CCR FOCUS - Clinical Cancer Research · therapeutic stratiﬁcation in the modern war against cancer. Clin Cancer Res; 23(11); 2617–29. 2017 AACR. See all articles in this CCR Focus

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Mutational Signatures in Breast Cancer:The Problem at the DNA LevelSerena Nik-Zainal1,2 and Sandro Morganella1

Abstract

A breast cancer genome is a record of the historic mutagenicactivity that has occurred throughout the development of thetumor. Indeed, every mutation may be informative. Althoughdriver mutations were the main focus of cancer research for along time, passenger mutational signatures, the imprints ofDNA damage and DNA repair processes that have been oper-ative during tumorigenesis, are also biologically illuminating.This review is a chronicle of how the concept of mutationalsignatures arose and brings the reader up-to-date on this field,particularly in breast cancer. Mutational signatures have now

been advanced to include mutational processes that involverearrangements, and novel cancer biological insights have beengained through studying these in great detail. Furthermore,there are efforts to take this field into the clinical sphere. Ifvalidated, mutational signatures could thus form an additionalweapon in the arsenal of cancer precision diagnostics andtherapeutic stratification in the modern war against cancer.Clin Cancer Res; 23(11); 2617–29. �2017 AACR.

See all articles in this CCR Focus section, "Breast CancerResearch: From Base Pairs to Populations."

Introduction: Breast Cancer Genomics—Access All Areas

The central tenet of cancer research has for decades been theidentification of somatic driver mutations that are causally impli-cated in tumorigenesis (1). Thus, a host of breast cancer driverevents are now known (2), including copy number aberrations(3–8), such as the ERBB2 and CCND1 amplification loci andhomozygousdeletionsofCDKN2A/BandPTEN, andhigh-frequen-cy substitution and insertion/deletion (indel) driver mutations incancer genes like TP53 (�frequency 53%), PIK3CA (8%–26%),CDH1 (21%),AKT1 (8%), andGATA3 (4%; refs. 9–12). Separately,extensive germline exploration has led to documentation of rare,high-penetrance (BRCA1, BRCA2, TP53; refs. 13, 14), moderatepenetrance (PTEN, STK11, CDH1, ATM, CHEK2, BRIP1, PALB2;refs. 15–19), and common, low-penetrance risk alleles (20–24) fordeveloping breast cancer (25). Essentially, enormous efforts havebeen placed on breast cancer classification based on somatic andgermline mutation information, histopathologic markers, copynumber, and expression profiles (9, 26, 27)—all aimed at improv-ing diagnostic, prognostic, and therapeutic stratification.

Whenmassive parallel sequencing arrived in the late 2000s (28),the increase in the speed of sequencingwas of orders ofmagnitude,permitting access to large swathes of the human genome notpreviously accessible at a reasonable cost. In a striking testamentto this technology, five back-to-back breast cancer articles were

published in 2012 (9–12), providing a thorough view of themolecular foundations of breast cancer and saturating driver dis-covery in coding sequences (29).Quite apart from themere handfulof driver mutations present in each tumor, modern sequencingtechnologies enabled us to access themany thousands of passengermutations present in each cancer as well. Herein lies a significantrealization—that passenger mutations are not simply randommanifestations or mutational debris—they represent the scars ofbiologicalprocesses thathavegoneawryduring cancerdevelopmentand are, therefore, a rich historical record of tumorigenesis (30).

Mutational Signatures: Making Sense ofthe Mayhem

The followingmodelwas previously proposed: At the point of apatient's cancer diagnosis, the set of somatic mutations revealedthrough sequencing of the tumor is the aggregate outcome of oneor more mutational processes (30–32). Each process, defined bythe mechanisms of DNA damage and DNA repair that constituteit, leaves a characteristic imprint or mutational signature on thecancer genome (Fig. 1). The final catalog of mutations is alsodetermined by the intensity and duration of exposure to eachmutational process (Fig. 1). Some may be weak or moderate intheir intensity, whereas others may be very strong in their asser-tion. In addition, some exposures may be ongoing through theentire lifetime of the patient, even preceding the formation of thecancer, and some may commence late or become dominant laterin tumorigenesis (Fig. 1). Furthermore, cancers comprise subclo-nal populations, which may be variably exposed to each muta-tional process (33, 34), promoting complexity of the final land-scape of somatic mutations in a cancer genome.

Base Substitution Mutational Signatures inBreast Cancer

In 2012, the 183,016 substitutions present in 21 whole breastcancer genomes were used in a proof-of-principle exercise todemonstrate the existence of mutational signatures (30, 33).Critically, sequence context immediately 50 and 30 to each

1Wellcome Trust Sanger Institute, Hinxton Genome Campus, Cambridge, UnitedKingdom. 2East Anglian Medical Genetics Service, Cambridge University Hospi-tals NHS Foundation Trust, Cambridge, United Kingdom.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author: Serena Nik-Zainal, Wellcome Trust Sanger Institute,Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom.Phone: 0044-1223-834244; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-16-2810

�2017 American Association for Cancer Research.

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mutated base was taken into consideration in classifying eachsubstitution. As there are six classes of base substitution and 16possible sequence contexts for eachmutated base (A, C, G, or T atthe 50 base and A, C, G, or T at the 30 base), there are 96 possiblemutated trinucleotides for each tumor. Various mathematicalmethods were explored and finally, nonnegative matrix factori-zation was used to extract five substitution signatures present inthese tumors (signatures A–E, nowknownas signatures 1B, 2, 3, 8,and 13; refs. 30, 33; Fig. 2).

Subsequently, a methods article (35) and a landmark article(32) were published where this mathematical approach wasapplied across 30 cancer types involving 7,042 samples [507whole-genome sequencing (WGS) and 6,535 whole-exomesequencing (WES)] and revealed 21 substitution signatures alto-gether (http://cancer.sanger.ac.uk/cosmic/signatures). The num-ber of breast cancers available for analysis had increased consid-erably to 100 WGS and 800 WES tumors. Reassuringly, the same

five substitution signatures that were recognized previously wereconsistently identified in this larger dataset (30, 32), reinforcingconviction in the concept of mutational signatures and in themethods applied to extract them.

In a recent endeavor exploring 560WGSbreast tumors (36), thelargest cohort ofWGS cancers of a single tissue type to date, a totalof 12 substitution signatures were identified from 3,479,652mutations (Fig. 2A). This may superficially appear to be a sub-stantial surge in signature discovery in breast tumors. On closeinspection, many of the new signatures are relatively rare, presentin few samples (36). Thus, in a similar paradigm to that of drivers,wehave likely saturated thediscovery of high-frequency, commonmutational signatures in breast cancer. Sequencing further pri-mary breast tumors is unlikely to yield new,major signatures. Theincrease in power possibly permits disambiguation of closelycorrelated signatures. Signatures 1 and 5, hitherto classified assignature 1B, were only just separated by this analysis. Many

© 2017 American Association for Cancer Research

Multiple mutational processesadded together

A. Mutational process happening in allnormal cells throughout life

D. Mutational process due toacquired DNA repair defect

More likely to appear as “clonal”mutations

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to the cell becoming a malignant cell

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B. Mutational process dueto occupational exposure

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Figure 1.

Somatic mutational processes in human cancer. Each mutational process leaves a characteristic imprint, or mutational signature, on the cancer genome, comprisingDNA damage and DNA repair components. The arrows indicate the duration and intensity of exposure to a specific mutational process. The amount ofexposure to each mutational process could vary from one person to another. Mutational processes A, B, C, and D represent hypothetical mutational processes thathave occurred through the lifetime of the developing tumor. A could represent a normal mutational process that happens in all our cells (including normal cells),hence it is occurring in a small amount throughout life. B could represent a mutational process caused by an environmental insult, such as an occupationalexposure to a carcinogen. C could represent a mutational process which occurs in bursts through tumorigenesis such as intermitted exposure to a chemical or to anintermittent disease process. D could represent the acquisition of a defect in a gene involved in normal DNA repair. The final mutational portrait is a compositeof all the mutational processes that have been active over the lifetime of the cancer patient. A different patient could have all of these mutational processesoccurring in their tumor or could have some of the same mutational processes as well as other mutational processes present.

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http://cancer.sanger.ac.uk/cosmic/signatures


different algorithms are available today for mutation signatureextraction (37–41)—some may reveal 11 (with signature 1B) or12 signatures (signatures 1 and 5) from this dataset, or withmorerelaxation of parameters, even 13 (Supplementary Data). Regard-less, that five signatures were consistently seen when as few as 21samples were studied reveals that these early signatures are robustand common, and report ubiquitously present mutagenic pro-cesses in breast cells.

Of the 12 signatures now documented in breast cancer(ref. 36; Fig. 2A), signature 1B or signatures 1 and 5 are associatedwith age of diagnosis; signatures 2 and 13 are associated with theactivity of the APOBEC cytidine deaminases; signature 3 is asso-ciated with BRCA1/BRCA2 deficiency; signature 8 appears to beincreased in tumorswithBRCA1/BRCA2deficiency, although alsopresent at lower levels in other tumors; signatures 6, 20, and26 areassociatedwithmismatch repair deficiency; and signatures 17, 18,and 30 are of unknown etiology (36). Of note, these mutationalsignatures do not appear to demonstrate specificity to breastcancer subtype whether classified by estrogen receptor (ER) statusor other systems such as PAM50 or AIMS.

Most breast tumors have less than 20,000 substitutions in total(less than 6.5 mutations per Mb; Fig. 2B). Only a handful ofsamples have a very large number of mutations (up to 94,000substitutions; Fig. 2B). Irrespective of mutation burden, the vastmajority of samples comprise multiple mutational signatures(36). A subset of samples may be composed predominantly ofspecific signatures, and may even be overwhelmed by a very largenumber of mutations from these signatures and termed "hyper-mutators" (42). This trait is associated with certain mutationalprocesses: signatures 2, 13, 6, 20, 26, and17 in breast cancers (36).Indeed, someof these signatures (signatures 8, 13, and 17) appearto dominate later in breast tumorigenesis (43), observed latterlyin cancer evolution (33, 36) and in metastatic disease (44).Perhaps, in time, these associations will be definitively verifiedas harbingers of poorer outcomes.

It was previously observed that substitution signatures hadparticular relationships with classes of indels (30). Patients withgermline BRCA1/BRCA2 mutations exhibited an excess of largerindels (>3 bp) with microhomology present at breakpoint junc-tions (30). Moreover, tumors with signature 6, 20, or 26, whichare associated with mismatch repair deficiency, have a largenumber of indels at polynucleotide repeat tracts, consistent witha label of microsatellite instability in these cancers (32, 36). Thus,correlations are observable between substitution signatures andcrude indel patterns.

Advancing the Frameworks of MutationalSignatures

Mutational processes in human somatic cells are not restrictedto producing base substitutions. Indeed, DNA damage and DNArepair processes can generate patterns of indels and large-scalechromosomal aberrations or structural variation as well (31).Thus, the basic premise of mutational signatures was recentlyextended to structural variation in breast cancer (36).

Genomic instability is a broad concept that encompasses awiderange of chromosomal level abnormalities. Some tumors have alarge number of rearrangements (several hundred) that arefocused or "clustered" at specific loci reporting driver amplicons(e.g., CCND1, ERBB2) or are simply sites of chromothripsis (45),for example. In contrast, other tumors could have an equivalent

number of rearrangements but have them widely distributedthroughout the genome instead. Intuitively, different mutationalprocesses are likely to underpin these disparate genomic out-comes (36).

Rearrangements were thus separated according to whether theywere clustered or dispersed (Fig. 3A), and then by rearrangementclass (tandem duplication, deletion, inversion, or translocation;Fig. 3B) and by size (36). Following this classification, we appliedthe same mathematical framework, as described previously, andextracted six rearrangement signatures (RS; ref. 36; Fig. 3C). Thisexercise of defining rearrangement signatures was not simplyacademic—unsupervised hierarchical clustering yielded sevenmajor subgroups (groups A–G) that exhibited distinct associa-tionswith other genomic, histologic, gene expression, and clinicalfeatures (ref. 36; Fig. 4).

Three of the signatures are featured in homologous recombi-nation (HR)–deficient tumors: RS1, dominated by long (>100kb)tandem duplications, characterized many HR-deficient tumorsbut defined group F tumors associated with older age of diagnosisand poorer outcome in this small cohort; RS3, characterized byshort (<10 kb) tandem duplications was specific to BRCA1-mutant tumors (group D); whereas RS5, defined by deletions(<10kb), are present inBRCA1- andBRCA2-deficient samples andtypified group G BRCA2-mutated samples (36). Hence, we wereable to differentiate BRCA1- from BRCA2-null tumors, as well as aBRCA-like (but different) cohort with distinct clinical features(36). These diverse groups would have simply been labeled ashaving "genomic instability" in the past and been indistinguish-able (Fig. 4).

Of the remaining rearrangement signatures, RS2, characterizedby large (>100 kb) nonclustered deletions, inversions, and inter-chromosomal translocations, defined group E ER-positive tumors(36). In contrast, RS4 and RS6 were both characterized by clus-tered rearrangements and were enriched in groups A, B, and C,which were of mixed ER status but frequently had large driveramplicons, for example, ERBB2 and CCND1 (36).

Remarkably, deep analysis of individual rearrangement signa-tures has unearthed a novel, if somewhat disturbing, biologicalinsight. Very recently, 33 loci were identified as sites that arerearranged by long RS1 tandem duplications more frequentlythan expected in independent tumors from different patients,even if by only a single tandem duplication (46). Interestingly,these hotspots are enriched for breast cancer germline suscepti-bility loci, breast-specific super-enhancer regulatory elements,and oncogenes (46). These loci have high transcriptional activityin breast tissue and are susceptible to double-strand break (DSB)damage and, following DSB repair, to formation of rearrange-ments. Yet, not all classes of rearrangements are represented atthese sites—only long RS1 tandem duplications. It was hypoth-esized that long tandemduplications aremore likely to effectivelyincreasewhole copies of these regulatory elements/genes, and thatthis could confer some degree of secondary selective pressure,even if incrementally (46). Indeed, corroborative transcriptomicevidence was observed to support this postulate, providing adevastating insight into this mutational process of HR deficiency:It may commence as a passenger mutational signature but,unwittingly, creates secondary driver events. RS1 is, therefore, aparticularly deleterious genetic mechanism—an injurious muta-tional signature that perpetuates carcinogenesis (46).

This field of rearrangement signatures may only be in itsinfancy, but a number of deep messages are appearing, although

Mutational Signatures in Breast Cancer

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AssociationsA

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0.06

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at lower levels in many tumors

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Unknown

Unknown

Unknown

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<20% of samples

Rare <5% ofsamples

Gastric

Cervical, gastric,uterine

Esophageal, liver, lung,lymphoma, gastric

Adrenocortical,neuroblastoma, gastric

Osteosarcoma



Many tumor types

Many tumor types

Many tumor types

Adrenocortical, colon,uterine, ovarian, pancreas

More than half of all tumortypes examined so far


Reported to be in manyother tumor types, but not

consistently


Reported in nearlyall other tumor types

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clinical significance requires further evaluation. An exciting futureawaits as the fieldmatures and other tissue types are incorporatedinto these analyses.

Localized Mutational SignaturesThe substitution signatures described thus far report muta-

genesis distributed throughout the human genome. Intriguing-ly, localized mutagenesis has also been reported (30). Bycalculating an intermutation distance, or the distance from asubstitution to the one immediately preceding it in the refer-ence genome, we were able to appreciate focal substitutionhypermutation (30). Although most mutations in a cancergenome would exhibit an intermutation distance of approxi-mately 105 bp to approximately 106 bp, localized regions ofhypermutation or "kataegis" presented as clusters of substitu-tions with shorter intermutation distances (defined as six ormore substitutions with an average intermutation distance of<1,000 bp; refs. 30, 32, 36). These focal mutation showers hadstriking characteristics—an excess of cytosine mutations at aTpC sequence context and colocalizing with a different class ofmutation altogether, rearrangements.

Kataegis mutations bear a strong resemblance to those ofgenome-wide signatures 2 and 13, which are associated withAPOBEC enzymatic activity (47–49). APOBECs are a family ofcytidine deaminases that evolved to restrict retroviruses andretrotransposon elements. APOBECs require single-strandedDNA (ssDNA) as a substrate for deamination of cytosine touracil. Notably, experimental studies in yeast suggest that DSBsand end resection are a source of ssDNA required for APOBECsto generate kataegis (47). In contrast, alternative cellular pro-cesses such as replication or transcription have been hypoth-esized as a potential fount of ssDNA for APOBEC activity–generating signatures 2 and 13 (31). Thus, although APOBECenzymes are involved in kataegis, and genome-wide signatures2 and 13, they are believed to be mechanistically distinctmutational processes likely arising at different instances ofcellular stress (Fig. 5).

Interestingly, an alternative form of kataegis was also rarelyobserved (0.9% of all kataegis foci identified in breast cancer;ref. 36). Also colocalizing with rearrangements, this version ofkataegis exhibited a different base substitution pattern of T>GandT>C mutations predominantly at NTT and NTA sequences. Theetiology of this form of kataegis is unknown.

Dynamic Cellular Processes and MutationalSignatures

Thedistributionof somaticmutations is uneven through cancergenomes, has been extensively studied, and has been found to belargely influenced by replication time domains and histone epi-

genetic marks (50, 51). Predicated on being able to probabilis-tically assign everymutation in a cancer to amutational signature,similar analyses have now been performed as mutational signa-tures (36). Because mutational signatures are proxies for specificbiological processes, the advantage of performing these analysesas mutational signatures is that one can interpret the influenceof dynamic cellular events, such as replication, transcription, andnucleosome occupancy, on the associated biological processes(ref. 36; Table 1).

For example, one of the most noteworthy insights obtainedfrom this analysis was the degree of asymmetry observed betweenreplication strands for particular signatures. For approximately100,000 mutations on the leading replicative strand, approxi-mately 140,000 mutations were observed on the lagging strandspecifically for APOBEC-related signatures 2 and 13 (36). Thislevel of asymmetry implies that replication has amechanistic rolein the generation of signatures 2 and 13 (Fig. 5). APOBECsdemand ssDNA as a deamination substrate, and replication is aperfect physiologic source of ssDNA. Indeed, in 2016, four otherpublications supported this observation through in vivo (52, 53)and in vitro (54, 55) studies. Replication strand asymmetry wasalso observed for signature 26 (36), one of the four mutationalsignatures associated with deficiency of mismatch repair. Hadthese analyses been performed on all mutations combined, thespecific behaviors (Table 1) would not have been appreciable—the signal diluted by aggregation. Thus, these vignettes demon-strate the value of performing analyses as mutational signatures.

Ultimately, a profound theme has crystallized. Differentsignatures exhibit different relationships with replication, tran-scription, and chromatin organization, fortifying how muta-tional signatures must be true biological phenomena and arenot simply theoretical, mathematical constructs.

In the Midst of Chaos, Lies OpportunitySome mutational signatures are a direct pathophysiologic read-

out of the abrogation of a DNA repair gene/pathway and could beused as a biomarker to reportDNA repair deficiency in a tumor (31,56). Somatic nullness of a single gene, such as BRCA1, however,does not simply produce one mutational signature; it produces amultitude of mutational patterns (36). On one hand, this compli-cates an already burdened mutational landscape. Conversely, thiscould be used to our advantage for potential clinical applications.

Very recently, a supervised Lasso logistic regression model wasused to learn the multiple substitution, indel, and rearrangementmutational signatures that distinguish germline BRCA1/BRCA2–mutated cancers from sporadic tumors (57). Six mutationalpatterns were found to be discriminatory and were weighted tocreate a mutational signature–based predictor of BRCA1/BRCA2deficiency called HRDetect (57).

Figure 2.Currently known extracted substitution mutational signatures in human breast cancers. A, Table of 12 mutational signatures extracted using nonnegativematrix factorization. Each signature is ordered by mutation class (C>A/G>T, C>G/G>C, C>T/G>A, T>A/A>T, T>C/A>G, T>G/A>C), taking immediate flankingsequence into account, resulting in 96 triplets. For each class,mutations are orderedby50 base (A, C, G, T)first, before 30 base (A, C, G, T).Y-axis reports theprobabilityof a signature generating each of the 96 triplets. Signature extraction was performed separately in 17 cancer types. The bars report the results of the extractionon the 560 breast cancers (37) using a widely available algorithm using simply default parameters (38), and the error bars demonstrate the variability (of thepresumptive same signatures) between cancers of different tissue types. The table also contains the associated etiologies of each signature, the prevalenceof these signatures in breast cancer, andwhether the signature is also seen in other tumor types. HR, homologous recombination.B,Absolute numbers ofmutations ofeach signature in each sample (top) andproportionof each signature in each sample (bottom). PanelB reprintedbypermission fromMacmillanPublishers Ltd.: Nature534:47–54, copyright 2016.






Tumor with many clusteredrearrangements

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HRDetect outperforms customary copy number–basedapproaches (refs. 58–60; e.g., HRD index) for detectingBRCA1/BRCA2 deficiency and any individual signature on itsown (HRDetect AUC ¼ 0.98). This is unsurprising, as a predictorthat hunts for some combination of many signatures would bemore sensitive and specific than a predictor that is dependent ononly a single signal (57). Thus, HRDetect works extraordinarilywell even in situations of reduced mutation information second-ary to low tumor cellularity, low sequencing depth (e.g., lowcoverage WGS sequencing of �10-fold rather than 30-fold), orincreased noise (e.g., in cancer specimens that have artefactualgenetic changes arising from formalin fixation; ref. 57). Thisobservation could have immediate potential applications.

Of particular clinical importance, HRDetect revealed a largerproportion of patients with BRCA1/BRCA2 deficiency thanexpected, of up to 22%, that is, many more than the 3.9% ofgermline mutation carriers that were knowingly recruited to thestudy (57). More than half of these tumors would not have beendetected asBRCA1/BRCA2null using targeted sequencing of thesegenes alone. BRCA1/BRCA2–null tumors are selectively sensitiveto compounds such as PARP inhibitors (61–64), which arecurrently theoretically reserved for approximately 1% to 5% ofthe germline mutation carriers. Profoundly, if in fact one in everyfive breast cancer patients has the equivalent of a BRCA1/BRCA2–null tumor, could they be similarly selectively sensitive to PARPinhibitors? This is unknown, and it is nownecessary to embark onexperiments and/or clinical trials to seek conclusive evidence. Themessage to the community is this: We need clinical trials of drugslike PARP inhibitors, which are not restricted to germline muta-tion carriers, and are applied to sporadic breast and possibly othertumors in the general populace.

Beyond that of driver mutations, mutational signatures couldcontribute a powerful, additional spoke in the wheel of cancerdiagnostics and therapeutic stratification. There is likely to bescope for identifying other pathophysiologic processes with sen-sitivities to different therapies (e.g., replication stress with WEE1/ATR inhibitors or perhaps stratifying sensitivity to immunothera-pies; refs. 65, 66). The academic abstraction of mutational sig-natures takes a step closer toward the clinic.

Critical Dissection of the MutationalSignatures Concept

Although it is a fast-paced and exciting field, the mutationalsignatures model does warrant critical scrutiny. No matter howsophisticated the analyses of in vivomutagenesis, there are limita-tions to studying tumors—it is an uncontrolled and noisy system,

and even the best clinical metadata collections will, at best,provide associations.

First, we acknowledge that the model requires validationExperiments that show how different signatures can be gener-

ated by different exposures will contribute toward reinforcing theconcept. The field of environmental mutagenesis (67–71) willargue that historic TP53 and HPRT reporter assays, and experi-ments exposing mouse embryonic fibroblasts to external expo-sures (34), such as ultraviolet light and tobacco carcinogens,already provide evidence that mutations generated through exo-genous exposures generate mutation patterns that are similar tothose observed in human cancers. However, there have beenlimited efforts to demonstrate similarly clear relationships forendogenous mutational processes. Perhaps, systematic surveys ofmutational signatures of DNA-damaging agents and from abro-gation of DNA repair genes will be required to truly convince thescientific community that mutational signatures observed inhuman cancers arise from both external and internal sources ofDNA damage and DNA repair. Experimental evidence showingthat the amount of exposure (whether to a chemical compoundorendogenous exposure) is correlated with the degree of mutagen-esis will also help to strengthen conviction in thismodel. Thefinaldemonstration of being able to turn on a signature (through geneknockout) and turn it off again (through reversing the mutation)could definitively authenticate this model.

Second, what is the mathematical rigor of this concept?The principle of factorizing or reducing a complex, multidi-

mensional dataset into simpler parts is not unusual. Multipledifferent mathematical methods have been developed for pre-cisely this purpose (37–41). Although showing striking similarity,the results obtained through these different methods are notidentical (Supplementary Data, Supplementary Figs. S1–S3). Thishas raised concerns regarding reproducibility.

There are signatures that are staunchly similar, for example, thesignatures related to the activity of APOBEC enzymes (signatures2 and 13), which are pervasive, robust across algorithms (Sup-plementary Data, Supplementary Fig. S3), and undisputed asmutational signatures in human tumors. Related signatures areadmittedly sometimes less clearly distinguishable (Supplemen-tary Note, Supplementary Fig. S3). Various post hoc processingmethods are reported to be used to tease these apart, and these doresult in differences in the final extracted signatures. For example,signatures 6, 20, and 26 (all related tomismatch repair deficiency)are historically more difficult to disentangle because of common-alities in their 96-element profile. That they are more challenging

Figure 3.Extracting rearrangementmutational signatures in humanbreast cancers.A,Whole genomeCircos plotswere adapted from theRCircos package. Features depictedin Circos plots from outermost rings heading inwards: Karyotypic ideogram outermost. Base substitutions next, plotted as rainfall plots (log10 intermutationdistance on radial axis, dot colors: blue¼C>A; black¼C>G; red¼C>T; gray¼ T>A; green¼ T>C; pink¼ T>G). Ringwith short green lines¼ insertions; ringwith shortred lines ¼ deletions. Major copy number allele ring (green ¼ gain), minor copy number allele ring (pink ¼ loss); central lines represent rearrangements(green ¼ tandem duplications; pink ¼ deletions; blue ¼ inversions; and gray ¼ interchromosomal events). Note the difference in the nature of the distribution ofrearrangements between the two tumors depicted. The whole genome profile on the left has >300 rearrangements that are clustered at distinct loci inspecific chromosomes. In contrast, the >300 rearrangements present in the profile on the right-hand side are uniformly dispersed through the genome. Themutational processes underpinning the differing distributions in these two tumors are most likely to be different. Thus, separating rearrangements into whetherthey are clustered or dispersed represents a first step in the rearrangement classification. B, Types of rearrangements that can be ascertained easily. Thehypothetical pieces of reference DNA from two different chromosomes on the left can be rearranged to form four main classes of rearrangements, as shown on theright. This is a second step in the classification of rearrangements prior to rearrangement signature extraction. The rearrangements are also divided by sizebefore extraction.C,Six rearrangement signatures extracted using nonnegativematrix factorization. Probability of rearrangement element ony-axis. Rearrangementsize on x-axis. Chr, chromosome; Del, deletion; Inv, inversion; Tds, tandem duplication; Trans, translocation.





to disambiguate fromone another does notmean that they donotexist, of course, and may even reflect biological interactionsbetween them.

The assignment of the amount of each signature present inindividual tumors is also a source of variation between algo-rithms. Invariably, these algorithms assign a small proportion ofevery signature to every sample examined. This is unlikely to bebiologically true, so penalties may or have been introduced toincrease the "sparsity" of mutation assignments, resulting invariation in final signature contributions to individual samples.Thus, mutational signature extraction and the assignments ofthese signatures are currently not fully deterministic. Of course,balance needs to be struck between precise signature analysis andnot overfitting data through post hoc processing.

Another potential source of muddied results comes frompooling of data across tumor types. At a first approximation,increasing the size of a cohort would provide greater statisticalpower for analysis. However, mixing of tumor types, particularlyif they are not of equivalent numbers (e.g., 500 breast cancers

with 25 leukemias) and have differingmutational burdens, couldresult in signal dilution or interference. This can be difficultto disentangle; therefore, pooled analyses should be undertakenwith a very clear declaration of methods, including what post hocprocessing steps are used. In the community, it remains contro-versial whether pooled analyses should be used. Such analysesimply that we expect to extract mutational signatures that areidentical across all tissues. This may be true for some signatures,but not for all. There is no reason to expect that genes involvedinHR repair performprecisely the same functions at the same timein the cell cycle to the same degree, in breast tissue as well as incolonic tissue. Indeed, the likelihood is that they almost certainlydo not.

Even when studying a specific tissue type such as breast cancer,we acknowledge that there are genuine biological differencesbetween cohorts of samples (see Supplementary Figs. S1–S3 forcomparison of two cohorts). Rare signatures present in 1% to 2%of tumors only,may not have been detected previously, because itwas simply not present in any prior dataset examined


Group A

Group B Group B

B C D E F GA

Group D Group G

Group G

4

4 4

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

1819

2021

22

X Y

4

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

1819

2021

22

X Y

4

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

1819

2021

22

X Y

4

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

1819

2021

22

X Y

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

1819

2021

22

X Y

3

2

1

5

6

7

8

910

11

12

13

14

15

16

17

18

19

2021

22

X Y

Rearrangementsignatures

Rearrangement signatures

Substitutionsignatures

Substitution signatures1

1

Ins Mh Rep None

5

54

2

2

13 6

6

3

3

308 18 1720 26

Indelpatterns

Indel patterns

BRCA1/BRCA2 statusER status

Figure 4.

The spectrum of signatures within 560 breast cancers and individual patient whole genome profiles. The panels in the middle represent, from top to bottom:BRCA1- or BRCA2-null samples (dark purple) versus what are believed to be non-BRCA1/BRCA2–mutated samples (light purple), ER status (black ¼ positive;gray¼ negative), proportions of substitution signatures, rearrangement signatures, and indel patterns present in the 560patients. Figure legends are provided at thetop of the figure. Samples are ordered according to hierarchical clustering performed on rearrangement mutational signatures. Six whole genome profiles ofindividual patients are shown to demonstrate how individualized each cancer genome is per patient. Note the striking differences between the six patients,even within the same "group" (groups B and G). Group D is enriched with BRCA1-null tumors, group G is enriched with BRCA2-null tumors, and group F isenriched with tumors that are never genetically BRCA1 null, are BRCA-like but different. Ins, insertions; Mh, microhomology mediated; Rep, polynucleotide repeat-tract mediated.

CCRFOCUS




Table1.

Sum

maryofrelationshipsbetwee

nea

chmutationa

lsigna

ture

andvarious

gen

omicfeatures.The

20mutationa

lsigna

turesareno

tedintheleft-m

ostco

lumn.Thisisfollo

wed

byinform

ationonmutationclasses,

features

that

predominan

tlycharacterize

each

signa

ture,and

associated

etiologies,ifkn

own.Relationshipsrelating

totran

scriptiona

lstran

ds,replicationtime,an

dstrand

san

dchromatinorgan

izationarealso

noted.

Mutationa

lsigna

ture

Mutation

type

Predominan

tfeatures

ofsigna

ture

Associated

mutationa

lproce

ssTran

scriptiona

lstrand

Rep

licative

strand

Rep

lication

time

Chromatin

organ

ization

1Sub

C>T

atCpG

Dea

minationof

methy

l-cytosine

(ageassociated

)

Somebias

Enriche

dlate

5Sub

T>C

Uncertain

(age

associated

)Somebias

Somebias

Enriche

dlate

Slig

hten

richmen

tat

linker

2Sub

C>T

atTpCpN

APOBECrelated

Somebias

Strong

lagging

strand

bias

Enriche

dlate

13Sub

C>G

atTpCpN

APOBECrelated

Somebias

Strong

lagging

strand

bias

Flat

6Sub

C>T

(and

C>A

andT>C

)MMRdefi

cien

tSomebias

Flat

20Sub

C>A

(and

C>T

andT>C

)MMRdefi

cien

tSomebias

Enriche

dlate

26Sub

T>C

MMRdefi

cien

tSomebias

Strong

bias

Enriche

dlate

Enriche

dat

linker

3Sub

HRdefi

cien

tSomebias

Somebias

Enriche

dlate

8Sub

C>A

Amplifi

edbyHR

defi

cien

cy?

Somebias

Enriche

dlate

18Sub

C>A

Uncertain

Somebias

Somebias

Enriche

dlate

Enriche

dat

nucleo

somes

andperiodic

17Sub

T>G

Uncertain

Somebias

Enriche

dlate

Enriche

dat

nucleo

somes

andperiodic

30Sub

C>T

Uncertain

Flat

RS1

Rea

rrLa

rgetand

emdup

lications

(>100kb

)Uncertain

typeof

HRdefi

cien

cy?

NA

NA

Enriche

dea

rly

RS2

Rea

rrDispersedtran

slocations

NA

NA

Enriche

dea

rly

RS3

Rea

rrSmalltan

dem

dup

lications

( <10

kb)

HRdefi

cien

cy(BRCA1)

NA

NA

Enriche

dea

rly

RS4

Rea

rrClustered

tran

slocations

NA

NA

Enriche

dea

rly

RS5

Rea

rrDeletions

HRdefi

cien

tNA

NA

Enriche

dea

rly

RS6

Rea

rrOther

clustered

rearrang

emen

tsNA

NA

Enriche

dea

rly

Rep

eat-

med

Indel

<3bpindel

atpolynu

ctract

MMRdefi

cien

tNA

NA

Enriche

dlate

Enriche

dat

linkeran

dperiodic

Microho

mIndel

�3bpindel

with

MMEJ-junctions

HRdefi

cien

tNA

NA

Enriche

dlate

Abbreviations:MMEJ,microho

mology-med

iateden

djoining;N

A,n

otavailable;polynu

c,polynu

cleo

tide;

Rea

rr,rea

rran

gem

ent;Sub

,sub

stitution.

Rep

rinted

bypermissionfrom

Macmillan

Pub

lishe

rsLtd.:NatureCommun

ications

7:1138

3,co

pyright

2016.






C>T transition

GC

TA

TA

TA

CG

AT

G

G T C T C A G

G T U T C A G

G T T C A GC A G A G T C

G T C T C A GC

5

• Characterized by an excess of cytosine mutations at a TpC context• Localized distribution• Enriched at rearrangements (sites of previous DSBs)

5 5 55

3 3

3

5

5

5

3

3

3

A G A G T C

G T T C A GC A G A G T

C

C>T transition

UNG

APOBEC

APOBEC

Kataegis

• Characterized by an excess of cytosine mutations at a TpC context• Genome-wide distribution• Enriched in early replication time domains• Enriched on lagging replication strand

Signatures 2 and 13

APOBEC

Baseexcisionrepair

LIG3/XRCC1POLB

APE1

GC

TA

TA

TA

CG

AT

GC

C>G transversion

GC

TA

GC

TA

CG

AT

GC

C>A transversion

GC

TA

AC

TA

CG

AT

GC

A

B

Figure 5.

Mechanistic insights frommutagenesis: the APOBEC family of enzymes in genome-wide (signatures 2 and 13) and localizedmutational signatures (kataegis). On thebasis of the predominant cytosine mutagenesis at a TpC sequence context, the APOBEC family of enzymes has been implicated in causing both localizedkataegis and genome-wide signatures 2 and 13. A, APOBECs cause DNA damage, particularly on ssDNA, by deaminating cytosine into uracil. Uracil-N-glycosylase(UNG) first removes uracil before other components of the Base Excision Repair pathway restore the damaged DNA to its original state. If DNA is uncorrectedand enters replication as uracil or an abasic site, then the possibilities are of generating C>T transition or C>G and C>A transversion mutations. B, AlthoughAPOBECs are involved in both localized and genome-widemutagenesis, there is mounting experimental and analytic evidence to support the hypotheses that thesesignatures arise by different mechanisms. Kataegis is believed to require a DSB to arise first, before end resection of the DSB leaves ssDNA exposed forAPOBEC deamination (left). In contrast, APOBEC deamination that gives rise to signatures 2 and 13 requires long stretches of ssDNA that could occur duringuncoupling of the leading and lagging replication strands (right).


CCRFOCUS



(Supplementary Figs. S4–S5). Thus, getting a different result suchas a novelmutational signature in a new datasetmay be a genuinenew finding, provided, of course, that many of the canonicalsignatures are also detected.

It is very likely that mutational signatures in human tumors doindeed exist, but how analyses are performed could affect theresults of a signature extraction. Therefore, for any given analysis,it is vital to report how it was performedwith absolute clarity, andfor reviewers to critically assess whether the method applied isappropriate to the biological question being asked. What isdescribed in this review is what we have seen in breast cancersto date, although the possibility of change is there. Mathematicalextractions of mutational signatures have their limitations andshould not be considered as deterministic. Intertissue variation isexpected (Supplementary Table S1). Perhaps one way of present-ing data is that of an average signalwith error bars indicative of theintertissue variation (Fig. 2A).

Thus, there is variability in mathematical extraction of signa-tures depending on the algorithm used, on how data are used(whether analyzed as a pool of multiple tumor types or analyzedas separate tumor types), and evenonwhether the data are derivedfrom whole genome or from exome sequencing experiments.How best to handle these issues remains uncertain and will likelybe resolved in time.

Future DirectionsToday, we can demonstrate and quantifymutational signatures

in breast and other cancers; we can gain novel biological insightsand potentially exploit signature properties for clinical applica-tions. As noted above, some thoughtfulness is still required in theinterpretation of any cancer-based analysis, and experimentalwork remains the bastion for substantiating proposed etiologiesor mechanisms underpinning mutational signatures.

Notwithstanding, we are able to thoroughly profile cancergenomes per patient (Fig. 4 shows six strikingly different wholegenome profiles). Soberingly, for the near approximately 700WGS and approximately 1,500 WES breast cancers that havealready been scrutinized, no two patients shared the same set ofdrivers or the same quantities of signatures (Fig. 4). Personalizedgenomics is, therefore, not an option for us to debate; it is a fact oflife and a challenge we must embrace.

Applying comprehensive genomic approaches judiciously(72), particularly within the context of clinical trials, could prove

to bemost rewarding. If we had access to informative cohorts withoutcome data available, this would indeed help to acceleratetranslation into the clinic (72, 73).

It should also go beyond that of resequencing primary cancers.Precursors of breast cancer such as ductal carcinoma in situ (DCIS)and metastasic lesions (66) should be targeted for similarlydetailed levels of driver and mutational signature investigation.Likewise, tumors separated temporally and spatially in individualpatients could provide useful perspectives on tumorigenesis.There also remains more to explore through integration withother modalities, such as expression (74) and methylation, andassessments of surrounding tissue microenvironment.

Last but not least, the insights on mutational signatures haveonly transpired because data generated throughmany sequencingstudies, from many academic and clinical centers, have beenshared with the wider community. Thus, any future sequencingendeavor, be it within a clinical trial or otherwise, should becommitted to data sharing. This is because the opportunity tolearn new things from data resources not just immediately, butsubsequently, is huge, particularly if thorough genomic profilingis available.

Disclosure of Potential Conflicts of InterestS.Nik-Zainal is listed as a co-inventor onmultiple patentfilings related to the

application of mutational signatures that are owned by Genome ResearchLimited, and is a consultant/advisory board member for Artios Pharma Ltd.No potential conflicts of interest were disclosed by the other author.

AcknowledgmentsThe authors thank Esther Lips (NKI, Holland, the Netherlands), Shelley

Hwang (Duke University, Durham, NC), and Alastair Thompson (MD Ander-son Cancer Center, Houston, TX) for critical assessment of the manuscript. Theauthors also thank the ICGC Breast Cancer Working Group and the BASISConsortium funded by the Seventh EU Programme for having the foresight tosee the potential and conceive the idea of these extensive resequencing experi-ments in breast cancer.

Grant SupportS. Nik-Zainal was a Wellcome-Beit Fellow and personally funded by a

Wellcome Trust Intermediate Clinical Research Grant (WT100183MA) at thestart of writing this review, and subsequently funded by a CRUK AdvancedClinician Scientist Award (C60100/A23916). S. Morganella is funded by corefunds from the Wellcome Trust Sanger Institute.

Received January 18, 2017; revised February 27, 2017; accepted April 7, 2017;published online June 1, 2017.

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2017;23:2617-2629. Clin Cancer Res Serena Nik-Zainal and Sandro Morganella LevelMutational Signatures in Breast Cancer: The Problem at the DNA

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CCR FOCUS - Clinical Cancer Research · therapeutic stratiﬁcation in the modern war against cancer. Clin Cancer Res; 23(11); 2617–29. 2017 AACR. See all articles in this CCR Focus