Next‐Generation Genotoxicology: Using Modern Sequencing Technologies to Assess Somatic Mutagenesis and Cancer Risk

Abstract

Mutations have a profound effect on human health, particularly through an increased risk of carcinogenesis and genetic disease. The strong correlation between mutagenesis and carcinogenesis has been a driving force behind genotoxicity research for more than 50 years. The stochastic and infrequent nature of mutagenesis makes it challenging to observe and to study. Indeed, decades have been spent developing increasingly sophisticated assays and methods to study these low frequency genetic errors, in hopes of better predicting which chemicals may be carcinogens, understanding their mode of action, and informing guidelines to prevent undue human exposure. While effective, widely used genetic selection‐based technologies have a number of limitations that have hampered major advancements in the field of genotoxicity. Emerging new tools, in the form of enhanced next generation sequencing platforms and methods, are changing this paradigm. In this review, we discuss rapidly evolving sequencing tools and technologies, such as error‐corrected sequencing and single cell analysis, that we anticipate will fundamentally reshape the field. In addition, we consider a variety emerging applications for these new technologies, including the detection of DNA adducts, inference of mutational processes based on genomic site and local sequence contexts, and evaluation of genome engineering fidelity, as well as other cutting‐edge challenges for the next 50 years of environmental and molecular mutagenesis research. This article is protected by copyright. All rights reserved.

Full text

Review
Next-Generation Genotoxicology: Using Modern Sequencing
Technologies to Assess Somatic Mutagenesis and Cancer Risk
Jesse J. Salk
1, 2
and Scot t R.Kennedy
3
*
1
Department of Medicine, Division of Medical Oncology, University of Washington School of
Medicine, Seattle, Washington
2
TwinStrand Biosciences, Seattle, Washington
3
Department of Pathology, University of Washington, Seattle, Washington
Mutations have a profound effect on human health,
particularly through an increased risk of carcinogen-
esis and genetic disease. The strong correlation
between mutagenesis and carcinogenesis has been
a driving force behind genotoxicity research for
more than 50 years. The stochastic and infrequent
nature of mutagenesis makes it challenging to
observe and to study. Indeed, decades have been
spent developing increasingly sophisticated assays
and methods to study these low-frequency genetic
errors, in hopes of better predicting which chemicals
may be carcinogens, understanding their mode of
action, and informing guidelines to prevent undue
human exposure. While effective, widely used
genetic selection-based technologies have a number
of limitations that have hampered major advance-
ments in the field of genotoxicity. Emerging new
tools, in the form of enhanced next-generation
sequencing platforms and methods, are changing
this paradigm. In this review, we discuss rapidly
evolving sequencing tools and technologies, such as
error-corrected sequencing and single cell analysis,
which we anticipate will fundamentally reshape the
field. In addition, we consider a variety emerging
applications for these new technologies, including
the detection of DNA adducts, inference of muta-
tional processes based on genomic site and local
sequence contexts, and evaluation of genome engi-
neering fidelity, as well as other cutting-edge chal-
lenges for the next 50 years of environmental and
molecular mutagenesis research. Environ. Mol.
Mutagen. 61:135–151, 2020. ©2019 The Authors.
Environmental and Molecular Mutagenesis published by Wiley
Periodicals, Inc. on behalf of Environmental Mutagen Society.
Key words: chemical carcinogenesis; cancer riskassessment; in vivo mutation; error- corrected NGS; consensus
sequencing; single - cell sequencing; single molecule sequencing
INTRODUCTION
Exposure to environmental factors has been known to
alter the genetic makeup of organisms since the seminal
work by Hermann Muller in 1927 showing that Drosoph-
ila exposed to X-rays led to new heritable traits (Muller
1927). Other environmental factors, including ultraviolet
light and reactive chemicals, were reported soon after
(Stadler and Sprague 1936; Auerbach et al. 1947). It
wasn’t until the publication of the structure of DNA in
1953, and the subsequent description of DNA polymer-
ases that a mechanism linking environmental exposures to
mutagenesis and heritable changes became fully apparent
(Watson and Crick 1953; Bessman et al. 1958; Lehman
et al. 1958). The ensuing years led to a rapid expansion of
studies to catalog and better understand environmental
mutagens. By the mid-1970’s, experiments in rodent
models indicated that the majority of known mutagens
were, in fact, carcinogenic (McCann et al. 1975). Because
of the strong link, as well as the desire to save both time
and money, evaluating the mutagenic potential of a
compound has become a de facto surrogate for carcinoge-
nicity (Fig. 1). A detailed treatment of the regulatory
aspects of this important subject area is provided else-
where in this issue (Heflich et al. 2020).
Grant sponsor: National Institute of Environmental Health Sciences; Grant
number: R44ES030642.
Grant sponsor: National Institute of Justice; Grant number: 2017-DN-
BX- 0160.
Grant sponsor: Safeway/Albertsons Early Career Award For Cancer
Research; Grant number: n/a.
Grant sponsor: U.S. Department of Defense; Grant number: W81XWH-
18-1-0339.
*Correspondence to: Scott R. Kennedy, Department of Pathology, Uni-
versity of Washington, Seattle, WA.
E-mail: scottrk@uw.edu
Received 9 August 2019; Revised 20 September 2019; Accepted 25
September 2019
DOI: 10.1002/em.22342
Published online 8 October 2019 in
Wiley Online Library (wileyonlinelibrary.com).
Environmental and Molecular Mu tagenesis 61:135^151 (2020)
©2019 The Au thors. Environmental and Molecular Mutagenesis published by Wi leyPeriodicals, Inc. on behalf of Environmental Mutagen S ociety.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.

A number of key technologies have been developed
over the past 50 years to quantify genotoxicity in both
in vitro and in vivo settings. The spontaneous mutation
rate in normal somatic mammalian cells is estimated to
be in the range of 10
−8
–10
−9
mutations per nucleotide
per cell division (Lynch 2010). Directly detecting these
rare events at the DNA sequence level is technically chal-
lenging (Milholland et al. 2017)—the molecular equiva-
lent of “Where’s Waldo?”(Handford 2007). Not only
does one need to screen a very large number of nucleo-
tides cells to obtain a reasonable statistical confidence of
mutant frequencies, but the method for detecting muta-
tions must also have an error rate below the true mutant
frequency.
To circumvent these challenges, most standard mutagen-
esis assays rely on some means of biological enrichment,
whereby mutations are detected by a selectable phenotype
they create. While the specifics differ, the general approach
relies on exposing bacterial or mammalian cells to a puta-
tive mutagen and then quantifying the ratio of cells harbor-
ing a mutation in a selectable marker to the number of cells
present in the absence of selection. In vitro selection-based
mutagenesis assays include the classic Ames assay and sev-
eral mammalian cell culture-based mutation tests, such as
HPRT and APRT (Ames et al. 1973; Thompson et al.
1980). While highly effective, in vitro assays have several
limitations that make them imperfect surrogates for human
toxicology, including differences in metabolic activation/
inactivation of the tested compound, the use of only a small
number of cell types, and continuous cellular proliferation
that can result in potential “jackpot”events. In vivo assays
include transgenic rodent models, such as the MutaMouse
and the BigBlue mouse/rat assays which involve multistep
transfer of DNA from mutagen-exposed rodents into phage
and then into bacteria (Kohler et al. 1991; Myhr 1991). By
taking advantage of the in vivo context, transgenic animals
solve some of the issues inherent to the in vitro assays. As
a testament to their utility, these selection based assays are
still widely used decades after their initial development. A
history detailing the importance of these technologies is
provided in this issue by DeMarini (DeMarini 2019).
While these methods are ubiquitous in both research and
regulatory settings, reliance on selection to quantify muta-
genesis comes at a cost. The nuclear genome is a dynamic
system with spatially heterogeneous levels of biomolecular
activity, such as transcription, chromatin accessibility, adja-
cent nucleotide context, and DNA repair which strongly
modulate susceptibility to mutagenesis across the genome
(Hodgkinson and Eyre-Walker 2011). Most such assays
rely on a single reporter locus that is often artificially intro-
duced. Furthermore, the number of possible mutations that
render a selectable phenotype may be limited in some
cases, leading to an underestimation bias arising from the
inability to observe variants that result in no phenotypic
changes (eg, synonymous mutations). Lastly, selectable
markers are not always portable between different
0 YEARS 20
Metastasis
DNA Damage
Genotoxicity Preneoplastic biology Oncology
Mutation Self-Sufficient
Growth
Resistance
to apoptosis
Invasion Tumor Mass
Fig. 1. The genesis of cancer. Cancer exists on a continuum. Mutations
arise as a result of repair and replication errors due to endogenous
processes and environmental factors. These mutations are the substrate for
neoplastic clonal evolution: those that confer a proliferative or survival
advantage upon the host cell will be naturally selected. Carcinogens
promote tumorigenesis by increasing the rate of mutation or by enhancing
net-positive selection. Given the often impractically long lag-time between
a carcinogenic insult and overt tumor formation, technologies that are able
to sensitively detect DNA damage, mutation induction, and clonal
outgrowths are essential tools in a genetic toxicologist’s armamentarium.
Environmental and Molecular Mutagenesis. DOI 10.1002/em
136 Salk and Kennedy

experimental systems and are currently limited to a few
common organismal models.
Technologies that directly identify mutations in DNA of
primary tissue samples without necessitating a multistep
selection and cloning process would open up opportunities
to identify mutagenic compounds in a more unbiased man-
ner. One such method is the Pig-a assay (Bryce et al.
2008). This assay uses flow cytometry to rapidly screen
millions of cells for those that lack expression of a particu-
lar nonessential surface protein due to inactivating muta-
tions. Helpfully, this approach can be applied to both
humans and model organisms, but generally only to red
blood cells, limiting its applicability to the other tissues in
the body and making it difficult to confirm the exact nature
of the mutations themselves (mature red blood cells are
enucleate).
Several sensitive biochemical assays for mutation detec-
tion have been developed, often based on resistance to
endonuclease cleavage or allele-specific PCR. While
extremely sensitive, these methods are either too low-
throughput or excessively narrow in scope (ie, interrogate
only one or a few bases) to gain wide usage (Parsons and
Heflich 1997; Bielas and Loeb 2005). Thus, until the
advent of modern next-generation sequencing (NGS), also
referred to as massively parallel sequencing, selection-
based assays have been the dominant technology for evalu-
ating mutagenesis.
Beginning in approximately 2005, NGS has revolution-
ized many of fields of life science, including cancer biol-
ogy, population genetics, evolutionary biology, and cellular
biology. There are a several commercially available NGS
platforms that differ in their underlying approaches to
obtaining sequence information, but all share the ability to
simultaneously obtain this information from tens of thou-
sands to billions of individual DNA templates. Conse-
quently, it is now possible to obtain data on a genome-
wide scale. In addition, NGS technologies are read-based.
This “digital tabulation”approach differs from conven-
tional Sanger sequencing methods by obtaining the nucleo-
tide sequence of many individual DNA molecules, thus
enhancing the ability to detect minor mutant populations
within a heterogeneous DNA mixture which is generally
the context in which somatic mutagenesis occurs (Metzker
2010; Fig. 2).
The distinct advantages offered by NGS will revolution-
ize environmental mutagenesis and toxicology by overcom-
ing past limitations and providing new opportunities for
study. Despite its transformative potential, NGS has only
recently gained attention in this field, as several key techni-
cal hurdles have now been overcome. In this review, we
discuss the advances in modern DNA sequencing technolo-
gies that are enhancing the ability to detect low-frequency
mutagenic events and DNA damage. We review cutting
edge applications that are currently being facilitated by
these new technologies and others we see on the horizon.
NEXT-GENERATION SEQUENCING TECHNOLOGIES
In genetic toxicology, most applications of NGS to date
have focused on augmenting and enhancing the throughput
of well-established genotoxicity assays—for example,
increasing the throughput of sequencing of mutant shuttle
vectors or plaques from transgenic models (Yuan et al.
2011; Besaratinia et al. 2012; Beal et al. 2015; Chang et al.
2015). Other applications have included non-mutational
assessments of genetic toxicology, such as epigenetic and
transcriptional changes, induced by chemical exposure
(Chauhan et al. 2016; Li et al. 2017a), as well as the whole-
genome detection of environmentally induced de novo muta-
tions in offspring of exposed individuals (Reviewed in
[Marchetti et al. 2019; Godschalk et al. 2019]).
However, neither of these cases fully realize the aspira-
tional goal of being able to directly measure genotoxin-
induced DNA mutations in any tissue type of any organ-
ism. This is because modern sequencing platforms are not
without their limitations. Given the random nature of geno-
toxic insults, genetic toxicology assessment in the absence
of biological selection generally necessitates being able to
detect low-frequency somatic mutations in a large popula-
tion of non-mutant DNA molecules. In theory, DNA sub-
populations of any size should be detectable by NGS when
assessing a sufficient number of molecules. However, while
notably better than Sanger sequencing, standard NGS plat-
forms still generate errors at a substantial rate. Mistakes
arising during DNA preparation, amplification, cluster gen-
eration, and the many steps of sequencing itself typically
result in ~1% artifactual bases, and this background can be
significantly higher in certain sequence contexts (reviewed
in (Salk et al. 2018)). In contrast, the biological mutation
frequency of even heavily mutagenized animals is on the
order of one mutation per million nucleotides. Therefore, to
detect chemically induced somatic mutations, far more sen-
sitive NGS technologies are needed.
Error- Corrected Next- Generation Sequencing
Several approaches have been employed to improve the
accuracy of NGS. Initial efforts to reduce the technical
error rate of NGS focused on bioinformatic filtering of
low-confidence sequences. For example, a number of vari-
ant calling tools filter the data based on the distribution of
variants with the sequencing reads or require variants to be
seen in multiple independent sequencing reads in both read
orientations (Wang et al. 2013). More recently, statistical
approaches have been specifically developed to improve
variant calling by modeling the error profile of specific
sequencing platforms (Wei et al. 2011; Wilm et al. 2012).
These bioinformatic approaches allow for the detection of
variants to mutant fraction of ~0.5%. This level of sensitiv-
ity is effective for clonally expanded mutations (such as
those arising in the germ line or found in tumors) but is
Environmental and Molecular Mutagenesis. DOI 10.1002/em
137Next-Generation Genotoxicology

AB
D
C
F
G
E
Fig. 2. Analog vs. digital DNA sequencing. A common need in genetic
toxicology is to identify mutations in cell populations. The appropriateness
of the sequencing technology depends on mutational clonality. (A) Clonal
mutations are those present in all or most cells in a tissue (gray), whereas
subclonal mutations (colors) are present in only a subset. (B) When DNA is
extracted from a tissue, a mutation’s clonality is reflected in the isolated
molecules that are then (C) prepared for sequencing. (D) With traditional
Sanger sequencing, all molecules from the same genomic region are
genotyped together en masse in a capillary system, which produces an
analog output (electropherogram tracing) that is the average of many
different DNA molecules. (E) Generally only substantially clonal mutations
can be reliably detected. (F) In contrast, next-generation sequencing
operates by massively parallel sequencing of millions of individual
molecules digitally. On the widely used Illumina sequencing-by-synthesis
platform, this is accomplished by flowing fluorescently labeled nucleotides
across a surface coated with small biochemically generated colonies of
individual molecules (clusters), and recording the sequence of colors of each
cluster through multiple cycles of addition. (G) The resulting output is not a
single sequence, but millions of individual ones that reflect both clonal and
subclonal mutations down to approximately 1% abundance.
Environmental and Molecular Mutagenesis. DOI 10.1002/em
138 Salk and Kennedy

still orders of magnitude above the spontaneous mutant fre-
quency of DNA (Martincorena et al. 2015, 2017).
In addition to bioinformatic filtering, enzymatic removal
of DNA damage has been shown to reduce the number of
false variant calls in NGS. For example, 8-oxo-dG and
cytidine deamination, two of the most common DNA dam-
aging events, can be biochemically removed with the
damage-specific glycosylases FPG and UDG, respectively.
Combinations of glycosylases with other repair enzymes
can further repair damage-induced artifacts (Chen et al.
2017b), yet not all mutagenic lesions are recognized by
these enzymes, nor is the fidelity of in vitro repair perfect,
and the possibility exists that these approaches introduce
new errors at low levels.
The approach to error-corrected next-generation sequenc-
ing (ecNGS) that has, thus far, proven the most significant
for improving accuracy is consensus-based error correction
(Fig. 3). The technique relies on the general concept of
grouping reads that are copies derived from an original
DNA molecule and then bioinformatically creating a con-
sensus sequence from the related molecules. An important
aspect of this approach is the need to identify related reads,
which can be accomplished by the use of a uniquely identi-
fying “molecular barcode”(also referred to as “unique
molecular identifier”(UMI), “single molecule identifier”,
or simply a “tag”) for each original DNA fragment that will
be propagated to all daughter molecules during amplifica-
tion and sequencing. Molecular barcodes can be comprised
of unique fragmentation shear points, exogenously intro-
duced degenerate DNA sequences, or a combination of the
two. Importantly, they must provide enough sequence
diversity to minimize the probability that two independent
molecules will share the same molecular barcode by
chance.
Several groups introduced the idea of using molecular
barcodes to correct sequencing-based errors, but these ini-
tial studies focused on non-variant detection applications,
such as read assembly and molecular counting (Hiatt et al.
2010; Casbon et al. 2011; Fu et al. 2011). With the publi-
cation of the SafeSeqS method, Kinde et al. definitively
introduced the idea of using molecular barcoding for
improving the accuracy of mutation detection by applying
single-stranded molecular barcodes in the tails of PCR
primers, reducing the error rate to ~10
−5
(Kinde et al.
2011; Fig. 3A). A number of variations on this concept
have been published, including single-molecule molecular
inversion probes (Hiatt et al. 2013), circular sequencing
(Lou et al. 2013), and CypherSeq (Gregory et al. 2016),
among others. Consensus-making techniques that label just
one strand of original double-stranded molecules or cannot
distinguish the identity of the two strands markedly reduce
sequencer-based artifacts, such as base calling errors and
amplification errors introduced during cluster generation,
thereby reducing the methodological background by two to
three orders of magnitude and making it possible to
confidently identify rare variants at ~0.1% abundance (Salk
et al. 2018).
However, methods relying on single-stranded tagging
are fundamentally limited by base selectivity of DNA poly-
merases which, at best, have error rates of ~10
−6
(McInerney et al. 2014). Of particular relevance is the ele-
vated rate of misincorporations at sites of mutagenic DNA
damage. For example, the presence of 8-oxo-dG adducts or
deaminated cytosine bases (dU) dramatically increases the
misincorporation rate of polymerases upon traversal of the
lesion (Shibutani et al. 1991; Lindahl 1993). These mis-
incorporation events can be propagated to daughter mole-
cules during PCR, making it difficult to distinguish
between artifacts induced by chemical adducts and bona
fide variants occurring at dC and dG bases. Moreover, dif-
ferent DNA adducts are repaired with vastly different effi-
ciencies by the cell (Wood 1996). Thus, with these
methods, experiments involving mutagen exposure run the
risk of detecting the presence of both adducts and true
mutations. Given that mammalian cells are quite adept at
recognizing and repairing adducts in vivo, it is incorrect to
equate adducts with mutations (the vast majority will be
repaired before mutation occurs in vivo). Cumulatively,
these factors contribute to a practical detection limit of
~10
−4
–10
−5
, depending on DNA quality and experimental
conditions (reviewed in (Salk et al. 2018)). This is excel-
lent for many applications but does not reach the accuracy
threshold needed for direct mutagenesis assessment.
Some mutagenic compounds are capable of increasing
the mutation frequency of DNA by ~1000-fold or more.
However, because the spontaneous mutation frequency of
the mammalian nuclear genome is normally very low
(on the order of one-per-10-million base pairs), even a
1000-fold increase is still below what is reliably detectable
by single-strand UMI-based methods. Extending the con-
cept of molecular barcoding to include asymmetric double-
stranded UMIs allows for the sequencing information
derived from complementary strands of original double-
stranded to be compared for an additional level of error
correction. Double-stranded consensus calling requires
uniquely identifying each original DNA molecule (ie, a
unique molecular identifier) and its constituent strands (ie,
a strand-defining element) in a way that allows the
sequences to be related to each other. Duplex Sequencing
was the first method to use double-stranded consensuses to
remove both sequencer and early PCR derived errors
(Schmitt et al. 2012; Kennedy et al. 2014; Fig. 3B). A
number of derivative approaches, including BiSeqS
(Mattox et al. 2017), muSeq (Kumar et al. 2018), and
BotSeqS (Hoang et al. 2016), have been developed that
establish molecular barcodes and strand-defining elements
via partial bisulfite treatments or random shear points in
conjunction with ultra-low genome coverage. With all these
approaches, the theoretical error rate of double-strand con-
sensus methods is estimated to be ~10
−9
, which roughly
Environmental and Molecular Mutagenesis. DOI 10.1002/em
139Next-Generation Genotoxicology

(Wild-type)
(Mutation)
(Error)
Tagging by
primer-extension PCR amplication Grouping duplicates Consensus making
A Safe Sequencing System (SafeSeqS)
(Wild-type)
(Mutation)
Starting
population PCR amplication
Single-strand
consensus making
B Duplex Sequencing
Ligate Duplex-
tagged adapters Group duplicates
(Wild-type)
Duplex
consensus making
C 2D Nanopore Sequencing
D PacBio Circular Consensus Sequencing
Ligate hairpin adapters
to circulize
Zero mode waveguide sequencing
of closed loo
p
Hairpin ligation Nanopore sequencing Comparing duplex
strand-pair sequences
(Mutation)
Consensus making
(Mutation)
Consensus making
Comparing tandem
strand-
p
air se
q
uences
(Fluorescence)
Starting
population
Fig. 3. Techniques for error corrected DNA sequencing (ecNGS). The
highest accuracy NGS methods rely on sequencing-by-consensus, whereby
data from multiple sequence reads derived from an original molecule are
combined to reduce the impact of sequencing or sample preparation errors in
each read. (A) The SafeSeqS approach uses random molecular barcodes
appliedtoPCRprimerstouniquelytagPCR amplicons, which are then further
amplified and sequenced. Variation within the sequence of reads with identical
tags can be discounted as technical artifacts (X’s). Some errors that occur
during the first extension cycle may escape correction (triangles). (B) Duplex
Sequencing relies on ligation to apply molecular barcodes to both strands of
original double-stranded molecules. These are used alone or in combination
with fragmentation points to uniquely label both strands such that derivative
sequence reads from each strand can be directly related back to their founder
strand and compared to those from its complement. The method is significantly
more accurate that single-stranded consensus-making methods but is more
sequencing-intensive. (C) 2D sequencing on nanopore platforms uses physical
linkage of the two strands of an original duplex, which are then sequenced
together without the need for amplification. The method is fast and simple, but
nanopore platforms are lower accuracy and throughput than more widely used
sequencing-by-synthesis platforms. (D) Circular Consensus Sequencing on the
PacBio single-molecule platform similarly links the two strands of an original
double-stranded with hairpins to allow multiple sequencing passes across both
original strands. As with 2D, lower raw platform accuracy and throughput are
drawbacks but very long reads can be obtained.
Environmental and Molecular Mutagenesis. DOI 10.1002/em
140 Salk and Kennedy

reflects the square of the error rate for single-strand molec-
ular barcoding methods. Duplex methods have been used
by a number of groups to study the occurrence of muta-
tions arising from a number of genotoxic species, including
smoking, aflatoxin, aristolochic acid, urethane, benzo[a]
pyrene, and reactive oxygen species (Kennedy et al. 2013;
Hoang et al. 2016; Chawanthayatham et al. 2017).
Single -Cell Sequencing Technologies
Typical NGS protocols rely on fragmenting the genomes
of thousands of cells. The result is a mixture of contribut-
ing cellular genotypes when the underlying population is
heterogeneous. In such situations, ecNGS approaches are
needed to detect these rare variants in the sea of wild-type
sequences if their abundance is below approximately 1%.
However, the creation of a heterogeneous mixture of DNA
fragments from many different genomes eliminates the
ability to identify variants to within the same cell, poten-
tially underestimating the mutagenic potential of a com-
pound that may only bio-accumulate in certain cell types
(or cell division states). Sequencing the DNA from single
cells overcomes this problem and ensures that observed
mutations came from the same cell.
Typical single-cell sequencing (SCS) protocols require
isolation of individual cells followed by lysis and usually
some form of whole-genome amplification to generate
enough DNA for sequencing (Zong et al. 2012; Fu et al.
2015; Dong et al. 2017; Chen et al. 2017a). Somatic muta-
tions would typically be heterozygous (absent recombina-
tion events or loss of heterozygosity) and expected to be
present in 50% of reads mapping to the genomic position
of interest. SCS methods have been able to successfully
detect structural variants (Wang et al. 2012), copy-number
variations (Navin et al. 2011), and single nucleotide vari-
ants (Dong et al. 2017) on a genome wide scale. To date,
SCS approaches have not been widely deployed to evalu-
ate genotoxicity at the single-cell level. However, recent
work by the Vijg group demonstrated the ability of SCS
to detect mutations induced by mutagenic exposure with
N-ethyl-N-nitrosourea, indicating its potential utility
(Dong et al. 2017).
Another barrier to deploying SCS for genotoxicity appli-
cations is throughput and, by extension, cost. In response
to the need for more high-throughput methods, microfluidic
sorting of cells (Rinke et al. 2014), nano-well technologies
(Gierahn et al. 2017), and emulsion droplet partitioning
technologies (Klein et al. 2015) have been developed and
have increased throughput up to ~10,000 cells. A promis-
ing new approach to massively parallel SCS, termed com-
binatorial cellular indexing, uses intact fixed cells or nuclei
as “reaction vessels”to physically partition the nucleic
acids of interest. A unique combination of DNA sequences
(ie, a cellular index) are enzymatically introduced to all the
nucleic acids present within each cell/nucleus, a technique
sometimes referred to as “combinatorial indexing”or
“split-pool barcoding.”Because all sequencing reads
derived from nucleic acids from the same cell share the
same cell-specific index, the sequencing data can be com-
putationally grouped and assigned to a specific cell. This
approach offers the ability to examine hundreds of thou-
sands of cells without the need for complex single-cell han-
dling equipment and has been used to study structural
variations, transcriptomics, and epigenetics (Cusanovich
et al. 2015; Cao et al. 2017; Vitak et al. 2017; Rosenberg
et al. 2018). The steady improvements in throughput and
cost makes SCS increasingly attractive for answering
important hypotheses about genotoxicity that can only be
answered at the level of individual cells. The efficient com-
bination of SCS with high-accuracy single-molecule con-
sensus sequencing methods would be an extremely
powerful tool of the future.
Direct Sing le-Molecule S equencing
Several mutagenesis assays are routinely used to detect
clastogenic compounds, such as the micronucleus and chro-
mosomal aberration assays (Araldi et al. 2015). Although
effective from a risk assessment perspective, these classic
tools do not yield specific sequence information. Modern
sequencing platforms are able to detect structural variants,
but with the added benefit of providing detailed sequence
information and genomic location. While Illumina’s revers-
ible terminator dye technology, with its reasonably good
accuracy and high throughput, is well suited to detect
single-nucleotide changes, it is currently limited to read
lengths of less than 300 bases (600 bases for paired-end).
Short read length significantly hinders the ability to detect
large structural variations and genomic rearrangements.
Therefore, structural variants are bioinformatically detected
by searching for reads spanning a break point or inferred
by read-pairs mapping farther apart than a few kilobases or
to different chromosomes (Alkan et al. 2011). Bioinfor-
matic detection tends to have highly variable sensitivity
and specificity rates due to the size of the structural variant,
occurrence of chimeric PCR products prior to sequencing,
overlapping clusters or read-hopping on the sequencer, or
the occurrence of erroneous read mapping arising from
pseudogene sequences elsewhere in the genome (Alkan
et al. 2011; Kosugi et al. 2019).
Direct single-molecule sequencing (SMS) is a relatively
new technology that offers a number of advantages over
short read sequencing methods. Two different SMS tech-
nologies are currently commercially available: single-
molecule real-time sequencing (SMRT; commercialized by
Pacific Biosciences) and nanopore (commercialized by
Oxford Nanopore Technologies). van Dijk et al. (2018)
provides a detailed comparison of these two technologies.
Both approaches produce very long reads (10–250 kb) and
directly sequence genomic DNA without the need for
Environmental and Molecular Mutagenesis. DOI 10.1002/em
141Next-Generation Genotoxicology

intermediate PCR amplification. The elimination of PCR
chimeras and the addition of more sequence information
within a single read significantly reduce mis-mappings and
increases the probability of spanning breakpoints, minimiz-
ing false positives.
Although these technologies enhance the ability to detect
structural variants, they exhibit much higher error rates in
the detection of single nucleotide variants, often as high as
15%–20% (Quail et al. 2012; Ross et al. 2013; Jain et al.
2017). However, these platforms are amenable to platform
specific variations of consensus sequencing to reduce their
high false-positive rates. For example, in SMRT-based plat-
forms, circularized original DNA molecules can be
sequenced repeatedly with a highly processive DNA poly-
merase and a “circular consensus sequence”made for each
template, improving the accuracy of SNV calls by several
orders of magnitude (Travers et al. 2010; Fig. 3C).
Nanopore-based technologies, however, are not yet amend-
able to significant consensus error correction by repeated
sequencing of the same molecule. Currently, a type of
double-strand consensus can be made by affixing a hairpin
adapter to the DNA fragments such that the two strands
can be sequentially sequenced in a reverse complementary
fashion, referred to as “two-directional”sequencing
(Fig. 3D). This approach has been reported to reduce the
error rate to ~3%–5% (Jain et al. 2015; Tyler et al. 2018).
Two recent methods, termed Rolling-Circle to Con-
catameric Consensus and Intramolecular-ligated Nanopore
Consensus Sequencing, offer the possibility of increasing
the accuracy of nanopore-based platforms by implementing
a circular consensus sequencing-like approach, analogous
to what is performed on the PacBio platform (Li et al.
2016; Volden et al. 2018).
NEXT-GENERATION SEQUENCING APPLICATIONS
Modern sequencing platforms are rapidly transforming
the ability to detect, quantify, and characterize genomic
DNA at an ever increasing rate and scale. These technolo-
gies open up new potential avenues of research that are
likely to have a profound impact on the study of genomic
toxicology and mutagenesis. We highlight a number of
emerging applications for modern sequencing platforms
that are of high relevance for genotoxicity studies.
Adduct Detect ion by Sequencing
Genotoxic compounds that induce mutagenesis typically
do so by chemical modification of the DNA that induces
base mis-insertion by DNA polymerases during genome
replication or repair. The majority of damage is effectively
removed by multifaceted cellular repair processes before
mutation occurs (Sancar et al. 2004). However, the level of
DNA damage and efficiency of repair can vary widely by
genomic context and damage type, with some adducts and
genomic locations being essentially unrepaired (Chang
et al. 2015; Perera et al. 2016; Geacintov and Broyde
2017). As such, there is far from a one-to-one relationship
between the presence of an adduct and risk of mutagenesis.
Indeed, this is the impetus behind the widely used comet
assay that grossly quantifies the aggregate presence of
DNA break and adducts but has the limitation of not pro-
viding sequence context or genomic location information.
While outside the scope of this review, the presence of
unrepaired DNA adducts has been shown to lead to
increases in transcriptional mutagenesis and significant
physiological consequences, even when the underlying
DNA sequences is unchanged (reviewed in (Brégeon and
Doetsch 2011)).
A number of approaches have been developed to take
advantage of modern sequencing platforms to assess the
distribution of DNA adducts on a genome wide scale and,
frequently, at single-nucleotide resolution. Current short-
read technologies, such as the Illumina platform, are typi-
cally unable to directly detect DNA adducts, so the pres-
ence of chemical alterations must be inferred by other
means. One strategy is the detection of read start or termi-
nation positions. This approach relies on the ability of
bulky lesions, such as alkyl groups, to block the DNA
polymerases during the PCR steps used in library prepara-
tion (Hu et al. 2016; Hu et al. 2017; Wu et al. 2018). The
result is that the DNA fragments being sequenced will ter-
minate immediately adjacent to the blocking moiety. The
use of DNA repair enzymes or chemical treatments has also
been employed to specifically cleave DNA at sites of dam-
age followed by adapter ligation and sequencing. The result
is similar to the above, whereby the 50-end of a read
denotes a site immediately adjacent to a site of damage.
This strategy has been used to detect UV (Mao et al. 2016;
Hu et al. 2017), cisplatin (Hu et al. 2016), and bulky alkyl
adducts (Mao et al. 2017; Aloisi et al. 2019). The presence
and location of ribose bases in DNA can be similarly
inferred, simply by inducing breaks with alkaline hydroly-
sis (Orebaugh et al. 2018).
Another frequently used strategy to infer DNA damage
employs enrichment for, or depletion of, DNA fragments
containing adducts. Depletion-based approaches make use
of enzymatic removal of adducts that render those DNA
fragments unsequenceable. The readout is a drop in cover-
age areas of the genome prone to DNA damage relative to
undamaged ones (Bryan et al. 2014). This approach
exhibits poor sensitivity when adducts are present in only a
small minority of DNA molecules, as is the case in many
in vivo applications. One solution is to enrich adduct-
containing molecules via immunoprecipitation of DNA
bearing specific adducts or bound repair proteins (ie, base
excision repair or nucleotide excision repair, etc.) (Bryan
et al. 2014; Hu et al. 2017, Hu et al. 2016; Li et al. 2017b).
In an analogous approach, base adducts that are poorly
targeted by immunoprecipitation can be chemically
Environmental and Molecular Mutagenesis. DOI 10.1002/em
142 Salk and Kennedy

modified to make them amendable for capture (Wu et al.
2018). Both methods can significantly improve detection of
damage or repair activity on a genome-wide scale.
An advantage of many single-molecule sequencing plat-
forms is that many DNA adducts can be directly detected
without prior manipulation. In the case of the PacBio
SMRT sequencing technology, chemical modifications to
the template base affect the kinetics of dNTP incorporation
by DNA polymerases in a defined way that is relatively
specific to each adduct (Clark et al. 2011). Most studies
have focused on endogenous epigenetic modifications (ie,
methylation), but the methods and statistical analysis
employed by these studies could easily be adapted to gen-
otoxicity applications.
Challenges in detecting blocking lesions are one notable
limitation for this polymerase-based approach. Nanopore
technologies, on the other hand, are well suited for identi-
fying bulky adducts. Base-calling is accomplished by
observing changes in ionic current/impendence that are
specific to the template base as it passes through the
nanopore structure (reviewed in (Deamer et al. 2016)).
Base modifications are detectable because they alter this
characteristic profile in an adduct-specific way. Most efforts
have focused on detecting endogenous methylations
(Laszlo et al. 2013; Schreiber et al. 2013), but an increasing
number of reports are beginning to characterize a wider
variety of exogenous DNA adducts more relevant to
genetic toxicology, including pyrimidine dimers, benzo[a]
pyrine, 8-oxo-dG, abasic sites, and double-strand cross-
links (An et al. 2012; Wolna et al. 2013; An et al. 2015;
Perera et al. 2015; Zhang et al. 2015).
Characterizing Genotoxicit y by Muta tional Signatures
One of the primary goals of genotoxicity testing is to
link specific exposures to mutagenesis and, ultimately, car-
cinogenesis. Controlled exposure studies in animal models
are currently the gold standard for relating exposure to car-
cinogenicity. However, the linking of mutagenic exposure
to cancer in human populations is far more complex and
largely depends on population level epidemiological studies
(Wild 2008). With some rare exceptions, such as skin can-
cer with sun exposure and cervical cancer with human pap-
illomavirus, definitive attribution of a specific instance of
cancer to a specific genotoxic event is extremely difficult,
especially when compounded with the naturally occurring
accumulation of mutations in cancer relevant genes during
aging (reviewed in (Risques and Kennedy 2018)). Tools
that enable detection of genotoxic exposure in humans, and
more closely link its relationship to cancer, would have a
profound impact on clinical medicine and public health, as
well as important legal and ethical implications.
The relative incidence of different types (or spectra) of
single-base substitutions are nonrandom and strongly
depends on the specific nature of the mutagen. On their
own, simple mutation spectra (ie, A!Gvs.C!A) have
limited specificity due to significant overlap between differ-
ent mutagens and their predominant mutation type. Local
sequence context, however, strongly influences the fre-
quency of a given type of mutation. The identity of
flanking nucleotides adds a great deal of additional infor-
mation that can be harnessed to better indicate the exact eti-
ology of observed mutations (Fig. 4).
Data generated by The Cancer Genome Atlas and other
large-scale sequencing efforts have provided an opportunity
to identify many distinct mutational patterns in a wide vari-
ety of cancer types. By taking into account known cancer
biology and patient medical history, analysis of the tumor
mutation patterns can, in some cases, provide a correlative
link between exposure and the observed mutational pat-
terns; for example, high levels of mutations seen in mela-
noma are consistent with pyrimidine dimers (The Cancer
Genome Atlas 2015). These patterns can be readily
detected in tumors using standard NGS techniques because
of the clonal nature of tumor formation. Mutations present
early in neoplastic transformation are propagated to descen-
dent tumor cells, where they are easily identified as well
above the background error rate of sequencing (Fig. 4A).
This is in contrast to early genotoxin-associated mutations
in normal tissues, which are present in only a minority of
cells among a larger unmutated population, and where far
more sensitive methods are required.
The primary challenge in performing this type of spectral
analysis has been that somatic tumor mutations are the
result of the cumulative mutational processes incurred by
the founding cancer cell’s lineage since embryogenesis. As
such, it is necessary to deconvolute the relative contribu-
tions of each of these mutational processes. Alexandrov
et al. were the first to report the use of nonnegative matrix
factorization, a statistical method developed for decomposi-
tion of multivariant data, to computationally parse out con-
stituent mutational processes based on both the specific
mutation type (ie, G!T/C!A) and the identity of the
adjacent 50and 30bases (Alexandrov et al. 2013). In their
initial work, the authors reported 21 “mutational signa-
tures”(or “trinucleotide signatures”) across the TGCA data
set, with some of the signatures exhibiting high tumor-type
specificity (Alexandrov et al. 2013). Recent analysis of
tumor sequencing data, comprising 4645 whole genomes
and 19,184 exomes, has validated the vast majority of the
initially reported signatures, as well as further expanded the
number of mathematically defined signatures to now
include a total of 49 single-base substitution signatures,
11 doublet-base substitution signatures, 4 clustered-base
substitution signatures, and 17 small insertion/deletion sig-
natures (Alexandrov et al. 2018).
Mutational signatures have risen to prominence in the
genomic literature over the last 5 years (reviewed in
(Phillips 2018)), but they are not without limitations. Sig-
natures are computationally derived. Some portion of the
Environmental and Molecular Mutagenesis. DOI 10.1002/em
14 3Next-Generation Genotoxicology

described signatures could be computational artifacts or
subfeatures within other processes. Furthermore, the bulk
of research on mutational signatures has focused on their
presence in tumors, for the practical reasons described
above. The signatures observed in a tumor may not fully
recapitulate processes in normal tissues. Signatures in
tumors arise from both endogenous and exogenous sources
(Alexandrov et al. 2013; Alexandrov et al. 2015;
Alexandrov et al. 2018) and are an amalgamation of muta-
genic processes that may be somewhat biased by clonal
sweeps that occur during tumor formation when effects
unrelated to exposure-associated mutagenesis are operative.
Recent work using error-corrected sequencing to study
aflatoxin-induced mutations in normal mouse tissue
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Fig. 4. Approaches for assessing mutational signatures. Mutational
spectra, particularly polynucleotide mutational signatures, provide
important mechanistic insights into mutational processes. Most of what
we know about these patterns has come from natural or artificial means
of single cell cloning. (A) Exome or whole-genome sequencing of tumor
populations reflects the somatic processes operative in the founding cell
of the most recent clonal sweep. (B) Single cells can be cloned from
cultured populations exposed to known or suspected mutagens to assess
their mutational signatures (C) The clonal variants present in individuals
that were not present in their parents reflects the state of mutational
processes during gametogenesis or early embryogenesis. (D) Sequencing
of cloned cells or molecules from certain selection-based mutagenicity
assays can be used similarly, although the patterns may be distorted by
the selection system itself. (E) With ecNGS, it is now possible to obtain
mutational spectra by directly sequencing DNA from any tissue of any
organism.
Environmental and Molecular Mutagenesis. DOI 10.1002/em
144 Salk and Kennedy

demonstrated the low-frequency signature to be distinctly
different from that observed in the tumor itself. This sug-
gests additional mutagenic processes may have developed
during tumorigenesis that were unrelated to aflatoxin expo-
sure (Chawanthayatham et al. 2017; Fedeles et al. 2017).
For most genetic toxicologists, a forensic analysis of
the mutational processes that led to clonal tumors is only
useful insofar as the knowledge can be applied for pro-
spectively screening new compounds. Sequencing human
cancers that follow natural exposures, similar to sequenc-
ing of family trios to infer germ line processes that intro-
duce mutations between generations (Fig. 4C), is simply
not a practical tool in this regard. Most conventional gen-
otoxicity assays are not equipped to take advantage of tri-
nucleotide signature analysis due to their reliance on
selective markers with a narrow nucleotide repertoire
which can significantly bias observed spectrum (Fig. 4D).
Simple, and even trinucleotide, mutational spectrums can
be assessed from transgenic rodent assays by manually
picking hundreds of phage plaques for sequencing, but in
addition to being very labor intensive, the approach is still
complicated by an incomplete repertoire of three base-pair
groups within the small reporter genes and the fact that
synonymous mutations do not result in phenotypic
changes.
A less biased approach for experimentally obtaining
detailed mutational spectra without any biological selec-
tion is cloning of single cells after compound exposure
followed by large-scale sequencing (Fig. 4B). In an out-
standing recent study by the Nik-Zanal group, the authors
carried out whole-genome sequencing on induced pluripo-
tent stem cells that were cloned from populations treated
with nearly 80 known or suspected carcinogens, identify-
ing dozens of distinct signatures (Kucab et al. 2019). This
more than quadrupled the existing collection of signatures
that have been experimentally ascribed to from exogenous
sources—a list which will undoubtedly continue to grow
(Chawanthayatham et al. 2017; Huang et al. 2017; Ng
et al. 2017; Boot et al. 2018).
Cultured cells cannot fully recapitulate all the meta-
bolic and distribution complexities of in vivo exposures
and single-cell cloning is not trivial (Blokzijl et al.
2016). However, the extensive signature knowledge and
mathematical methods generated from both this approach
and from genotyping tumors can be readily applied to
the above-described new sequencing technologies. Many
of these have sufficient accuracy to detect low-frequency
genotoxin-induced mutations without need for clonal
expansion of any form (Fig. 4E). This opens the possibil-
ity of being able to assess mutational signatures in any
cell type from any tissue from any species directly from
extracted DNA (Chawanthayatham et al. 2017). Much
remains to be done in this emerging space, but the future
remains bright for its applications in genomic
toxicology.
Neo -Genotoxicity: Genome E ngineering Technologies
The classic fields of genetic toxicology and environmen-
tal mutagenesis have typically focused on the effects of
broadly acting DNA damaging chemicals and their effects
to human health. However, the emergence of new genetic
manipulation technologies, what we term “neo-
genotoxins,”presents both new challenges and new oppor-
tunities for the field. A critical aspect of these tools, espe-
cially from a regulatory perspective, is determining their
specificity in altering the genome in the desired way. Like
traditional chemical mutagens, off-target DNA cutting or
gene mis-insertion could increase the risk of cancer by
inadvertently interrupting an oncogene or tumor suppres-
sor. However, unlike randomly acting small molecules, the
rules for predicting where in the genome this might hap-
pen, and the technical complexities for site-specific screen-
ing, are completely different.
With the development of programmable endonucleases,
such as zinc-finger nucleases, transcription activator-like
effector nucleases, and, most recently, CRISPR/Cas nucle-
ases, it is now possible to make targeted genomic alter-
ations in situ (reviewed in (Gaj et al. 2013)). In theory, the
20–40 bases targeted by these enzymes should be more
than sufficient to ensure complete specificity, but the pres-
ence of pseudogenes, human genetic variation, and a toler-
ance for sequence changes in the recognition sequence, can
reduce site specificity (Lessard et al. 2017). In silico
methods have been developed to help predict off-target
effects of these nucleases, especially for the CRISPR/Cas
family of endonucleases, but have shown only moderate
concordance with experimental data (reviewed in (Chuai
et al. 2017)).
Using modern sequencing platforms, several unbiased
methods have been developed to detect the presence of
double-strand breaks. A primary concern with these tech-
nologies is the hundreds to thousands of potential off-target
sites that exist across the genome. Further complicating the
issue is that the probability of cutting off-target sites can
vary by several orders of magnitude which means that
brute force sequencing may not be sensitive enough to
detect rare off-target events. While the specifics of each
approach are different, they largely depend on using
in vitro digestion with the nuclease in question followed by
the introduction of a known universal sequence via liga-
tion/integration or the cell’s homologous recombination
machinery that can be selected by PCR or targeted
pulldown. These methods have reported a wide range of
off-target cutting depending on the method used (Fu et al.
2013; Frock et al. 2015; Tsai et al. 2015; Cameron et al.
2017; Tsai et al. 2017). There is a substantial need for more
accurate and sensitive methods to detect off-target cut sites.
A notable limitation of these methods is the inability to
practically assess off-target effects in vivo, which will be
critical for regulatory testing and widespread medical use
Environmental and Molecular Mutagenesis. DOI 10.1002/em
14 5Next-Generation Genotoxicology

of genome-editing technologies. To date, we are aware of
only one in vivo method, termed “Verification of in vivo
Off-targets”(VIVO), that has been published. This
approach uses a combination of in vitro off-target detection
with evaluating the observed off-target sites seen in the
in vitro data for characteristic deletion events caused by
in vivo expression of CRISPR/Cas9 in mouse liver (Tsai
et al. 2017; Akcakaya et al. 2018). Further complicating
matters is that the highly sequence-dependent nature of
both on-target and off-target effects makes animals untena-
ble surrogates for assessing genotoxicity induced by
human-genome targeted nucleases.
The clinical importance of neogenotoxins has become
even more apparent with the emergence of cell-based thera-
pies. While cells do not constitute a genotoxin per se, the
genetic engineering and potential for clonal selection of
mutation-harboring subpopulations during their develop-
ment can lead to increased risk of acquiring cancer from
within the transplanted cells. For example, recent studies
have shown that genome editing using CRISPR-Cas9
results in TP53-mediated DNA damage response and cell-
cycle arrest. Consequently, there is a strong selective
advantage for cells harboring inactivating mutations in this
important tumor suppressor (Haapaniemi et al. 2018; Ihry
et al. 2018; Sinha et al. 2018). In other words, the effect of
even perfectly accurate on-target cutting is natural selection
of cells bearing the most common genetic driver in all
human cancers. These issues, and others that have not yet
been discovered, are likely to complicate therapeutic appli-
cations involving genetically engineered cells, such as for
regenerative medicine or CAR-T-based cancer therapies.
Technologies for accessing these risks will need to be
extremely accurate, quickly adaptable to new targets, and
equally applicable to in vitro preclinical usage as to in vivo
human studies—a tall order by any estimation.
Carcinogenicit y vs. Mu tagenicity
While essentially all human mutagens are carcinogens,
the reverse is not always true. Mutagenesis is an imperfect
surrogate for cancer risk. Nonmutagenic carcinogens may
drive neoplasia through inflammation, epigenetic modifica-
tions, and endocrine disruption that drives aberrant cellular
proliferation (Ohshima et al. 2003; Baccarelli and Bollati
2009; Soto and Sonnenschein 2010). In these cases, classic
selection-based mutagenesis assays would not easily detect
these compounds as likely carcinogenic, indicating why
2-year rodent studies remain a safety requirement for new
drug approval.
A number of recent reports show that clonal expansion
of cells harboring somatic mutations in cancer-associated
genes is a normal part of aging (reviewed in (Risques and
Kennedy 2018)). Because non-genotoxic carcinogens are
generally believed to accelerate carcinogenesis by forcing
unregulated cell division, clonal expansions of mutations
could be used as a marker of emerging ability to proliferate
outside the confines of the normal regulated tissue architec-
ture (Salk and Horwitz 2010). The development of ultra-
accurate ecNGS may offer a way to quantify these expan-
sions and correlate their presence with environmental expo-
sure or potentially cancer risk. Approaches could involve
the sequencing of large panels of cancer driver genes or
hypermutable portions of the genome for clonal expan-
sions. A similar idea has been used in studying somatic
evolution in dysplastic and cancerous tissue (Salk et al.
2009; Naxerova et al. 2017; Baker et al. 2019). Detection
of very early preneoplastic changes at the cellular level by
observing accelerated growth of small clones could be car-
ried out in conjunction with mutagenesis screening using
the same ecNGS methods. For an in-depth discussion on
this topic, please see the accompanying review by Parsons
and colleagues (Harris et al., 2020).
FUTURE APPLICATIONS AND CONCLUSIONS
The utility of modern sequencing platforms has
expanded well beyond the initial use of sequencing DNA
for genome assembly and germ line variant detection, for
which they were originally developed. While in its infancy,
these technologies are ushering in a renaissance for the
study of genotoxicity and somatic mutagenesis. The digital
nature and massive scale at which these technologies oper-
ate is already providing rich data sets that are orders of
magnitude beyond that which was available to the field’s
pioneers.
Ultimately, the technologies and methods that we have
described here will be deployable for direct monitoring of
exposures in human populations—a concept famously
envisioned by William Thilly more than three decades ago
(Sattaur 1985). Widely recognized environmental carcino-
gens such as aflatoxin and aristocholic acid cause thou-
sands of cancer deaths globally per year, but, at the current
time, it is impossible to know which individuals may have
been exposed during their lives and are at the greatest risk
(Ng et al. 2017). From the point of view of an individual,
routine screening in at-risk populations could identify those
who would most benefit from close clinical surveillance.
From a public health perspective, population testing
could aid in identifying regional exposure hot spots where
source control efforts could be most effective. Numerous
statistically defined “cancer clusters”have been described,
frequently near industrial sites (Thun and Sinks 2004).
New tools that more directly link chemical exposure of
individuals to an instance of cancer could empower com-
munities with objective data to more effectively demand
cleanup and provide local governments and regulators with
early detection tools to prevent clusters in the first place.
Due to the generalizability of NGS technologies to any
source of DNA, surveying native organisms for mutagenic
Environmental and Molecular Mutagenesis. DOI 10.1002/em
146 Salk and Kennedy

signatures in their genome would allow for environmental
monitoring for the presence of mutagens. An amusing, yet
entirely appropriate, analogy is the proverbial canary-in-a-
coal mine; in this modern rendition, it is the canary’s
genome that serves as a biosensor for mutagenic coal dust
(Fig. 5). We envision that many of the varieties and appli-
cations of the new technologies outlined in this review can
be combined to obtain a more complete picture of gen-
otoxicity and cancer risk both in model systems and
humans. The use-cases described herein are likely to be
only the beginning of our needs as we look toward engag-
ing with mutagenic new environments, such as inter-
planetary space, and consider new high-risk medical
frontiers, such as gene editing of the germ line. The full
breadth of applications for these new tools remains to be
seen, but their use will undoubtedly offer new avenues of
research and further drive development of technologies that
will carry us through the next 50 years.
AUTHORCONTRIBUTIONS
S.R.K. and J.J.S. conceptualized the review topics.
S.R.K. wrote the initial manuscript draft. S.R.K. and
J.J.S. contributed to the figures and manuscript.
Conflict of Interest
J.J.S. is an employee and equity holder at TwinStrand
Biosciences. S.R.K. is a paid consultant and equity holder
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Fig. 5. Canary-in-a-coal-mine: a century later. A hundred years ago, at
the suggestion of John Scott Haldane, caged canaries were routinely
brought into British coal mines as an early warning sign of human-
relevant toxic gases. Although their routine use ceased in the 1980s, the
broader concept of using sentinel species to infer the presence of
environmental hazards remains highly germane in modern genetic
toxicology. Should it have been possible to collect and analyze a DNA
sample from one of Haldane’s birds using modern ecNGS techniques, it is
quite likely that the mutagenic signature of benzo[a]pyrene could have
been identified and used to inform efforts to mitigate the environmental
cancer risk. Other naturally present sentinel organisms, including humans
themselves, can be similarly used.
Environmental and Molecular Mutagenesis. DOI 10.1002/em
14 7Next-Generation Genotoxicology

at TwinStrand Biosciences and a paid consultant for Wil-
cox & Savage, PC.
Acknowledgments
We would like to thank Dr. Penny M. Faires for scien-
tific editing, Clint Valentine for mutational spectra
graphics, and the scientific team at TwinStrand Biosciences
and members of the HESI Genetic Toxicology Technical
Committee (GTTC) for inspiration and championing new
genomic technologies. This work was supported in part by
DOD/CDMRP grant W81XWH-18-1-0339, NIJ grant
2017-DN-BX-0160, and Safeway/Albertsons Early Career
Award in Cancer Research to S.R.K. and NIH/NIEHS
grant R44ES030642 to J.J.S.
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Accepted by—
C. Yauk
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