3-Peter Devilee Session I - 12.02.2019-My genome Our...

Peter Devilee

Predicting breast cancer – an update

Human Genetics & Pathology

Leiden Univ Medical Centre

LEIDEN, NL

Disclosure

15-feb-192 P. Devilee (LUMC)

(potentiële) belangenverstrengeling Geen

Voor bijeenkomst mogelijk relevante

relaties met bedrijven(Bedrijfsnamen)

• Sponsoring of onderzoeksgeld

• Honorarium of andere (financiële)

vergoeding

• Aandeelhouder

• Andere relatie, namelijk …

• NWO, KWF, Horizon2020

•

••

Breast cancer in the Netherlands

1 in 8 (lifetime risk)

.

15-feb-193

van der Waal D, Verbeek AL, den Heeten GJ, Ripping TM, Tjan-Heijnen VC, Broeders MJ. Breast cancer diagnosis and death in

the Netherlands: a changing burden. Eur J Public Health 2015;25(2):320-324

P. Devilee (LUMC)

Clinical management of breast cancer risk


Risk class Low (<2) Moderate

(RR: 2-3)

High

(RR: >3)

BRCA1/2

Life time risk <20% 20-30% >30% >60%

Start prevention 50 yr 40 yr 35 yr 25 yr

Physical

examination- - + +

Mammography Population

screening

<50 yr Annually

>50 yr

population

screening

<60 yr Annually

>60 yr

population

screening

>40 yr

biannually

>60 yr

annually

MRI - - - +

Dutch guideline

Main risk factors for breast cancer

• Biology

• Age

• Hormonal factors

• Breast density on mammogram

• Lifestyle

• Reproductive factors

• Alcohol / smoking / physical exercise / obesity

• Use of HRT, radiation exposure

• Family history

• Familial relative risk (FRR)


Predicting breast cancer – current practice


B44

B56

Gene-test

Gene-specific counseling

Family history

“positive”

“negative”

Breast cancer genes: N = ?

15-feb-197

Proven:

ATM, BRCA1, BRCA2, CHEK2, PALB2

Syndromic:

PTEN, STK11/LKB1, TP53, NF1

Possible:

BRIP1, NBN, FANCM, MRE11A, RECQL,

MUTYH, PPM1D, RAD51C, RAD51D,

XRCC2, RINT1, MMR genes, . . .

Challenges:

• Which are truly associated?

• Risk estimates uncertain

• Subtype-specific risks

• Allelic diversity (missense changes)

• Risks to other cancers?

P. Devilee (LUMC)

Gene-specific risks


High risk

Moderate risk

Near-PopulationRisk

Lee et al. (2016); Lee et al. (2019)

0

10

20

30

40

50

60

70

80

20 30 40 50 60 70 80

Bre

ast

Ca

nce

r R

isk

(%

)

Age (years)

BRCA1 BRCA2 PALB2 CHEK2 ATM Untested

Confidence intervals . . .

15-feb-19P. Devilee (LUMC)9

Easton et al. 2015

Low

Moderate

High

Assumptions:

• Applying estimates of relative risk to

population incidence (England 2003-2007)

• Ignoring competing mortality

• Ignoring other risk factors as modifiers

• “Typical” mutation carrier, i.e., ignoring

allelic diversity

Assumptions:

• Applying estimates of relative risk to

population incidence (England 2003-2007)

• Ignoring competing mortality

• Ignoring other risk factors as modifiers

• “Typical” mutation carrier, i.e., ignoring

allelic diversity

2x lifetime risk

4x lifetime risk

PALB2

CHEK2

DNA Sequencing, big data

15-feb-19P. Devilee (LUMC)10

Study Target Cases Controls

BRIDGES Gene Panel N=35 60,000 60,000

CARRIERS Gene Panel N=30 30,000 30,000

Li et al. 2018 Gene Panel N=625 11,400 4,000

Couch et al. 2017 Gene Panel N=21 30,000 GnomAD

Large-scale case-control studies

BRIDGES – preliminary results major genes


BRIDGES

(Pop-based)

Meta-analysis[1] Ambry[2]

Gene OR (95%CI) OR (90%CI) OR (95% CI)

BRCA1 11.60 (8.54-15.78) 11.4 -

BRCA2 5.63 (4.58-6.91) 11.7 -

PALB2 5.32 (3.79-7.47) 5.3 (3.0–9.4) 7.46 (5.12-11.19)

ATM 1.90 (1.52-2.39) 2.8 (2.2–3.7) 2.78 (2.22-3.62)

CHEK2 2.45 (2.10-2.86) 3.0 (2.6–3.5) 2.26 (1.89-2.72)

[1] Easton et al. (2015) N. Engl. J. Med. 372, 2243; [2] Couch et al. (2017) JAMA Oncol. 3, 1190

Polygenic risk scores


77 SNPs – max 154 risk alleles

Mavaddat et al, J Natl Cancer Inst. 2015 Apr 8;107(5)

(N = 33,381)

(N = 33,673)

Relative risk

RR = 1

Common SNPs: united we stand, divided we fall

Risk stratification: 313-SNP PRS


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

25 30 35 40 45 50 55 60 65 70 75

Life

time

Abs

olut

e R

isk

Age (years)

>99%

95-99%

90-95%

80-90%

60-80%

40-60%

20-40%

10-20%

5-10%

1-5%

<1%

Overall breast cancer – Test set

Mavaddat et al, Am. J. Hum. Genet., 2019

Middle quintile

Interaction of BRCA1/2 and PRS


BRCA1(N=15252)

BRCA2(N=8211)

Breast cancer Ovarian cancer

CIMBA Data – Kuchenbaecker et al. (2017) J Natl Cancer Inst. 109(7): djw302

N=7797 events N=2462 events

N=4330 events N=631 events

Impact on risk management?


• BRCA1• Breast cancer• 88 SNPs

12 jr

Risk-prediction tools: BOADICEA

• Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation

Algorithm

• Online tool

• Computes chance of carrying BRCA1 and BRCA2 mutation

• Computes age-specific lifetime risks to breast and ovarian cancer

• Uses family history and BRCA1, BRCA2, ATM, CHEK2, PALB2 prevalences and

risks

• Includes polygenic risk score

• Includes non-genetic risk factors in simple additive risk model

• CE-mark procedure ongoing


Lee, Antoniou, et al. Genet Med, 2019

BOADICEA – Family history only


2 2 2

B32

d34

B42 l

B45 r

d51

Age: 35

Pr64

25% 25% 19% 24% 19%

Family history only

BOADICEA – Family history + PRS


2 2 2

B32

d34

B42 l

B45 r

d51

Age: 35

Pr64

31% 19% 12% 28% 22%

Family history + PRS

~20% may shift risk class


No change

~100 BRCAx multiple case families

N=340

N=272

Lakeman et al., submitted

Moderate-risk gene carrier (CHEK2*1100delC)


35.3% 44.5% 20.2%

Lee et al., Genet. Med., 2019

Stop doing just panel testing!


0.1 1 10

CHEK2ATM

PALB2BRCA2

BRCA1

Approx. Centile 10% <0.1%1%

Relative Risk

Practice now:

• Aimed at detecting and excluding

high risk

• Testing involves mostly affected

women

• In positive families:

• Carriers receive gene-specific risk

estimates

• Non-carriers receive population

risks

• In negative families: family history

determines risk

Future:

• Aimed at assessing individual risk

• Anyone can be tested

• No “positive” or “negative” results

• Risks can be lower than average

• Non-genetic risk factors

incorporated

• Some risk factors are modifiable

15-feb-1922

Future of genetic counseling

P. Devilee (LUMC)

Risk-based population screening?


0.00

0.02

0.04

0.06

0.08

0.10

0.12

20 25 30 35 40 45 50 55 60 65

10 y

ear

risk

Age (years)

All Breast Cancers

>99%

95-99%

90-95%

80-90%

60-80%

40-60%

20-40%

10-20%

5-10%

1-5%

<1%

20% of women reach threshold before age 40

20-40% of women never reach threshold

Screening threshold

10-year risk all breast cancers, by SNP profile

Mavaddat et al (2015), JNCI 107, djv036

Acknowledgements


• Breast Cancer Association Consortium (BCAC)

• Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA)

• Breast Cancer Risk After Diagnostic Gene Sequencing (BRIDGES)

• PERSPECTIVE, CARRIERS, kConFAB, B-CAST, ENIGMA

• Hebon NL

3-Peter Devilee Session I - 12.02.2019-My genome Our...

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