germline mutations and melanoma: are melanoma genetic ...germline mutations and melanoma: are...
TRANSCRIPT
Germline mutations and melanoma:
are melanoma genetic scores the way
forward in picking patients at risk of
melanoma?
David Adams
@David_J_Adams [email protected] 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
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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
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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 [email protected] 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