Genetic Prediction of Individual Breast Cancer Risk Peter Devilee - - PowerPoint PPT Presentation

Genetic Prediction of Individual Breast Cancer Risk

Peter Devilee Leiden University Medical Center

Br46 44 Br63 Br45

10-Year risk to develop breast cancer in the Netherlands

0,00% 0,50% 1,00% 1,50% 2,00% 2,50% 3,00% 3,50% 4,00% 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Data: Netherlands Cancer Registry, 1999-2003

Absolute risk Age

Lifetime breast cancer risk in NL

2 4 6 8 10 12 14 16 1990 2000 2010 % Risk Year

Incidence* Death

* Invasive + CIS; Bron: IKNL

You?

0%

Risk stratification

100% Communicating risk:

• Relative risk
• Absolute risk
• Lifetime risk
• Cumulative risk
• 10-year risk

Br46 44 Br63 Br45

“Am I at risk?”

Breast cancer prevention

Options

 Chemoprevention  Prophylactic surgery  Screening

Disadvantages

 Side-effects  Over-diagnosis  Increased cost Target those most likely to benefit Identify women at greatest disease risk

Br46 44 Br63 Br45

“Am I at risk?”

Genetic Test

Genetic testing in NL

Linkage analysis

1991

BRCA1 BRCA2

1994 1995

CHEK2

2014

Gene panels (Research) PALB2

2018

Specific Cancer Syndromes: TP53, PTEN, CDH1, NF1, . . . .

1 10 0,000001 0,00001 0,0001 0,001 0,01 0,1 1

Relative Risk Allele frequency

BRCA1 BRCA2 TP53 PALB2 CHEK2 ATM CDH1 STK11 PTEN

Risk SNPs

Genetic architecture of breast cancer

1. BRCA1, BRCA2 2. Non-B1/2 genes 3. Risk SNPs

Risk management in NL

Low (RR <2) Moderate (RR: 2-3) High (RR >3) Life time risk <20% 20-30% >30% Start screening 50 yr 40 yr 35 yr Physical examination

• +

Mammography population screening <50 yr: annually >50 yr: population screening <60 yr: annually >60 yr: population screening MRI

• BRCA1/2 mutation carriers:

Uncertainties in risk level

Easton et al. 2015

Low Moderate High

PALB2 CHEK2 2x lifetime risk 4x lifetime risk

Genetic risk Population risk 95% CI

Br46 44 Br63 Br45

“Am I at risk?”

BRCA1/2 Test = Negative

Breast cancer risk prediction tools

 Absolute Risk Prediction Models  Gene Carrier Status Risk Prediction

Models

 Risk Prediction Models of Women at High

Risk

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)

FRR for breast cancer is ~2-fold

Number of Affected First Degree Relatives Relative Risk (99% CI) 1.00

(0.97 – 1.03)

1 1.80

(1.70 – 1.91)

2

2.93

(2.37 – 3.63)

≥3

3.90

(2.03 – 7.49)

Collaborative Group on Hormonal Factors in Breast Cancer (2001)

~12% of all patients and 7% of controls

FRR explained by “genetics”

(25 years of genetic research)

Unexplained: 41%*

BRCA1 BRCA2 CHEK2 ATM PALB2 TP53 PTEN LKB1 27 pre-iCOGS SNPs (9%) ~80 new SNPs

• n iCOGS (5%)

~65 new SNPs Oncoarray (4%) Estimated

• n chip (18%)

* For overall breast cancer in Europeans (Lower for ER-negative disease, early onset disease, and breast cancer in non-Europeans)

The multifactorial model

Number of individuals

Fletcher & Houlston (2010)

Risk level

Avg Risk

All breast cancer SNPs

77 SNPs – max 154 risk alleles

(N = 33,381) (N = 33,673)

Mavaddat et al (2015), JNCI 107: djv036

Two-way SNP interactions

Mavaddat et al (2015), JNCI 107: djv036

A polygenic risk score (PRS)

5% of women 5% of women

Under a polygenic multiplicative model

For women in the highest 1% of the PRS, the estimated OR compared with women in the middle quintile was 3.36 (95%CI: 2.95-3.83, p= 7.5x10-74)

Mavaddat et al (2015), JNCI 107: djv036

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 20 25 30 35 40 45 50 55 60 65 70 75 Absolute 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%

Risk of developing breast cancer

80% of women

All Breast Cancers – Absolute lifetime risk

Mavaddat et al (2015), JNCI 107: djv036

PRS and family history

Mavaddat et al (2015), JNCI 107: djv036

Br46 44 Br63 Br45

“Am I at risk?”

BRCA1/2 Test = Negative

SNPs

PRS

Gene Panel Testing

ATM

Panel testing: which genes are relevant?

Gene Breast Ovary BRCA1 BRCA2 TP53 PTEN CDH1 STK11 PALB2 ATM CHEK2 NF1 BRIP1 RAD51C RAD51D

Yes > 2x risk? Probably Unlikely No Unknown

Douglas Easton, unpublished

Don’t just do panel testing!

0.1 1 10

CHEK2 ATM PALB2 BRCA2 BRCA1

• Approx. Centile

10% <0.1% 1%

Relative Risk

Br46 44 Br63 Br45

“Am I at risk?”

BRCA1/2 Test = Negative

SNPs

PRS

Gene Panel Testing

ATM + Other risk factors:

• Family history
• Breast density etc.

Examples

Hilbers et al., manuscript in preparation

* * * * CHEK2* 1100delC

1.24 1.62 1.02 1.50 1.68 1.55 1.62 PRS-160 Odds Ratio

Combining PRS with family history

Lakeman et al., unpublished

Counseling familial breast cancer

Current practice

 Aimed at identifying or

excluding high risk

 Test affected individual  In “positive” families:

 Carriers are given gene-

specific risks

 Those testing negative are

given population risk

 In “negative” families,

family history determines risk Future practice

 A more holistic approach,

aimed at establishing individual risk

 Anyone can be tested  A wide range of risk

levels can be found

 Some risk factors are

modifiable

Outstanding issues

 Which genes confer risk?  Allelic diversity and risk*  Imprecise risk estimates  How do risk factors combine?*

* Journal Club

Filling in the gaps…

Allele frequency Penetrance

0.001 0.01 0.1

Sequencing studies: Terra incognita GWA studies: Stretching limits Linkage studies: exhausted

Need to do REALLY BIG studies

BRIDGES Gene Panel Sequencing

Studies Cases Controls Population-based 23 18,513 15,503 “Other” (clinic- based, selected for FH or bilaterality) 12 12,891 8,615 TOTAL 35 31,404 24,118

Truncating variants – known genes

Pop-based Familial/other Gene Cases Controls OR OR (95%CI) P BRCA1 278 35 11.96 (7.01-20.41) 2.53 (1.37-4.67) 6.7x10-25 BRCA2 394 110 3.51 (2.68-4.61) 2.85 (1.74-4.67) 1.1x10-24 PALB2 224 29 4.39 (2.75-7.02) 17.48 (7.53-40.59) 4.9x10-20 ATM 230 83 1.92 (1.33-2.76) 2.61 (1.67-4.10) 3.3x10-9 CHEK2 706 166 2.48 (1.96-3.13) 5.16 (3.80-7.02) 3.6x10-41

• 1100delC 591

130 2.67 (2.04-3.49) 5.05 (3.66-6.98) 2.6x10-36

• other

117 36 1.94 (1.21-3.20) 4.07 (1.75-9.48) 3.0x10-6

BRIDGES consortium, preliminary data

DNA variation and breast cancer

Long-range enhancers Transcription factor binding sites

5’

Aminoacid substitutions UTR Splicing signals UTR

3’

miRNA binding sites Polyadenylation signals

• Real breast cancer genes
• Candidate breast cancer genes
• Common SNPs

Complementation and HR efficiency

0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4

Relative HR to WT HBRCA2

hBRCA2 WT

Mesman et al., submitted

Translating functional results into cancer risk

OR 1 OR 1.5 OR 2.0 OR 2.5 OR 3.0

0,2 0,4 0,6 0,8 1 1,2

Relative HR efficiency

Continuous Model

OR > 3

OR 8.0

Towards better risk prediction

Genetics Family history Lifestyle Hormonal factors Breast density

Risk Calculator Personal risk estimate

10-Year risk to develop breast cancer in the Netherlands

0,00% 0,50% 1,00% 1,50% 2,00% 2,50% 3,00% 3,50% 4,00% 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Data: Netherlands Cancer Registry, 1999-2003

Start mammographic screening

Absolute risk Age

Risk stratification

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 year 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%

10-year risk all breast cancers, by SNP profile

OR = 1.00

20% of women reach threshold before age 40 20-40% of women never reach threshold

Screening threshold

Mavaddat et al (2015), JNCI 107: djv036

• Pop. Avg.

Risk

Identifying surveillance group

8% of women 17% of cases Clinical Guideline: Enhanced surveillance when lifetime risk > 17%

Acknowlegdements

LUMC

 Romy Mesman, Inge

Lakeman, Rick Boonen, Harry Vrieling, Haico van Attikum, Maaike Vreeswijk, Mar Rodriguez

 Clinical Genetics: Christi

van Asperen, Setareh Moghadasi, Juul Wijnen (Inter)national

 Douglas Easton  Jacques Simard  Fergus Couch  Matti Rookus (for Hebon)

Thank you for your attention…