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Lotz et al
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Title Molecular subtype, biological sex and age shape melanoma tumour evolution Authors
M. Lotz1*, Timothy Budden2, S.J. Furney3,4*
and A. Virós2*
1Mathematics Institute, The University of Warwick, Warwick, UK. 2Skin Cancer and Ageing Laboratory, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK. 3Genomic Oncology Research Group, Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland. 4Centre for Systems Medicine, Royal College of Surgeons in Ireland Dublin, Ireland. *Corresponding authors Martin Lotz: [email protected] Simon Furney: [email protected] Amaya Virós: [email protected]
Lead Contact Dr Amaya Virós Skin Cancer and Ageing Laboratory Cancer Research UK Manchester Institute The University of Manchester | Alderley Park SK10 4TG | United Kingdom Tel: +44(0)161 306 6038 Email: [email protected] Running title Sex and age determine melanoma mutations Word count (Introduction, Methods, Results, Conclusions): 2941 Table count: 4 Supplementary tables Figure count: 5 Keywords Melanoma, molecular sex-bias, molecular age bias
Funding and acknowledgements AV is a Wellcome Beit Fellow and personally funded by a Wellcome Trust Intermediate Fellowship (110078/Z/15/Z) and Cancer Research UK (A27412). SJF acknowledges support from the European Commission (FP7-PEOPLE-2013-IEF – 6270270) and the Royal College of Surgeons in Ireland StAR programme.
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Conflicts of interest: None
What’s already known about this topic? Cancer incidence and mortality are influenced by sex and age. Melanoma is more frequent in men, and the incidence and mortality rise with increasing age. The main mutations driving melanoma are predominantly linked to UV light damage and to errors accumulated in the DNA after each cell division, which are unrepaired. These clock-like mutations linked to cell division accumulate steadily over time in healthy tissue and cancers. What does this study add? Clock and UV mutations are tightly correlated and arise in melanoma as a function of age and sex. The molecular subtypes have a distinct pattern and rate of UV and clock mutations, and clock mutations depend on the amount of UV damage. The rate of clock mutations decreases as individuals’ age, reflecting a decrease in tissue proliferation during ageing. Men have more clock mutations, which reflect a distinct proliferation rate. What is the translational message? This study indicates age and sex shape the rate of mutations observed in melanoma. The mutational burden affects response to novel immunotherapies, so this work supports the rationale to stratify patients by their mutational landscape, age and sex to best discriminate possible responders.
What are the clinical implications of this work?
The decline of cell division-linked mutations in super-aged melanoma cells may be linked to the decrease in cancer incidence observed in the “super-aged” population. UV light not only damages DNA directly leading to mutations, but also influences the rate of cell proliferation. Additionally, men present a higher rate of mutations independently of UV. These data can better inform public health prevention campaigns.
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Summary Background: Many cancer types display sex and age disparity in incidence
and outcome. The mutational load of tumours, including melanoma, varies
according to sex and age. However, there are no tools to systematically
explore if clinical variables such as age and sex determine the genomic
landscape of cancer.
Objectives: To establish a mathematical approach using melanoma
mutational data to analyze how sex and age shape the tumour genome.
Methods: We model how age-related (clock-like) somatic mutations that arise
during cell division, and extrinsic (environmental ultraviolet light) mutations
accumulate in cancer genomes.
Results: Melanoma is primarily driven by cell-intrinsic age-related mutations
and extrinsic ultraviolet light-induced mutations, and we show these mutation
types differ in magnitude, chronology and by sex in the distinct molecular
melanoma subtypes. Our model confirms age and sex are determinants of
cellular mutation rate, shaping the final mutation composition. We show
mathematically for the first time how, similar to non-cancer tissues, melanoma
genomes reflect a decline in cell division during ageing. We find clock-like
mutations strongly correlate with the acquisition of ultraviolet light-induced
mutations, but critically, males present a higher number and rate of cell
division-linked mutations.
Conclusions: These data indicate the contribution of environmental damage to
melanoma likely extends beyond genetic damage to affect cell division. Sex
and age determine the final mutational composition of melanoma.
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Introduction
Sex and age disparity in cancer incidence and outcome are well
described and studies reveal age(1,2) and sex differences(3) in
genomics(4,5). Somatic mutations are drivers of cancer and mutations arise in
cells due to damage following cell-intrinsic processes, as well as due to
external environmental damage on the DNA. Recent work describes
computational methods to discern the multiple, distinct signatures of DNA
damage imprinted on DNA depending on the insult(6), but to date there are no
available models to study the relationship between the distinct damaging
processes.
We developed a mathematical framework to investigate the
dependencies between cell-intrinsic, cell-extrinsic mutational processes, sex
and age in cancer. Cutaneous melanoma exemplifies a cancer type primarily
presenting cell-intrinsic (cell division) and environmental (ultraviolet light)
damaging processes(7), as well as presenting an age and sex bias. Male
patients and the aged population have a higher incidence and rate of death,
so we studied if the genomic imprint of the major contributors to total
autosomal tumour mutation burden (TMB) in melanoma are possible sources
for the disparity.
Melanoma presents a broad range of clinical subtypes of disease,
categorized by age of onset, history and pattern of UVR exposure(8,9). At one
end of the spectrum, we identify elderly patients with melanomas arising at
anatomic sites that have been chronically exposed to UVR with a high tumour
mutation burden (TMB). In contrast, melanoma in younger patients arises
decades after sunburn, over skin that is intermittently exposed to UVR, with a
lower TMB(9–11).
The mutually exclusive oncogenic drivers V600BRAF and NRAS
underpin the majority of cutaneous melanomas(12). Loss-of-function
mutations in the tumour suppressor NF1 drive an additional subset of cases,
and a further subgroup is defined by the absence of V600BRAF, NRAS or NF1
mutations (triple wild type; W3)(13). These genetically distinct categories
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overlap to some extent with clinical characteristics, with V600BRAF being more
prevalent in younger patients(12).
Here we examine the relationship between mutational processes and
their contribution to the melanoma somatic mutation load, their variation over
time and across sexes. We provide a novel approach to model how the
specific damage patterns in DNA arise over time and across the sexes.
Analysing the strong bias in the mutational landscape could point to key
biological differences in how tumours develop and evolve during ageing and
across sexes.
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Methods Mutation data. The primary data are the somatic mutation calls
from The Cancer Genome Atlas (TCGA) MAF of the whole-exome
sequences of the skin cutaneous melanoma (SKCM) cohort9. We classified
samples by their mutations in V600BRAF, NRAS, NF1 or none of these
genes. Samples with V600BRAFor NRAS and an additional NF1mutation
were classified as eitherV600BRAF or NRAS. We inferred the mutational
processes by categorizing the single nucleotide substitutions in the
trinucleotide context and used mathematical models to infer their contribution
to the mutational landscape across biological sex and age
(Supplementary material).
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Results
Clock-like and UVR-driven mutations accumulate with age at distinct
rates in the molecular subtypes of melanoma
We catalogued the base substitutions in 396 whole exome cutaneous
melanoma samples from TCGA (TCGA-SKM)(12) according to 96 categories
defined by the base substitution, the preceding and following bases(7). 172
had a V600BRAF (BRAF) mutation, 96 NRAS, 44 NF1 and 84 samples were
non-BRAF/non-NRAS/non-NF1 (W3; Supplementary Table 1).
We inferred the mutational signatures that account for the somatic
mutations from the TCGA data. We extracted the DNA mutational signature
linked to UVR (Signature 7, COSMIC database), which is present to varying
degrees across melanomas. Next, we identified the intrinsic, age-related
signature observed in normal cells and cancers with high cell turnover, which
corresponds to spontaneous deamination of methylated cytosine residues into
thymine at CpG sites that remain unrepaired due to rapid DNA replication
(Signature 1, COSMIC database(7,14,15)). This clock-like mutational process
allows estimation of the number of divisions a cell has undergone since its
inception. Previous studies have modelled the potential disruption to the linear
acquisition of somatic mutations during aging that occurs when the neoplastic
phase alters the rate of mutation acquisition and found across multiple cancer
types that clock-driven mutations are linked to intrinsic cellular division despite
neoplastic and oncogenic driver ontogenesis(14,16). We confirmed a positive
correlation between the median number of Signature 1 mutations per year
and age for all samples (Spearman 𝜌 0.41, P<0.0003, Figure 1A), indicating
that these mutations accumulate with age. NRAS, NF1 and W3 melanomas
presented an increase in the mean Signature 1 mutations with age, but this
relationship was less significant in NF1 and W3 melanomas, likely due to the
lower sample size (NRAS Spearman 𝜌 0.35, P<0.01; Figure 1B). Strikingly,
there was no significant rank correlation between Signature 1 and age in
BRAF samples (Spearman 𝜌 0.03, P=0.41). To examine the difference in the
rates of Signature 1 mutation accumulation between BRAF, NRAS, NF1 and
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W3 melanomas, we determined the ratio between the number of Signature 1
mutations to age, and found significant differences in the ratios of BRAF and
W3 to NF1 melanomas (pairwise Wilcoxon rank-sum test with Bonferroni
correction, P<0.0027; Figure 1C), but less pronounced for BRAF and NRAS
samples. Next, we examined the relationship between Signature 1 mutations
and melanoma cell proliferation by investigating the gene expression of cell
cycle genes(17). We found there is a weak, but significant correlation between
Signature 1 and both cell cycle checkpoints G1/S (p=0.03 R=0.107) and G2/M
(p=0.016, R = 0.121) expression genes. Furthermore, when dividing the
melanomas by high versus low Signature 1 mutations based on the median,
we observed a significantly higher expression of both G1/S and G2/M genes
in the high Signature 1 group, which is more significant for the G2/M
expressed genes. Finally, in a multiple regression with the other factors in the
data set; age, gender, and molecular subtype, Signature 1 is the only factor
associated with G1/S and G2/M gene expression.
We next examined the contribution of Signature 7 mutations and found
a progressive increase as patients aged, in accordance with progressive
accumulation of UVR damage during the course of life (Spearman 𝜌 = 0.37,
P<0.006; Figure 1D). However, the rate of mutations varied depending on the
molecular subtype, with no significant correlation found in BRAF melanomas
between Signature 7 and increasing age (Spearman 𝜌 =-0.15, P=0.87, Figure
1E), and NRAS samples showing a steady increase of Signature 7 with age
(Spearman 𝜌 = 0.37, P<0.006). Similar to Signature 1, there was a significant
difference in the ratio of Signature 7 mutations to age across the subtypes
(P<0.03, Mann-Whitney U test with Bonferroni correction; Figure 1F).
We investigated the association between global UV damage Signature
7 and the novel probabilistic UV damage signatures that were more recently
defined(13). We calculated the likely UV-associated mutational signatures
with the version 3 signatures, using deconstructSigs for consistency. The
correlation between the sum of the four new signatures
(SBS7a/SBS7b/SBS7c/SBS7d) and the version 2 signature 7 is >0.95 (p-
value < 2.2e-16). The vast majority of mutations contributing to these
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signatures are SBS7a (median proportion of total signature 7 per sample =
0.4742) and SBS7b (median proportion per sample = 0.4954). As both
signature mutagens encompass the canonical CC-TT and atypical frequency
of C to T substitutions at a dipyrimidine site that is attributable to UV
mutagenesis, we retained the original, comprehensive Signature 7 to
strengthen our power to detect associations.
Ageing affects the dynamics of the mutational landscape
Common models of cancer have assumed that mutations accumulate
at a linear rate over time(18). Genetic changes accumulate from early
life(14,16) and a decline in replicative function with age is visible in many
tissues(19). We used our cohorts to test the relationship between ageing and
clock-like mutation rate in melanoma, and found the ratio of mutations per
year decreases with age (Spearman 𝜌 = −0.34 , P<0.005; Figure 2A).
Specifically, the decline in mutations per year is pronounced in BRAF
(Spearman −0.44, P<0.0004) but not statistically significant in NRAS or W3
melanomas. We did not include NF1 samples in the analysis, as this subtype
is almost exclusive to the elderly.
To analyse the differences in ageing dynamics, we considered the
mean number of mutations at each age, modelled by a Poisson distribution
with age-dependent rate, and show the ratio of mutations by age decreases in
older age groups (Figure 2B). We used an overdispersed Poisson (negative
binomial) regression to estimate the parameters of the exponential model for
each subtype BRAF, NRAS and W3; and found that overall, the amount of
Signature 1 mutations increases by a multiplicative factor of 𝑒𝛼 = 1.0124 per
year (Figure 2C). In contrast, the increase factor is only 1.005 for BRAF,
1.0156 for NRAS and 1.0235 for W3 melanomas (Figure 2D, Supplementary
Table 2,3). Thus, whilst the ratio of Signature 1 damage acquisition to age in
BRAF and NRAS melanomas decreases during ageing, likely reflecting a
deceleration of the cell proliferation rate during maturity, the ratio per year of
clock mutations in W3 melanomas slightly increases during the human
lifespan, reflecting a distinct behaviour (Figure 2E).
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The rate of clock-like mutations is linked to UVR damage
Previous experiments show acute UVR drives melanocyte
proliferation(20), but the long-lasting effects of UVR on cell division have not
been explored. We investigated the proportion of Signature 1 and Signature 7
across the melanoma subtypes and found that UVR underpins approximately
75% of all mutations in BRAF, NRAS and NF1 samples, while only half of the
mutations in W3 samples are accounted for by UVR (Figure 3A). Furthermore,
we find a greater proportion of the ageing signature that is uncoupled from
cellular division (Signature 5, characterised by T:A to C:G transitions)
contributing to the overall mutational burden of W3 melanomas. The
underlying biological process driving Signature 5 mutations is unknown, but is
linked to ageing independently of cellular division(16).
We then used Signature 1 and 7 to investigate the relationship
between UVR damage and cell cycle, and show that cell division rate,
predicted from Signature 1, tightly correlates with the total UVR-induced
mutations in melanoma (Spearman ρ =0.82, P<10-36, Figure 3B). The
correlation between Signatures 1 and 7 remains significant across all the
subtypes (BRAF: Spearman ρ 0.70, P<10-13; NRAS: ρ 0.72, P<10-12; NF1: ρ
0.8, P<10-8; W3: ρ 0.64, P<10-6; Figure 3C). There is a marked difference in
the increase of Signature 1 that is dependent on Signature 7 in both BRAF
and NRAS melanomas (BRAF: robust regression with slope 0.033; P<10-16);
NRAS: robust regression with slope 0.046; P<10-12). These data suggest UVR
increases the total mutation burden by damaging DNA directly, but may also
modify the TMB by affecting the dynamics of cell division. Importantly, these
data show cell proliferation is coupled to UVR, not age, in BRAF melanomas.
Ageing-associated mutations accumulate at different rates in males and
females
Melanomas from male patients present an overall higher number of
missense mutations than women, adjusted for age and relevant clinical
covariates(3). We investigated the relationship between Signature 1 and sex,
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and critically, we observed that males present a higher number of Signature 1
mutations per age (P<0.01, Mann-Whitney U test with Bonferroni correction;
median male-to-female ratio 1.24; Figure 4A). The difference is visible in the
BRAF and NRAS subtypes (Figure 4B), although it is less statistically
significant (P=0.1 for BRAF, P=0.6 for NRAS; Mann-Whitney U test with
Bonferroni correction). For males, we found a significant rank-correlation with
age (Spearman ρ 0.45, P<0.00023) but not for female samples (Spearman ρ
0.124, P=0.35; Figure 4A). Using multivariate negative binomial regression we
found that sex affects the rate of mutation accumulation (P<10-16) and
estimated the factor by which mutations increase per year in male and female
samples (Supplementary Table 2; Figure 4C-4D). We observed only males
presented a factor of mutation increase per year greater than one
(Supplementary Table 2). The results remain robust when restricting the
analysis to the molecular subtypes (except NF1 due to sample size), showing
an increase in Signature 1 mutations with age in males (Supplementary Table
2).
We next investigated whether the difference in the rate of mutation
accumulation persists when accounting for the effect of UVR-driven Signature
7 mutations. Assuming that the number of Signature 1 mutations is
proportional to the number of Signature 7 (see previous section Figure 3B,
3C), we investigated the ratios of Signature 1 to Signature 7 across
melanomas, adjusted for the effect of Signature 7 on cell division, and found
that there is little to no increase in Signature 1 mutations per year in either
sex. Moreover, the multiplicative rate of increase of the ratio of Signature 1 to
Signature 7 mutations per year turns out to be slightly smaller (by 0.01) in
males (Supplementary Table 4) in all samples, across BRAF and NF1
subtypes, and is not detected in NRAS and W3. These data imply that the
rate at which clock-like mutations accumulate per year depends on UVR, and
this dependence is stronger in males than in females. Thus, men and women
exposed to equal doses of UV will adjust their cell cycles differently, with men
increasing the rate of cell division. In summary, our results suggest that the
mutation rate due to cell division is determined by UVR exposure, and males
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are more susceptible to UVR-induced cell proliferation. In contrast, females
accumulate fewer Signature 1 mutations, at a slower rate, despite UVR
exposure (Figure 5).
Discussion
Sex and age differences have been observed in cancers(3,21). We
provide a novel mathematical framework to analyse the relationship between
different damaging processes shaping the mutational landscape of cancer;
and how the mutational processes can reveal the effect of age and sex on
carcinogenesis. We use the predominant mutational processes of the exomes
of cutaneous melanomas. Melanoma DNA is imprinted primarily by (1) the
clock-like changes due to cell division and (2) UVR-driven mutations. We
reveal both processes increase during ageing and are tightly correlated, which
poses the intriguing possibility that UVR exposure not only drives melanoma
by damaging DNA directly, but also likely influences the intrinsic biological
process of cell division and damage repair.
We observe the correlation of mutations to age is absent in BRAF
melanomas, in sharp contrast to NRAS and NF1 melanomas, where we
observe a gradual, long-term UVR exposure and cell division mutation burden
increase as patients’ age. These data support melanoma arises through
different pathogenic pathways in the distinct molecular subtypes. Although our
correlation studies do not imply causation, the punctuated rate of mutation
accumulation in BRAF melanomas could be driven by episodic acute sunburn.
Our model is in keeping with the clinical subtypes of disease, where BRAF
melanomas are more prevalent in younger patients, over intermittently sun
exposed skin with low levels of sun damage in the dermis (22–24).
Furthermore, mouse studies lend further support to the association between
episodic UV and BRAF melanoma, showing episodic UV is more efficient at
driving BRAF melanoma than cumulative UV (20,25). However, relying solely
on the UV-damage mutation pattern to infer the contribution of extrinsic
damage is a limited approach, as previous animal studies show that neonatal
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UV damage can lead to melanoma in the absence of accumulated DNA
mutations by triggering inflammation(26).
Recent models examining the correlation between lifetime risk of a
cancer and cell division show that tissues with higher cell turnover present an
increased cancer incidence in the population(27,28), which suggests tissues
with high cell turnover require less environmental damage to drive
tumourogenesis. Our results challenge this assumption in melanoma and
suggest extrinsic processes such as UVR can modulate the contribution of
cell division to mutation burden.
We show the decline in cell division during ageing present in healthy
tissues(19) is discernible in the genomic imprint of cancer cells. Recent work
shows the stem cell division rate declines with age(29), and we replicate
these findings mathematically in melanoma. The rate of proliferative decline,
moreover, is not uniform across all melanoma subtypes, and our framework
could test if the decline in cell division with age varies according to the tissue
of origin, and whether cell division decline due to age mirrors a decrease in
cancer incidence observed in the super-aged population.
Finally, we reveal an increase in cell division-linked mutations in
males, which could be due to an increase in the inherent proliferation rate of
male melanocytes, or to a decrease in the mutational repair of extrinsic UVR-
related mutations. A recent pan-cancer analysis has shown sex biases in
mutational load, tumour evolution, mutational processes and at the gene
level(4,5,30); and our study suggests that sex differences in melanoma
cannot be explained by lifestyle or age alone, and likely reflect sex-specific
biology. Intriguingly, Signature 1 is increased in females in an age-adjusted,
pan-cancer whole genome analysis(30), whilst our results reveal a contrary
sex bias in Signature 1 in melanoma.
One limitation of applying mutational signatures to infer disease
evolution is that different mutational stresses occur at different time points of
disease progression. In melanoma, UV damage is acquired when melanoma
is located to the skin. In contrast, Signature 1 summarises cell divisions
throughout the lifespan of the cell; from pre-malignancy to advanced stages.
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However, recent work found most melanoma mutations to be primarily early
truncal and monoclonal, and early and late stage TMB homogenous (31).
Our study provides new tools to examine the rate of mutation
accumulation in cancer, and to analyse how sex and age contribute to tumour
development. Tumour burden, age and sex are known to influence the
response to immunotherapies(32,33) and future studies should address how
these data can be leveraged to predict response to therapy and design
strategies to improve survival. Although limited to a single melanoma cohort,
this work supports the rationale for using the mutational processes, together
with age and sex, to stratify patients for novel immunotherapy trials as well as
to inform public health prevention and diagnostic campaigns.
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Figure legends
Figure 1. The molecular subtypes of melanoma present distinct ratios of
clock-like and UVR mutations per unit of time
(A) Correlation analysis between somatic mutations due to clock-like
Signature 1 mutations in cutaneous melanomas and age. Dots represent the
median number of mutations for each age.
(B) Correlation analysis between clock-like Signature 1 mutations in the
molecular subtypes of cutaneous melanomas (BRAF: red; NRAS: blue) and
age. Dots represent the median number of mutations for each age.
(C) Ratio of number of signature 1 mutations per year across the molecular
subtypes of cutaneous melanoma.
(D) Correlation analysis between somatic mutations due to UVR Signature 7
mutations in cutaneous melanomas and age. Dots represent the median
number of mutations for each age.
(E) Correlation analysis between UVR Signature 7 mutations in the molecular
subtypes of cutaneous melanomas (BRAF: red; NRAS: blue) and age. Dots
represent the median number of mutations for each age.
(F) Ratio of number of Signature 7 mutations per year across the molecular
subtypes of cutaneous melanoma.
Figure 2. Ageing affects the intrinsic mutation rate of the molecular
subtypes
(A) Correlation analysis between clock-like Signature 1 mutations per year
and age in cutaneous melanomas. Dots represent the median number of
mutations for each age.
(B) Distribution curves displaying Signature 1 mutation frequency across age
ranges (red: 50-60 year-old range; green: 60-70 year-old range; blue: 70-80
year-old range; purple: 80-90 year-old range).
(C) Exponential model for the accumulation of Signature 1 mutations in all
melanomas. This curve models the Poisson mean distribution of mutations at
each age, with age-dependent rate.
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(D) Exponential model for the accumulation of Signature 1 mutations in the
molecular subtypes of cutaneous melanoma (BRAF: red; NF1: green; NRAS:
blue; W3: purple). This curve models the Poisson mean distribution of
mutations at each age, with age-dependent rate.
(E) Change in Signature 1 mutations per year with age across BRAF, NRAS
and W3 subtypes.
Figure 3. The Signature 7 UVR imprint predominates in melanoma and is
tightly correlated to cell division Signature 1 mutations
(A) Mutation signature spectra and proportions in BRAF, NF1, NRAS, and W3
cutaneous melanomas.
(B) Correlation analysis between somatic mutations due to extrinsic, UVR-
driven Signature 7 mutations in cutaneous melanomas and intrinsic, clock-like
Signature 1 mutations.
(C) Correlation analysis between somatic mutations due to extrinsic, UVR-
driven Signature 7 mutations in cutaneous melanomas subtypes and intrinsic,
clock-like Signature 1 mutations (BRAF: red; NRAS: blue).
Figure 4. Melanoma in males accumulate more Signature 1 mutations
(A) Difference in Signature 1 mutations per age between males and females.
(B) Difference in Signature 1 mutations per age between males and females
by subtype (BRAF and NRAS).
(C) Exponential model for accumulation of Signature 1 mutations by sex. This
curve models the Poisson mean distribution of mutations at each age, with
age-dependent rate.
(D) Change of Signature 1 mutations per age over time, according to sex.
Figure 5. Summary findings.
(A) UV-light somatic mutations are strongly associated to the rate of cell-
division mutations, suggesting that an extrinsic mutational process (UV)
influences the intrinsic mutational process due to cell division. The rate of cell
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division in male melanoma is more strongly correlated to UV damage than in
females.
(B) Cancer cells bear the genomic imprint of decreasing rate of cell division
during ageing.
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Supplementary Materials and Methods Mutation data. The primary data are the somatic mutation calls from the The
Cancer Genome Atlas (TCGA) MAF
(skcm_clean_pairs.aggregated.capture.tcga.uuid.somatic.maf) of the whole-
exome sequences of tumours from the TCGA skin cutaneous melanoma
(SKCM) cohort(12).
For each cutaneous melanoma sample we added information on
whether the sample had mutations in V600BRAF, G12/G13/Q61NRAS, NF1 or none
of these genes. (Five) samples had both V600BRAF and G12/G13/Q61NRAS
mutations. These samples are likely to have acquired the NRAS mutations as
a consequence of targeted therapy, and were excluded from the study. There
were 24 (50) samples with mutations in both NF1 and V600BRAF or
G12/G13/Q61NRAS, and they were classified as either BRAF or NRAS. Overall,
202 (163) samples have BRAF mutations, 109 (88) samples NRAS mutations,
52 (21) samples NF1 mutations, and the remaining 100 (51) samples were
classified as triple wild type (W3). 278 of the samples had as primary site the
trunk, head and neck, or extremities, while the remaining samples where from
regional lymph nodes, distant metastases, or of unknown origin.
Mutation signatures. For each sample and for each of K=96 types of single
nucleotide substitutions (SNVs) in trinucleotide context, we count the number
of times this substitution occurs relative to the abundance of the given context
in the exome. The mutation catalogue is an integer vector counting the
number of times each substitution out of a predefined alphabet appears in a
sample, and such a catalogue can be approximated by a non-negative linear
combination of mutation signatures,
m ≈ ∑ eipi
N
i=1
where the pi ∈ ℝK are discrete probability distributions on the alphabet, called
mutation signatures. The coefficient ei is called the exposure of the sample to
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signature pi. Since the entries of each signature add up to 1, the exposure of
a signature measures the total number of mutations that can be blamed on
the corresponding signature.
A set of 30 mutation signatures has been extracted and catalogued in
the COSMIC database(7). Of these signatures, signatures 1, 5 and 7 have
been found to be relevant to melanoma(7,10). Signature 7 is known to be
related to UV exposure. Signature 1 is dominated by C>T substitutions in NpG
dinucleotide context, which is believed to be related to the spontaneous
deanimation of 5-methylcytosine, and has been observed to accumulate at a
constant rate over time in melanoma and some other cancers(14,16). We
exclude signature 5 because ASC studies using liver organoids suggest this is
an ageing mechanism independent of cell proliferation rate(16).
We estimated the exposure to signatures 1 and 7 using two different
methods. We first used the mutation signatures from the COSMIC database
and determined the exposure of signatures relevant to melanoma using (R
package deconstructSigs(34)) We validated the approach by re-deriving the
mutation signatures using a hierarchical Dirichlet process (R package hdp),
taking into account the different molecular subtypes (BRAF, NRAS, NF1 and
triple wild).
Specifically, we used this approach for deconstructSigs:
(https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-
0893-4 ) with the mutation signature from the COSMIC database.
deconstructSigs uses multiple linear regression to decompose a mutation
catalog as a combination of (given, here COSMIC) mutation signatures. The
alternative hdp package is obtained here:
( https://github.com/nicolaroberts/hdp ) and is based on a hierarchical Dirichlet
process (HDP), which is a nonparametric Bayesian approach. Using the hdp
package leads to comparable results.
Mathematical model of mutation accumulation. The number of mutations
present at any given age can be described using a Poisson process(35) with
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time-varying mean λ(t). As suggested in(35), we use an exponential model
λ(t) = N0eαt for the mean. To estimate the effect of the different subtypes on
the ratio of mutations by age we modelled the accumulation of mutations
using a homogeneous Poisson process with age as offset,
log E[N(t) | X1, … , Xk] = αt + ∑ βiXi
i
,
where the Xiare the covariates (gender, site, subtype in our case). Note that in
this model, the effect of each covariate on the expected ratio N(t)/t is
multiplicative. Since the distribution of the mutation count data was found to
be overdispersed (R package AES), we estimated a negative binomial
regression instead of a Poisson regression.
While the difference in mutation ratios was found to be highly
significant, the precise parameter values depend on the particular model
used. In fact, the densities of the mutation frequencies at a given age range
resemble a mixture of Poisson distributions, with one dominant component
and smaller clusters at higher mutation counts. By fitting a Poisson mixture
model, we found similar ratios between the coefficients associated to the
different subtypes.
Change in accumulation rate with age. Using the exponential model for
mutation accumulation N(t) = N0eαt , the derivative of f(t) = eαt/t by t is
df
dt=
eαt
t(α −
1
t) . This expression is negative if α < 1/t, meaning a decrease of
the ratio f(t) with t, and positive if α > 1/t, meaning an increase of f(t) with t.
In particular, values of α < 0.01 would imply that for all ages t<100 we ratio of
mutations by time is decreasing, while smaller ratios
Ratio of Signature 1 to Signature 7 mutations adjusting for the effect of
Signature 7. Using the exponential accumulation model, 𝑁(𝑡) = 𝐸 𝑁0𝑒𝛼𝑡 ,
where E (for Extrinsic mutations) denotes the number of Signature 7
mutations (Poisson regression with the logarithm of Signature 7 as offset), we
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estimated the ratio 𝑁(𝑡)/𝐸 of intrinsic, clock-like mutations when factoring out
the extrinsic, Signature 7 mutations (Supplementary Table 4).
Cell cycle gene expression analysis
Cell cycle specific gene expression signatures were calculated for each
sample. G1/S and G2/M specific genes were those previously used in Tirosh
et al. (17) to measure proliferating cells. For each sample the G1/S and G2/M
gene expression signature scores were determined by calculating the
geometric mean of all genes in each signature. RNA-seq (log2 transformed
RSEM) data was downloaded from UCSC Xena TCGA database
(https://xenabrowser.net/datapages/) and statistical analysis was performed in
R (version 3.5.1).
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Supplementary Tables
BRAF NRAS NF1 W3
All 172 96 44 84
Male 105 61 29 51
Female 67 35 15 33
Mean Age 53 59 70 63
Mean Mutations 595 844 1708 586
Mean Signature1 33 47 70 32
Supplementary Table 1. Proportion of subtypes in TCGA data set, average age of diagnosis by subtype, mean number of Signature 1 mutations and mean number of total mutations in BRAF, NRAS, NF1 and W3 cutaneous melanomas
Signature 1 Signature 7
MALE FEMALE MALE FEMALE
BRAF 7.89 8.66 73.92 71.53
NRAS 7.54 7.4 76.35 77.26
NF1 5.32 13.7 77.35 60.06
W3 17.67 18.9 55.09 42.51
Supplementary Table 2. Proportion of mutations of signatures 1 and 7 in the total mutation load broken down by subtype and sex.
Multiplicative accumulation
of Signature 1 by year (𝑒𝛼)
MALE FEMALE
Total 1.018 (P<10^(-8)) 1.0037 (P = 0.401)
BRAF 1.01 (P<0.044) 0.997 (P=0.38)
NRAS 1.02 (P<0.01) 1.007 (P=0.39)
NF1 1.001 (P=0.91) 0.992 (P=0.68)
W3 1.049 (P<1.5 10^(-8)) 0.988 (P=0.28)
Supplementary Table 3. The yearly rate at which Signature 1 mutations accumulate by sex and molecular subtype.
Multiplicative accumulation MALE FEMALE
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of Signature 1 / Signature 7
per year (𝑒𝛼)
Total 0.995 (P<10^(-12)) 1.005 (P <1.2 10^(-8))
BRAF 1.0046 (P<10^(-5)) 1.0127 (P<10^(-16))
NRAS 1 (P=0.95) 1.0016 (P=0.4)
NF1 0.985 (P<10^(-16)) 0.996 (P=0.13)
W3 0.9998 (P=0.9) 0.965 (P<10^(-16))
Supplementary Table 4. The yearly rate a which Signature 1 mutations increases relative to Signature 7 mutations by sex.
Ethics Sequencing data was obtained from TCGA, in accordance with ethical guidelines Consent for publication All authors consent to the data publication Availability of data and materials Patient data is available from cBioportal Authors’ contributions AV conceived the project. ML led the mathematical models and SF led the bioinformatics. AV, ML and SF interpreted the data and wrote the manuscript.
Competing interests The authors declare that they have no competing interests Funding and acknowledgements AV is a Wellcome Beit Fellow and personally funded by a Wellcome Trust Intermediate Fellowship (110078/Z/15/Z) and Cancer Research UK (A27412). SJF acknowledges support from the European Commission (FP7-PEOPLE-2013-IEF – 6270270) and the Royal College of Surgeons in Ireland StAR programme.