Germline mutations and melanoma:

are melanoma genetic scores the way

forward in picking patients at risk of

melanoma?

David Adams

@David_J_Adams da1@sanger.ac.uk Oct 2019

The Melanoma Genetics Consortium

Interplay between genetics and environment

from: TA Manolio et al. Nature 461, 747-753 (2009)

Heritability of Complex DiseasesFamily studies

GWAS

CDK4BAP1

Unknown CDKN2A

~10% of cases

3 or more members with a melanoma diagnosis

Familial melanoma

Law & Bishop et al., 2015

*

*

*

Common Genetic Variants and Melanoma Risk

Common Genetic Variants and Melanoma RiskNodular Superficial Acral

Ocular “Bapoma”

Tsao et al., Genes & Development 2012

We don’t know why people get acral

We don’t know why people get mucosal

Limited knowledge of ocular (BAP1)

Mucosal

Ossio et al., 2017

Population Incidence of Melanoma

SSM

ALM

NM

MM

LM/ LMM

CDK4BAP1

Unknown CDKN2A

Familial (UV-associated) melanoma

?

⤷ 184 cases from 105 pedigrees/families

⤷ 2-11 cases of melanoma in each pedigree

⤷ Early age of onset (many patients <40 years old)

⤷ Enriched for cases with multiple primaries

⤷ Genome sequenced

Melanoma in families without CDKN2A Variants

Mainly Leeds/Leiden/Queensland families

2 2

p

legend

melanoma

I

II

III

IV

V

VI

Frequency of melanoma in the general population = 1/50

Melanoma in families without CDKN2A Variants

disruptive

gene

Finding familial cancer genes

disruptive

Finding familial cancer genes

CDKN2A

CDK4BAP1

Random clustering?

Unknown

Architecture of familial melanoma

Screening for variants in TERT promoter

TSSATG

SP1SP1 EtsEtsE-box E-boxEts

-100 -200

Ets

C T G C C T G A A A C T REFERENCE

-57 -55 -60

CCC TT TCA A AG GG/

CCC TT TCA A AG GG/

PATIENT A

PATIENT B

reference

patient A

patient B

As described by Horn et al., Science. 2013

Second TERT promoter family

L890*

ATR – possible melanoma predisposition gene

N489D

OCA2 – possible melanoma predisposition gene

Nathan et al., 2019. Germline variants in oculocutaneous albinism genes and predisposition to familial cutaneous melanoma.

Potjer et al., 2019. Multigene panel sequencing of established and candidate melanoma susceptibility genes in a large cohort of Dutch non-CDKN2A/CDK4 melanoma families.

Deletion

0

50

,00

0

10

0,0

00

15

0,0

00

20

0,0

00

25

0,0

00

30

0,0

00

35

0,0

00

40

0,0

00

45

0,0

00

Gene Affected

50

0,0

00

Wildtype Sample Sample with Deletion

D i s t a n c e F r o m T h e A f f e c t e d G e n e

Pathogenic variants in population-ascertained melanoma cases

CDKN2A

Family Aus1: Deletion segregation

Whole exome sequenced

Somatic POT1 mutations in CLL(Protection of telomeres 1)

POT1 = Protection of telomeres 1 gene

Tyrosine to a cystine

OB domain

POT1 mutations in familial melanoma

TEP1

The Shelterin complex

TTAGGG TTAGGG

TTAGGG TTAGGG TTAGGG TTAGGG

POT1

POT1

POT1 mutations control telomere length

POT1 mutations in familial melanoma

Adapted from Speedy et al., Blood, 2016

POT1 variants: longer telomeres

POT1 mutations also found in:

CLL

CTCL

Some suggestion:

CRC

Cardiac angiosarcoma

POT1 variants in population-ascertained melanoma cases

Sequenced 6,227 people

Functionally Proven Disruptive Alleles2,929 cases 15

3,298 controls 8

Robles-Espinoza et al., 2019

BAP1 variants in population-ascertained melanoma cases

O’Shea et al., 2018

No uveal!

Sequenced 2,731 people

Functionally Proven Disruptive Alleles1,977 cases 3

754 controls 0

O’Shea et al., 2018

Pathogenic variants in population-ascertained melanoma cases (approximate frequencies)

2% have CDKN2A alleles (Harland et al., 2014)

0.5% have POT1 alleles

0.15% have BAP1 alleles

Close to zero ATR, OCA2

0% have TERT promoter mutations (>2500 cases)

Where are the risk variants?

Law & Bishop et al., 2015

*

*

*

Common Genetic Variants and Melanoma Risk

R (strong) r (weak)

- D84E

- R142H

- R151C

- I155T

- R160W

- D294H

- V60L

- V92M

- R163Q

MC1R variants in the population

Red hair is a recessive traitHeterozygotes are pale with freckles

MC1R variants in population-ascertained melanoma cases

Fitzpatrick Type 1 skin

30% of the UK population have these variants

Why are MC1R variants associated with increased melanoma risk?

Do the genomes of melanomas from MC1R R allele carriers look different?

How does the mutation number differ?

273 tumour : germline paired samples:

- All from different patients

- All from white-skinned individuals

- All cutaneous

(no occult, acrals or mucosals)

- 99 females, 174 males

- 43 primaries and 230 metastases

- Ages of diagnoses from 15 to 90

(plus cases from Yale)

112 extremities

106 trunk

21 head & neck

Why are MC1R variants associated with increased melanoma risk?

What are the genetic differences between MC1R carriers and non-carriers

C>TIn culture systems various mechanisms linked to MC1R variants

- regulation of AKT- defects in DNA repair

C>A C>G C>T T>A T>C

Covariates: Age, sex, sample type (primary vs met) Nicola Roberts

MC1R and melanoma mutation count

T>G

All mutation classes enriched

Non-carrier

Carrier

TC>TC

Tri-nucleotide context

Known or speculative causes of mutational signatures

Deamination of 5-methyl

cytosine; clock-like process

Known or speculative causes of mutational signatures

Ultraviolet light

Known or speculative causes of mutational signatures

Defective DNA mismatch

repair

Known or speculative causes of mutational signatures

Unknown

Known or speculative causes of mutational signatures

Same mutational processes operative. “No R allele” specific signature

Age related signature (“Signature 1”) associated with R allele status- R allele carriers 57.6 vs 62.2 years for 0/0

Take home: No specific process (that we can see) associated with MC1R just more mutations

UV is the overwhelming signature (“Signature 7”)

Zero alleles

1 or 2 R alleles

UV signature

0.0 0.2 0.4 0.6 0.8 1.0

Signature proportions

Tri-nucleotide context: somatic mutations

Germline MC1R R variants are associated with a better

outcome in melanoma

Davies et al., 2012 More neo-antigens??

Law & Bishop et al., 2015

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R/R 4x riskR/0 2x risk

Common Genetic Variants and Melanoma Risk

Polygenic Risk Scores

Person 2

MC1R

Person 1

Person 3

MC1R TYR OCA2 CDKN2A

Increasin

g po

lygenic R

isk Score

Region/ Gene Chromosome Pigmentation Nevi (moles)Telomere, Senescence,

other

PARP1 1 X

ARNT 1 X

CYP1B1/

RMDN22 X X

CASP8 2 X

MITF 3 X X

TERT 5 X

SLC45A2 5 X

CDKAL1 6 X

AGR3 7 X

CDKN2A 9 X

RAD23B 9 X X

OBFC1 10 X

TYR 11 X

CCND1 11 X X

ATM 11 X

OCA2 15 X

FTO 16 X

MC1R 16 X

ASIP 20 X

MX2 21 X

PLA2G6 22 X X

Biological pathways for these genes:• Pigmentation (14)

• Nevi (moles) (7)

• Telomere, senescence, and other

pathways (5)

Cutaneous Melanoma

Distribution of relative risk estimatesManaging Your Risk Study (n=509)Mean relative risk = 1.0, Median = 0.9

Female Male

Nu

mb

er o

f p

arti

cip

ants

Relative Risk (%) Relative Risk (%)

Anne Cust

Median Relative Risk

Distribution of absolute (remaining) lifetime risk estimates

Managing Your Risk Study (n=509)Mean absolute remaining lifetime risk = 5%, Median = 4%, range = 0.4-30%

Female Male

Nu

mb

er o

f p

arti

cip

ants

Absolute Risk (%) Absolute Risk (%)

Worst polygenic risk = absolute risk of 30%CDKN2A risk = absolute risk of 50%

To see how well the polygenic score works for melanoma, we have:

– Identified 78 genome-wide significant SNPs in this meta-analysis

– Used these latest results to generate a polygenic score for UK Biobank participant

– Examined how well the score predicts melanoma risk in fair skinned persons recruited

into UKBiobank (about 400k persons)

Cases (with melanoma) vs Controls (without)

As expected, the score is higher for those with a diagnosis of melanoma but there is considerable overlap

• While the overall score for cases and controls overlaps considerably, the score does have discrimination ability in UK Biobank

• For instance, if we break the score into quintiles, and compare the other quintiles to the “lowest 20% of score”, we see that those in the highest quintile have 4 times the risk of those in the lowest quintile

• Or if we consider deciles then the top decile have 7 times the risk to those in the bottom decile

How much discrimination does the score have?

• We know that there are components to the score which are determined by pigmentation and naevus predisposition (plus telomere length but this is not so well delineated)

• The majority the SNPs identified contribute through one of these mechanisms (eg the MC1R alleles contribute to pigmentation)

• In fact the melanoma pigmentation score is highly correlated (>0.7) to polygenic scores for skin colour and ease of tanning

• Overall, the polygenic score is marginally better than simply looking at pigmentation and naevus count

Other comments on the melanoma PRS

Uses of polygenic risk scores

• Would we really use it?• Maybe when we’re all sequenced.

• To “explain” families/individuals who don’t carry high-penetrance variants.

• As a research tool to stratify patients i.e. to determine if common geneticcorrelates with response.

Bionimbus Project

756 families (1318 subjects)

• Most families (n=435): only 1 case seq

• ‘Discovery’ Families (n=121): most informative families for gene discovery

– 3 or more CMM cases sequenced

• 36 Families: 4 CMM cases

• 18 Families: 5+ CMM cases

– Australia, USA, France, Netherlands, Spain, Italy

@David_J_Adams da1@sanger.ac.uk Oct 2019

The Melanoma Genetics Consortium

Chi Wong0 20 40 60 80

0

25

50

75

100

Survival (weeks)

% S

urv

ival

Eµ-TCL1

Eµ-TCL1;Pot1aQ94E/+

Eµ-TCL1;Pot1bQ94E/+

**

****ns

n = 27

n = 33

n = 16

n = 15Eµ-TCL1;Pot1aQ94E/+;Pot1bQ94E/+

Liver

Blood

POT1 WT

POT1 Mut

POT1 mouse model

POT1Y89C Leeds variant mouse

Pot1bY89C/+

Wild-type

POT1 mice have longer telomeres

POT1 mouse model