Lotz et al

1

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: martin.lotz@warwick.ac.uk Simon Furney: simonfurney@rcsi.ie Amaya Virós: amaya.viros@cruk.manchester.ac.uk

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: amaya.viros@cruk.manchester.ac.uk 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.

Lotz et al

2

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.

Lotz et al

3

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.

Lotz et al

4

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

Lotz et al

5

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.

Lotz et al

6

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).

Lotz et al

7

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

Lotz et al

8

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

Lotz et al

9

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).

Lotz et al

10

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,

Lotz et al

11

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

Lotz et al

12

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

Lotz et al

13

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.

Lotz et al

14

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.

Lotz et al

15

References 1. Balch CM, Soong S, Gershenwald JE, Thompson JF, Coit DG, Atkins

MB, et al. Age as a Prognostic Factor in Patients with Localized Melanoma and Regional Metastases. Ann Surg Oncol [Internet]. 2013 Nov 10 [cited 2018 Nov 14];20(12):3961–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23838920

2. Cavanaugh-Hussey MW, Mu EW, Kang S, Balch CM, Wang T. Older Age is Associated with a Higher Incidence of Melanoma Death but a Lower Incidence of Sentinel Lymph Node Metastasis in the SEER Databases (2003–2011). Ann Surg Oncol [Internet]. 2015 Jul 5 [cited 2018 Nov 14];22(7):2120–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25940571

3. Gupta S, Artomov M, Goggins W, Daly M, Tsao H. Gender Disparity and Mutation Burden in Metastatic Melanoma. J Natl Cancer Inst [Internet]. 2015 Nov 20 [cited 2018 Nov 16];107(11):djv221. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26296643

4. Li CH, Haider S, Shiah YJ, Thai K, Boutros PC. Sex differences in cancer driver genes and biomarkers. Cancer Res. 2018;78(19):5527–37.

5. Yuan Y, Liu L, Chen H, Wang Y, Xu Y, Mao H, et al. Comprehensive Characterization of Molecular Differences in Cancer between Male and Female Patients. Cancer Cell [Internet]. 2016;29(5):711–22. Available from: http://dx.doi.org/10.1016/j.ccell.2016.04.001

6. Kucab JE, Zou X, Morganella S, Joel M, Nanda AS, Nagy E, et al. A Compendium of Mutational Signatures of Environmental Agents. Cell [Internet]. 2019;177(4):821-836.e16. Available from: https://doi.org/10.1016/j.cell.2019.03.001

7. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin A V, et al. Signatures of mutational processes in human cancer. Nature [Internet]. 2013;500(7463):415–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23945592%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3776390

8. Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, et al. Melanoma. Lancet [Internet]. 2018 Sep 15 [cited 2018 Nov 15];392(10151):971–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30238891

9. Shain AH, Bastian BC. From melanocytes to melanomas. Nat Rev Cancer [Internet]. 2016;16(6):345–58. Available from: http://dx.doi.org/10.1038/nrc.2016.37

10. Cancer Genome Atlas Network. Electronic address irwatson mdanderson org, Cancer Genome Atlas N. Genomic Classification of Cutaneous Melanoma. Cell [Internet]. 2015;161(7):1681–96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26091043

11. Whiteman DC, Watt P, Purdie DM, Hughes MC, Hayward NK, Green AC. Melanocytic nevi, solar keratoses, and divergent pathways to cutaneous melanoma. J Natl Cancer Inst [Internet]. 2003;95(11):806–12. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12783935

Lotz et al

16

12. Akbani R, Akdemir KC, Aksoy BA, Albert M, Ally A, Amin SB, et al. Genomic Classification of Cutaneous Melanoma. Cell [Internet]. 2015 Jun 18 [cited 2018 Nov 15];161(7):1681–96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26091043

13. Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, Nones K, et al. Whole-genome landscapes of major melanoma subtypes. Nat Publ Gr [Internet]. 2017;545(7653):175–80. Available from: http://dx.doi.org/10.1038/nature22071

14. Alexandrov LB, Jones PH, Wedge DC, Sale JE, Campbell PJ, Nik-Zainal S, et al. Clock-like mutational processes in human somatic cells. Nat Genet [Internet]. 2015 Dec 9 [cited 2019 Feb 21];47(12):1402–7. Available from: http://www.nature.com/articles/ng.3441

15. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, et al. The Life History of 21 Breast Cancers. Cell [Internet]. 2012 May 25 [cited 2018 Dec 13];149(5):994–1007. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22608083

16. Blokzijl F, De Ligt J, Jager M, Sasselli V, Roerink S, Sasaki N, et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538(7624):260–4.

17. Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (80- ). 2016;352(6282):189–96.

18. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature [Internet]. 2009;458(7239):719–24. Available from: http://dx.doi.org/10.1038/nature07943

19. Wang Y, Hekimi S. Stem cells and healthy aging. Science (80- ). 2015;350(6265):1199–204.

20. Viros A, Sanchez-Laorden B, Pedersen M, Furney SJ, Rae J, Hogan K, et al. Ultraviolet radiation accelerates BRAF-driven melanomagenesis by targeting TP53. Nature [Internet]. 2014;511(7510):478–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24919155

21. Balch CM, Soong SJ, Gershenwald JE, Thompson JF, Reintgen DS, Cascinelli N, et al. Prognostic factors analysis of 17,600 melanoma patients: validation of the American Joint Committee on Cancer melanoma staging system. J Clin Oncol [Internet]. 2001 Aug 15 [cited 2018 Nov 15];19(16):3622–34. Available from: http://ascopubs.org/doi/10.1200/JCO.2001.19.16.3622

22. Curtin JA, Fridlyand J, Kageshita T, Patel HN, Busam KJ, Kutzner H, et al. Distinct sets of genetic alterations in melanoma. N Engl J Med [Internet]. 2005;353(20):2135–47. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16291983

23. Viros A, Fridlyand J, Bauer J, Lasithiotakis K, Garbe C, Pinkel D, et al. Improving melanoma classification by integrating genetic and morphologic features. PLoS Med [Internet]. 2008;5(6):e120. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18532874

24. Landi MT, Bauer J, Pfeiffer RM, Elder DE, Hulley B, Minghetti P, et al. MC1R germline variants confer risk for BRAF-mutant melanoma. Science (80- ) [Internet]. 2006;313(5786):521–2. Available from:

Lotz et al

17

http://www.ncbi.nlm.nih.gov/pubmed/16809487 25. Trucco LD, Mundra PA, Hogan K, Garcia-Martinez P, Viros A, Mandal

AK, et al. Ultraviolet radiation–induced DNA damage is prognostic for outcome in melanoma. Nat Med [Internet]. 2018 Dec 3 [cited 2018 Dec 11]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/30510256

26. Zaidi MR, Davis S, Noonan FP, Graff-Cherry C, Hawley TS, Walker RL, et al. Interferon-gamma links ultraviolet radiation to melanomagenesis in mice. Nature [Internet]. 2011;469(7331):548–53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21248750

27. Tomasetti C, Vogelstein B, Cancer SK, Hopkins J, Cancer K. Variation in cancer risk is explained by number of stem cell divisions. 2016;347(6217):78–81.

28. Wu S, Powers S, Zhu W, Hannun YA. Substantial contribution of extrinsic risk factors to cancer development. Nature [Internet]. 2016;529(7584):43–7. Available from: http://dx.doi.org/10.1038/nature16166

29. Tomasetti C, Poling J, Roberts NJ, London NR, Pittman ME, Haffner MC, et al. Cell division rates decrease with age, providing a potential explanation for the age-dependent deceleration in cancer incidence. Proc Natl Acad Sci U S A [Internet]. 2019; Available from: http://www.ncbi.nlm.nih.gov/pubmed/31548407

30. Li CH, Prokopec SD, Sun RX, Yousif F, Schmitz N, Boutros PC. Sex Differences in Oncogenic Mutational Processes. bioRxiv [Internet]. 2019 Jan 1;528968. Available from: http://biorxiv.org/content/early/2019/01/28/528968.abstract

31. Birkeland E, Zhang S, Poduval D, Geisler J, Nakken S, Vodak D, et al. Patterns of genomic evolution in advanced melanoma. Nat Commun [Internet]. 2018;9(1):1–12. Available from: http://dx.doi.org/10.1038/s41467-018-05063-1

32. Kugel CH, Douglass SM, Webster MR, Kaur A, Liu Q, Yin X, et al. Age Correlates with Response to Anti-PD1, Reflecting Age-Related Differences in Intratumoral Effector and Regulatory T-Cell Populations. Clin Cancer Res [Internet]. 2018 Nov 1 [cited 2019 Feb 6];24(21):5347–56. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29898988

33. Samstein RM, Lee C-H, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet [Internet]. [cited 2019 Feb 6]; Available from: https://doi.org/10.1038/s41588-018-0312-8

34. Rosenthal R, Mcgranahan N, Herrero J, Taylor BS, Swanton C. deconstructSigs : delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol [Internet]. 2016;1–11. Available from: http://dx.doi.org/10.1186/s13059-016-0893-4

35. Podolsky MD, Barchuk AA, Kuznetcov VI, Gusarova NF, Gaidukov VS, Tarakanov SA. Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels. Asian Pacific J Cancer Prev. 2016;17(2):835–8.

Lotz et al

18

Lotz et al

19

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.

Lotz et al

20

(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

Lotz et al

21

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.

Lotz et al

22

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

Lotz et al

23

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

Lotz et al

24

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

Lotz et al

25

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).

Lotz et al

26

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

Lotz et al

27

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.