number of relevant inter-genic events is likely to increase.

The ability to detect noncoding mutations,especially in regulatory regions, is a strength ofWGS. Large-scale efforts have produced a func-tional map of noncoding variation, which hasfacilitated the discovery of driver mutationsin regulatory regions of the genome. However,further research is necessary to facilitate the in-terpretation of novel noncoding variants foundby WGS in a personalized cancer medicinesetting.

Complementarity with the Transcriptome

Alongside advancements in WGS, RNA se-quencing technologies including whole-tran-scriptome sequencing (WTS) can provide com-plementary insights in a personalized medicinesetting. WTS enables genome-wide quantifica-tion of gene expression, which substantiallyexpands the capture of potentially actionable



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molecular aberrations. Substantial efforts existaiming to use tumor expression profiles to refinecancer diagnoses and subtyping. Translation ofthese efforts to interpretable and clinically ac-tionable parameters such as the widely usedPAM50 gene set in breast cancer (Chia et al.2012) can further advance these aims. The inte-gration of gene expression data into functionalclusters or pathways using statistical methodsor visualizations can help identify dysregulatedcancer pathways. This can provide rationale forguiding targeted cancer treatment, including theuse of experimental therapeutics (Tomasettiet al. 2017). When treatments fail, gene expres-sion analysis can also elucidate resistance mech-anisms and potentially suggest follow-up targets(Jones et al. 2010).

The existence and functional impacts ofmany events observed in the genome canbe further analyzed by WTS. Amplified anddeleted genes can be assessed for differentialexpression. The presence of oncogenic or dele-terious mutations on the expressed transcriptcan be confirmed. Exon skipping and intronretention can be identified and potentiallylinked to splice site variants. Transcriptome as-sembly can facilitate detection of potentially on-cogenic alternative transcripts. The presence ofoncogenic gene fusions can be confirmed, andtheir expression verified. The effects of promoterand enhancer mutations on gene expressioncan be quantified. Tumor suppressors such asBRCA1 can be assessed for potential epigeneticsilencing.

However, WTS brings its own limitations.Foremost among them is the uncertainty thattranscribed genes and variants will be faith-fully translated in proportional quantities. Com-parisons between RNA-level and protein-levelexpression frequently reveals weak, nonlinearcorrelations.WTSalsocannotcaptureposttrans-lational processing and modifications, whichhave significant functional impacts. Correlationof WGS with proteomics technologies may en-able further advances in personalized cancercare, but various technological, scientific, and lo-gistic obstaclesmustfirst be overcome to catalyzeclinical translation (Duarte and Spencer 2016;Kwon et al. 2016; Nice 2016).

Cancer Immunology

Genome sequencing has been at the heart ofrecent advances aiming to personalize cancerimmunotherapy. In particular, the joint analysisof human leukocyte antigen (HLA) sequenceand neoantigen repertoire holds promise forpredictive patient stratification. Computationalapproaches for HLA genotyping can be per-formed using WGS, WES, or RNA-seq data(Szolek et al. 2014). Neoantigens can be cata-loged based on peptide sequence modificationsprofiled by WGS or WES, and their bindingaffinity to major histocompatibility complex(MHC) proteins can be predicted usingmachinelearning–based methods (Jurtz et al. 2017). Al-though most such analyses are limited to exonicregions, whole-genome analysis can be valuablefor the discovery of novel features relevant totumor immunology. For example, WGS canexpand the profiling of neoantigens by incorpo-rating fusion antigens (Zhang et al. 2016).

Emerging Capabilities of WGS

The past decade’s explosion of short-read NGStechnology has spurred more recent develop-ments in long-read sequencing. Long-readsenable more reliable SV detection, as well asresolution of repeat-rich regions. Alternatively,linked-read technologies extend paired-end short-read sequencing by grouping multiple distantshort reads by molecule, which enables haplo-type phasing and improvements to SV detection(Spies et al. 2017) and de novo assembly (Jack-man et al. 2017). Both long-read and linked-readsequencing could substantially improve the res-olution of complex genomic events, moving to-ward precise and comprehensive cancer digitalkaryotypes.

The quantification of inter- and intratumorheterogeneity is also advancing toward potentialclinical translation. Actionable driver eventshave a wide range of recurrent clonal or subclo-nal fractions (McGranahan et al. 2015), whichsuggests the need to include clonality as adimension of clinical cancer sequence analysis.Moreover, the analysis of mutational clonalitycan indicate temporal shifts in the processes

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that drive somatic mutation. Andor et al. (2015)recently showed that tumor heterogeneity, de-tected byNGS, is correlated with increasedmor-tality. As clinical sequencing helps to formulatean improved understanding of events drivingmetastatic cancer (Robinson et al. 2017), analy-sis of tumor heterogeneity may eventually helpto predict drug resistance.

CONCLUDING REMARKS

The inherent genomic complexity of cancersgives rise to a range of genomic events and sig-natures that are becoming increasingly relevantin patient-treatment stratification. However,the expectation that an individual’s tumor willharbor previously described and functionallycharacterized genomic events is not certain.The successful clinical application of personal-ized genomic medicine therefore must rely onbroad screening approaches, a conclusion thatwas also reached in a study comparingWES and

WGS in gastric cancer (Wang et al. 2014). Awhole-genome approach is currently the mostefficient way to build a comprehensive pictureof the genomic variation in a tumor withoutresorting to multiple technical platforms. Thatbeing said, there are still significant challengesthat must be overcome before approaches suchas whole-genome and transcriptome analysis(WGTA) can be universally adopted. However,on the assumption that sequencing costs willfollow the historical downward trend, a moregradual uptake ofWGTA for more refined strat-ification and subtyping of rare tumors may beachievable in the short term. Furthermore, asunanticipated clinical successes from WGTAcontinue to permeate the field and the infra-structure to support such approaches mature, atransition away from targeted sequencing in theclinic is perhaps to be expected.

WGTA adds complexity to the analysis ofgenomic data, particularly given the potentialfor the discovery of novel variants that require

Clinical genomicknowledge base

Rapid clinical pipeline—automated informative and actionable events(defined by knowledge base)Integrated analysis pipeline—semi-automated-inferred informative and actionable events(manual interpretation)

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Figure 2. Amodel for whole-genome and transcriptome analysis (WGTA)-driven personalized genomics in thecancer clinic.



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additional investigation and interpretation. Up-to-date and comprehensive knowledge basesthat document current and emerging clinicallyrelevant genomic information are thereforeessential if identification and stratification ofgenomic results is to be achieved in a clinicallyrelevant time frame across a broad range of clin-ical pipelines. To this end, a number of knowl-edge bases have been established in recent years,which attempt to collate, summarize, and serveup relevant genomic-therapeutic associations(for review, see Kumar-Sinha and Chinnaiyan2018). Computational approaches leveragingnatural language processing for capturing andcurating new and existing knowledge are likelyto be successful in increasing the efficiency ofthese efforts (Yim et al. 2016). Finally, algo-rithms that can reliably infer the potentialrelevance of novel genomic events and thenlink these events to hypothetical or inferred clin-ical action with limited manual interventionare needed. Their development will be criticalfor reducing analysis and interpretation costs,shortening turnaround time, and promotingwidespread adoption of multi-omic approachesin the clinic.

For some forward-thinking jurisdictions,collating the complete genomic informationfor tumors coupled with extensive clinical infor-mation will provide an unprecedented researchplatform to understand the mechanisms under-lying therapeutic response, acquired resistance,and failure. Likewise, we envisage that WGTAcould be undertaken many times during thecourse of the disease, serially analyzing thechanges accrued during various stages oftreatment providing a real-time view of cancerprogression and treatment response. Such stud-ies will generate a feedback loop, which will beinvaluable for the study of cancers as they relateto improved disease stratification and therapeu-tic intervention. Ultimately, clinical annotationof the therapeutic action taken, and outcomeobserved, would feed into large-scale analysesin the preclinical setting to further the under-standing of the biological processes drivingcancer and identifying rational biomarkers forcancer diagnostics and new drug development(Fig. 2).

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published online May 29, 2018Cold Spring Harb Perspect Med Eric Y. Zhao, Martin Jones and Steven J.M. Jones Whole-Genome Sequencing in Cancer

Subject Collection Next-Generation Sequencing in Medicine

Next-Generation Sequencing Technologies

Elaine R. MardisW. Richard McCombie, John D. McPherson and Sequencing

Single-Cell Applications of Next-Generation

Martelotto, et al.Naishitha Anaparthy, Yu-Jui Ho, Luciano

Cancer Genomics: From Discovery to ClinicThe Impact of Next-Generation Sequencing on

Elaine R. MardisNext-Generation SequencingFuture Promises and Concerns of Ubiquitous

W. Richard McCombie and John D. McPherson

DisorderNext-Generation Sequencing in Autism Spectrum

Stephan J. Sanders

Next-Generation Sequencing StrategiesShawn E. Levy and Braden E. Boone

Worldview of the EpigenomeSequencing in High Definition Drives a Changing

Emily Hodges

Characterizing the Cancer Genome in BloodSarah-Jane Dawson

Whole-Genome Sequencing in CancerEric Y. Zhao, Martin Jones and Steven J.M. Jones Pharmacogenetics and Pharmacogenomics

The Role of Next-Generation Sequencing in

Ute I. Schwarz, Markus Gulilat and Richard B. Kim

Disease RiskHigh Throughput Sequencing and Assessing

Shannon M. Rego and Michael P. Snyder DisabilityResearch and Diagnostics for Intellectual The Use of Next-Generation Sequencing for

et al.Ricardo Harripaul, Abdul Noor, Muhammad Ayub,

Clinical Versus Research SequencingYuriy Shevchenko and Sherri Bale Results

Next-Generation Sequencing and the Return of

Sénécal, et al.Bartha Maria Knoppers, Minh Thu Nguyen, Karine

http://perspectivesinmedicine.cshlp.org/cgi/collection/ For additional articles in this collection, see

Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved


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