Multifactorial Breast Cancer Risk Assessment

Paul JamesParkville Familial Cancer Centre

Breast Cancer risk is a complex trait with contributions from multiple interrelated domains

• High Risk Genes – well characterized average risks• Moderate / Low Risk genes – improving risk estimates• Polygenic Risk Score

• Family History

• Epidemiological Risks: intrinsic: age, height, menstrual hx

• modifiable: weight, hormone use, alcohol

• Mammographic Density

All forms of risk contribute with evidence of minimal interaction / epistasisYang J Clin Oncol 2020, Gallagher JAMA Netw Open 2020, Borde JNCI 2020, Kapoor JNCI 2021

For Mod Risk genes modification can substantially alter clinical interpretation

High Risk

Moderate Risk

CHEK2: PRS Modification

Average

High PRS Quintile

Low PRS QuintilePopulation Risk

Increasing Data on the combined effects of multiple risk factors

How does Multifactorial Risk Assessment work at an individual level?

High Risk Cohort

3092 women affected by BCAssessed by a Familial Cancer Clinic:

• Young; mean age dx 44y• Familial: 66% 1st/2nd Deg with BC

37% multiple relatives BC• Enriched: ↑grade, ER-/TNBC,

multiple primary BC, OC, Male BC

Previous clinical genetic testing excluded BRCA1, BRCA2

Low Risk Cohort

4823 women, unaffected by BC

Older (mean age 64)Ongoing screening – with continued cancer status updates

Family Hx: 32% 1st/2nd Deg BC9% multiple relatives BC

• Panel Gene Testing – 10 established BC predisposition genesPALB2, CHEK2, ATM, BARD1, RAD51C, RAD51D, TP53, CDH1, PTEN STK11

• SNP Genotyping (Mavaddat et al., 2015 PRS-77) – PRS Score (70 SNPs)• Family History (all cancers to 3rd degree relatives)

RESULTS

• The strength of each risk factor assessed by regression modelling.

Pathogenic Variants in Panel Genes:5% Clinic Cases1.4% Healthy Screened Controls

PRS-70: HR per SD 1.64 (1.55-1.71)

Effect of polygenic risk greater for:

Early onset: Before 50 years: HR 1.64 (1.56-1.72)After 50 years: 1.43 (1.36-1.50)

ER Positive disease: ER+ : HR 1.76 (1.65-1.86)ER - : 1.41 (1.31-1.52) Cumulative Lifetime Risk of BC (%)

Unaffected

Affected

AUC 0.64 (0.63-0.65)

RESULTS• The strength of each risk factor assessed by regression modelling.

• Weighted adjustment of all BC/OC family history to account for ascertainment in cases

Family History:OR 95% CI

Breast cancer 1st/2nd Deg Relative 1.33 1.28-1.39

1st deg relative diagnosed <40 y 1.72 1.38-2.14

Multiple BC (≥3) 2.55 2.26-2.87

RESULTS• The strength of individual risk factors assessed by regression modelling.

PRS, Single Genes, and FHx• All contribute risk• Are essentially independent

PRS HR in PV carriers: 1.75 (1.26‐2.45)     p=0.001

Attenuation 2‐10%

Individually Combined

Logistic Regreesion Multiple Regresion

OR 95% CI OR 95% CIPRS-70Per SD 1.64 1.56-1.71 1.61 1.54-1.69

Pathogenic Variants10 genes

3.80 2.79-5.16 3.51 2.55-4.83

BC Family historyPer 1st/2nd Degree Relative

1.33 1.28-1.39 1.29 1.24-1.35

Family History PRS

Panel Test 0.02 0.56

Famliy History - 0.65

Interaction Termsp-values

Panel Gene TestingBest estimate of single gene risks

Polygenic RiskAttenuated HR per SD

BC Family HistoryAttenuated OR for FDR/SDR +

age of diagnosis

LTRi

LTRi – individual’s lifetime riskLTR0 – population average LTR

Composite Lifetime Risk of Breast cancer

75% 37%

15% 37%

10% 26%

BC Unaffected

CHEK2 pathogenic variantsn = 75

High

Mod

Pop

OR 2.5

Composite Risk

Range of Observed Composite Risk in carriers of Pathogenic Variants

PALB2 CHEK2 ATM RAD51C RAD51D BARD1 TP53, PTEN, CDH1

LifetimeRisk

Median – } Standard deviation. Lines = 90% range, Outliers: = BC cases, = Unaffected

79% 46%

17% 36%

4% 17%

40% 29%

60% 71%

0% 0%

High

Mod

Pop

Genetic Testing Composite Risk

Cases Controls

LoF Variants in 10 BC Panel Genesn=217: PALB2, ATM, CHEK2, RAD51C/D, BARD1, TP53, CDH1, PTEN, STK11

Cases Controls

Genetic Testing

Genes + FHx

Genes + FHx + PRS

Affected

Unaffected

50% Actionable

Increased Risk

50% BelowAverage Risk

X2 =1264 p < 0.0001

Integrated risk assessment improves the accuracy in the Familial Cancer Clinic…

…and in general population screening

n=3092

n=4823

3.4% 49% 29% 63%Net Reclassification Improvement:

CONCLUSIONS

• Polygenic risk and FHx combine to explain an important component of ‘penetrance’ in individuals with rare pathogenic variants in BC predisposition genes…

• And all three elements together provide the best assessmentof breast cancer risk available…with the addition of personalrisk factors

• ?? any reason not to include PRS with genetic testing currently offered to women referred to clinical services

Unaffected

Affected

Cumulative Lifetime Risk of BC (%)

AUROC 0.74 (.73‐.75)

USING POLYGENIC RISK MODIFICATION TO IMPROVE BREAST CANCER PREVENTION

An RCT of personalised risk assessment and risk management for unaffected women undergoing assessment for HBOC

2400 Women undergoing PT randomised to receive composite risk assessment or single gene test result

5yr Study of PRS in Practice

Inclusion criteria• Female, no phx DCIS BrCa OvCa• Aged >18 years• Undergoing BRCA1 BRCA2 PALB2 

CHEK2 ATM RAD51C RAD51Dpredictive test 

• Have access to, and basic familiarity with, digital platforms

Exclusion criteria• Unable to read/understand 

participant materials• Undergoing cancer treatment• Previous genomic testing

ELIGIBILITY

RECRUITMENT

Dedicated clinician education program

2000 participants, enrolled before first appointment

Randomised to receive:

1. Integrated risk assessment  = FHx / PRS / genetic test result+ personal risk factors

Or

2. Standard care (single gene result)

Followed for 3+ years

All data collection online

Genotyping with the Illumina ‘Confluence’ custom array

PROTOCOLProject ManagerResearch Coordinator

Short term:i. Compare distribution of 

assessed riskii. RM recommendations and 

intentions

Medium to long term:iii. RM uptake / adherenceiv. RA accuracy and calibration

AIMS & OBJECTIVES

CLINICAL

ANALYSE PERFORMANCE OF BREAST CANCER RISK MANAGEMENT THAT 

INCORPORATES PRS

HEALTH SERVICES

ESTABLISH HEALTH SERVICES IMPLICATIONS OF IMPLEMENTING PRS

PATIENT EXPERIENCE

EVALUATE PATIENT EXPERIENCE OF UNDERGOING PERSONALISED 

GENOMIC TESTING

i. Evaluate cancer‐related anxiety, adaptation, impact, value, patient‐assessed acceptability

ii. Qualitative interviews in a subset of participants

i. Simulation modelling long‐term implications for cancer outcomes and healthcare resources

ii. Economic evaluation to assess the incremental cost‐effectiveness

Acknowledgements

Supported by: Thanks  - : Participating patients and their families

Staff from Victorian, NSW and Tasmanian Familial Cancer Centres

Simone McInernyLyon MascarenhasJo McKinleyMaryAnne YoungMelissa SoutheyGeorgia Chevenix‐TrenchTatiane YanesFamilial Cancer Research Grp

Cancer Genomics Lab (PMCC)Ian CampbellNa LiKylie GorringeBelle Lim

Familial Cancer CentresMarion HarrisAlison Trainer

Geoffrey LindemanIngrid Winship

Yoland AntilAinsley Campbell

Pathology North NSWRodney Scott

LifePool StudyLisa Devereux