Predicting Individual Breast Cancer Risk Peter Devilee Leiden - PowerPoint PPT Presentation

Predicting Individual Breast Cancer Risk Peter Devilee Leiden University Medical Center

Br63 Br45 “Am I at risk?” Br46 44

10-Year risk to develop breast cancer in the Netherlands 4,00% 3,50% Absolute 3,00% risk 2,50% 2,00% 1,50% 1,00% 0,50% 0,00% 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 Age Data: Netherlands Cancer Registry, 1999-2003

Breast cancer incidence in NL

Lifetime breast cancer risk You?

Risk stratification Communicating risk: • Relative risk • Absolute risk • Lifetime risk • Cumulative risk • 10-year risk 0% 100%

Br63 Br45 “How high is my risk?” Br46 44

Breast cancer prevention Options Disadvantages  Chemoprevention  Side-effects  Prophylactic surgery  Over-diagnosis  Screening  Increased cost Target those most likely to benefit Identify women at greatest disease risk

Br63 Br45 “Is all this risk genetic?” Br46 44

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)

Twin studies Cancer incidence among 203.691 twin pairs from Scandinavia Cancer Proportion of variance due to Heritability Shared Non-shared environment environment Breast 0.31 0.16 0.53 Ovary 0.39 0 0.61 Lung 0.18 0.24 0.58 Colon 0.15 0.16 0.69 Prostate 0.57 0 0.43 Mucci et al. (2016) JAMA 315:68.

FRR for breast cancer is ~2-fold Number of Relative Risk Affected (99% CI) First Degree Relatives 1.00 0 (0.97 – 1.03) 1.80 ~12% of all patients 1 (1.70 – 1.91) and 7% of controls 2.93 2 (2.37 – 3.63) 3.90 ≥3 (2.03 – 7.49) Collaborative Group on Hormonal Factors in Breast Cancer (2001)

Prevalence of familial cancer Cancer Genetic* Familial** Sporadic Breast ~5 ~15 ~85 Colon ~5 ~25 ~75 Prostate ~3 ~15 ~85 * % of cases with a germline mutation in a known susceptibility gene ** % of cases with one or more family members with same disease

Genetic architecture of breast cancer TP53 1. BRCA1, BRCA2 CDH1 BRCA1 BRCA2 2. Non-B1/2 genes 10 STK11 3. Risk SNPs Relative Risk PALB2 PTEN CHEK2 ATM Risk SNPs 1 0,000001 0,00001 0,0001 0,001 0,01 0,1 1 Allele frequency

FRR explained by “genetics” (25 years of genetic research) Unexplained: 41%* Estimated on chip (18%) BRCA1 BRCA2 ~65 new SNPs Oncoarray (4%) CHEK2 ~80 new SNPs TP53 ATM 27 pre-iCOGS on iCOGS (5%) PTEN PALB2 SNPs (9%) LKB1 * For overall breast cancer in Europeans (Lower for ER-negative disease, early onset disease, and breast cancer in non-Europeans)

Genetic testing in NL 1991 1994 1995 2014 2018 Linkage BRCA1 CHEK2 PALB2 analysis BRCA2 Gene panels (Research) Specific Cancer Syndromes: TP53, PTEN, CDH1, NF1, . . . .

The result of a genetic test Known breast cancer genes:  A pathogenic variant detected  What is the breast cancer risk?  Risk to other cancers?  A pathogenic variant excluded  An unclassified variant detected BRCA1, BRCA1, PALB2, ATM, CHEK2 “syndromic” genes: PTEN, TP53, CDH1, NF1, STK11

Uncertainties in risk level PALB2 High 4x lifetime risk Moderate 2x lifetime risk Low CHEK2 Genetic risk Population risk 95% CI Easton et al. 2015

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 <50 yr: annually <60 yr: annually screening >50 yr: population >60 yr: population screening screening MRI - - - BRCA1/2 mutation carriers:

Panel testing: which genes are relevant? Gene Breast Ovary > 2x risk? BRCA1 Yes BRCA2 Probably TP53 Unlikely PTEN No CDH1 STK11 Unknown PALB2 ATM CHEK2 NF1 BRIP1 RAD51C RAD51D Douglas Easton, unpublished

BRIDGES Gene Panel Sequencing 35 Genes from commercial panels Studies Controls Cases ER-pos ER-neg Triple negative Population- 30 41,797 33,538 20,174 5,428 2,044 based “Familial/ Other” 14 (2,156) 11,411 5,099 1,681 469 (clinic-based, selected for FH/bilaterality) TOTAL 44 43,953 44,949 25,273 7,109 2,513

Truncating variants – known genes Population-based Familial enriched BRCA1 7.14 11.60 BRCA2 5.63 4.95 8.37 PALB2 5.32 CHEK2 2.45 5.13 ATM 1.90 3.34 0.1 1 10 100 0.1 1 10 100 Odds Ratio Odds Ratio BRIDGES consortium, preliminary data

Truncating variants – other DSB Population-based Familial enriched ( P= .0018 ) BARD1 1.87 2.81 BRIP1 FANCC FANCM MRE11 NBN RAD50 2.86 7.15 ( P= .00011 ) RAD51C RAD51D XRCC2 0.1 1 10 100 0.1 1 10 100 Odds Ratio Odds Ratio BRIDGES consortium, preliminary data

SNP profiles > 15 million in human genome

Breast cancer associated SNPs  ~180 identified today  Minor allele frequency in general population: 5-40%  Risk per allele: 1.03 – 1.26  Each individual will have an almost unique pattern of homozygosity/heterozygosity at each of these loci

All breast cancer SNPs 77 SNPs (max 154 risk alleles) (N = 33,381) (N = 33,673) Relative risk RR = 1 Mavaddat et al (2015), JNCI 107, djv036

Two-way SNP interactions Mavaddat et al (2015), JNCI 107: djv036

A polygenic risk score (PRS) Under a polygenic multiplicative model For women in the highest 1% of the PRS, the estimated 5% of OR compared with women in the middle quintile was women 3.36 (95%CI: 2.95-3.83, p= 7.5x10 -74 ) 5% of women Mavaddat et al (2015), JNCI 107: djv036

Risk of developing breast cancer All Breast Cancers All Breast Cancers – Absolute lifetime risk 0.35 >99% 95-99% 0.30 90-95% 80-90% 0.25 60-80% Absolute risk 40-60% 0.20 20-40% 10-20% 0.15 80% of 5-10% women 1-5% 0.10 <1% 0.05 0.00 20 25 30 35 40 45 50 55 60 65 70 75 Age (years) Mavaddat et al (2015), JNCI 107: djv036

PRS and family history Mavaddat et al (2015), JNCI 107: djv036

Combining PRS with family history Lakeman et al., Submitted

Don’t just do panel testing! <0.1% Approx. Centile 1% 10% BRCA1 PALB2 CHEK2 ATM BRCA2 0.1 1 10 Relative Risk

Towards better risk prediction Genetics Family history Risk Calculator Lifestyle Hormonal factors Breast density Personal risk estimate

Avoidable risks

Breast cancer risk prediction tools  Absolute Risk Prediction Models  Gene Carrier Status Risk Prediction Models  Risk Prediction Models of Women at High Risk

Risk stratification: BOADICEA Female, age 20, moderate-risk gene carrier (CHEK2* 1100delC) Lee and Antoniou, submitted

Br63 Br45 “Am I at risk?” Br46 44 + Other risk factors: SNPs + Gene BRCA1/2, PALB2, Panel Testing CHEK2 = Negative • Family history • Breast density etc.

Examples * * * 1.24 1.62 1.02 1.50 1.68 1.55 1.62 * CHEK2* 1100delC PRS-160 Odds Ratio Hilbers et al., submitted

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

DNA variation and breast cancer miRNA binding sites Splicing signals 5’ 3’ UTR UTR Long-range Transcription Aminoacid Polyadenylation enhancers factor binding substitutions signals sites • Real breast cancer genes • Candidate breast cancer genes • Common SNPs

Complementation and HR efficiency Mesman et al. (2018), Genet Med, in press

Translating functional results into cancer risk Continuous Model 1,2 1 Relative HR efficiency OR 1 0,8 OR 1.5 0,6 OR 2.0 0,4 OR 2.5 0,2 OR OR > 3 3.0 0 OR 8.0 Shimelis et al. (2017) Cancer Res. 77:2789-2799.

Acknowlegdements LUMC (Inter)national  Romy Mesman, Inge  Douglas Easton Lakeman, Rick Boonen,  Jacques Simard Harry Vrieling, Haico van  Fergus Couch Attikum, Maaike  Matti Rookus (for Hebon) Vreeswijk, Mar Rodriguez  Clinical Genetics: Christi van Asperen, Setareh Moghadasi, Juul Wijnen