genetics and risk of breast cancer what is the evidence

Genetics and Risk of Breast Cancer

What is the Evidence

Questions

• What is the role of mutation testing

• What is the risk to mutation carriers

• What is the evidence for intervention

• How does family history predict risk

• What lines of future research are required

What is the likelihood of finding a mutation ?

BRCA1 and BRCA2

• Mutations confer an autosomal dominant susceptibility to Breast and Ovarian cancer with high penetrance

• In some populations there are common mutations

• These are not the only genes involved

BRCA1 and BRCA2

• Population frequency (British Cases)

BRCA1 0.11%

BRCA2 0.12% (Peto et.al. 1999)

• Estimate 16% of hereditary breast cancer susceptibility is caused by these genes in UK.

Determining Probability of Being a Gene Carrier

• Empirical data

• Logistic Regression Analysis

• Bayes calculation

Ford et.al. 1997, Narod et.al. 1995

• 84% of BCLC families showed evidence of linkage to BRCA1 or BRCA2. (4 affected members).

• 76% of breast-ovarian families linked to BRCA1 (3 affected members, one ovarian).

Peto et.al. 1999• BRCA1 and BRCA2 Mutation Analysis

Status Mutation detected

BRCA1 BRCA2

Affected < 35 9/254 6/254

Affected 36-45 7/363 8/363

Affected <45, mother breast cancer 2/54 1/54

Affected <45, 1o with breast CA<60 3/52 1/52

*Affected <45, 1o with ovarian CA 3/5 0/5

*Affected 36-45, 2X 1o with breast <60 1/8 3/8

Schattuck Eidens et.al. 1997• BRCA1 and BRCA2 Mutation Analysis

Status Mutation detected

BRCA1 BRCA2

Affected < 35 ~5%

Affected 36 - 45 1-2%

Affected <45, mother breast cancer ~2%

Affected <45, 1o with breast CA<60 ~2%

Affected <45, 1o with ovarian CA ~6%

Affected 35, 1o relative with breast + ovarian 20%

Affected 35, Bilateral Breast Cancer 20%

Parmigiani et.al.1998

• Bayes Risk Calculation

– Uses population frequency of mutation– Uses penetrance data for gene mutation– Takes family structure into account– Assumes all non BRCA1/BRCA2 cancer is sporadic– Has been computerised

CASH data Ford data

Likelihood of identifying a mutation

BRCA1 and BRCA2 Mutation Detection

Status Cyrillic Peto

(BRCAPro)

BRCA1 BRCA2 BRCA1BRCA2

Affected < 35 2% 0.3% 4% 2%

Affected <45, 1o with breast CA<60 4% 2% 6% 2%

Affected <45, 1o with ovarian CA 22% 1.6% 60% 0%

Affected <45 2X1o relative with breast <60 21% 10% 13% 38%

Population Specific Mutations

Ashkenazi Jewish Population Over 2% of population

BRCA1 185delAG

5382insC

BRCA2 6174delT

Icelandic population 0.6% of population

BRCA2 999del5

Mutation Detection with Askenazi Jewish descent

• Lalloo et.al. 1998– Breast cancer <60 1/4– 2 X Breast cancer <70 3/10– BCLC criteria 5/5–

• Schattuck-Eidens et.al. 1997– Affected age 40 ~12%– Affected at 40 + 1o relative breast ~20%– Affected at 40 + 1o relative ovarian ~35%

Male Breast Cancer

Friedman et.al. 1997 (Californian Male Cases)

• 2/54 cases of male breast cancer had BRCA2 mutations• 17% had a 1o family history of breast cancer

Conclusions

In a small proportion of cases, mutation testing for BRCA1 and BRCA2 would be expected to have a high pickup rate.

Eg.

4 family members with breast cancer

Breast cancer <age 45 with 1o ovarian cancer

Conclusions

• Different systems exist to predict likelihood of detecting a mutation.– BRCApro– Logistic regression curves

• Not all of these have been validated in clinical practice.

Conclusions

• Testing for the common Ashkenazi Jewish mutations may be relevant,

– In the presence of modest family history. – with isolated young onset disease.

What is the Risk to Mutation Carriers ?

Risk to Mutation Carriers

• Derived from Linkage– Easton et.al. 1993

– Ford et.al. 1994,1998

• Empirical data from common mutations– Struewing et.al. 1997

– Steinumn et.al. 1998

Risk To Mutation Carriers (%)

Easton et.al. Ford et.al. Struewing Steinum ISD

BRCA1 BRCA2 BRCA1+2 BRCA2 (Population)

Br Ov Br Ov Br Ov Br Ov Br O v

By age 40 19 0.6 12 0.0 - - - - - -

By age 5050 22 28 0.4 33 7 15 - - -

By age 6054 30 48 7.4 - - - - - -

By age 65 - - - - - - - - 5.5 0.9

By age 7085 63 84 27 56 16 35 - - -

By age 75 - - - - - - - - 7.9 1.5

Conclusions

• BRCA1 and BRCA2 mutations confer a high risk of breast and ovarian cancer.

• All studies have potential sources of bias, the true risk will depend on the population and mutation type.

Modifying Risk to Gene Carriers

Modifying Risk

Screening Breast examination

Mammography

Ovarian Ultrasound

Hormonal Manipulation Tamoxifen

Surgical Intervention Mastectomy

Oophorectomy

Screening / Mammography

• Proven benefit when age > 50 in individuals at population risk– Meta-analysis, Kerlikowske JAMA 1995

• Conflicting Evidence for population screening ages 40 to 49– Some studies for, some against

• High Risk Screening– Uncontrolled longitudinal follow up of high risk cohort

Screening / Mammography

• Chart et.al. 1997 - Canadian High Risk Programme– 1044 women categorised as high, moderate or low risk

– 6 year follow up, mammography and breast examination

– in high risk group 7/381 had tumours at presentation

– 5/381 high risk developed tumours on follow up

• Lalloo et.al. 1998 - Manchester high risk breast clinic– 1259 women with a lifetime risk of breast cancer > 1 in 6

– 7 tumours prevalent (4 were in situ), 9 tumours incident

– 2 tumours were detected by self examination between screens

– 6 of incident tumours were palpable

Tamoxifen prevention Studies

The Breast Cancer Prevention Trial (P1)– Women at “increased Risk”, 13388 cases, 5 year follow up

– Tamoxifen reduced breast cancer risk (RR 0.5)

– Increased endometrial cancer and pulmonary embolus + cataract

– Overall mortality not significantly lower

Royal Marsden Chemoprevention Trial– 8 year follow up of 2471 women, power 90% for 50% effect

– No detectable effect on breast cancer

Hormone Replacement Therapy

• No study has looked at HRT in BRCA mutation carriers

• Generally, HRT confers a small increased risk of breast cancer.

• HRT decreases cardiovascular and osteoporotic events.

• In one large meta-analysis (anonymous 1997) positive family history did not show a significantly increased risk of breast cancer in HRT users as opposed to non-users. Numbers analysed were small.

• Breast cancer in BRCA1 carriers is often oestrogen receptor negative.

Prophylactic Surgery• Mastectomy

– Various modelling approaches looking at cost/benefit– Hartmann et.al. 1999 Retrospective study

• Estimated 90% reduction in breast cancer incidence• Did not take other post-operative morbidity into account

• Oophorectomy– Rebbeck et.al. 1998 (ASHG abstract)

• reduction in breast cancer in BRCA1 mutation carriers– Papillary serous carcinoma of peritoneum may arise in BRCA1 carriers after

oophorectomy. (Schorge et.al.1998, Piver et.al. 1993.)

Conclusions

• The benefits of prophylactic tamoxifen remain unproven.

• Family history of breast cancer is not necessarily a contraindication for HRT.

• More studies are needed

Conclusions

• Prophylactic mastectomy can have a role in patients at high risk of breast cancer.

• Prophylactic oophorectomy may reduce risk of ovarian cancer and breast cancer. Peritoneal tumours may still arise.

Conclusions

• Screening of high risk patients can be effective in detecting breast cancer.

• Overall benefit of screening remains to be demonstrated.

Presenting Risk

Lifetime Absolute Risk

• Your lifetime risk of dying is 100%

Relative Risk

• Your Risk of dying is 1X that of the population

Absolute Risk over Time

• Your risk of dying over the next 10 years is

2%

Presenting Risk

• Absolute Risk over a given time is – Easy to understand– Easy to base decisions upon

• Relative Risk can be converted to absolute risk– Assuming relative risk is constant over time– Assuming individual belongs to the population studied

• (Dupont and Plumber 1996)

How Can Risk be Estimated from Family History ?

Risk Analysis - Situation A

• Mother and Sister Affected with Breast Cancer

60

Risk Analysis - Situation B

• Three Relatives Affected with Breast Cancer

Risk Analysis - Situation C

• Mother Affected with Ovarian Cancer and Sister with Breast Cancer

Risk Analysis - Situation D

• Mother affected with bilateral breast cancer

40/55

40

Risk Analysis - Situation E

• Two second degree relatives with breast cancer < age 60

Risk Analysis - Situation F

• Mother affected with breast cancer age 45 and ovarian age 65

Estimation of Risk

• Empirical Data Studies– OPCS data set 3295 cases of breast cancer– CASH data set 4730 cases of breast cancer– Meta-analysis - Pharoah et.al.74 published studies

• Modelling of Data– CASH data– Gail Model 2,852 cases of breast cancer

• Linkage/Computer Analysis– Cyrillic (Uses CASH data)

Empirical Estimation of Risk

Advantages

– No model is assumed

– Information is directly applicable

Disadvantages

– Data is population specific

– Data only covers a small range of situations

– Large studies are needed for meaningful data

CASH data (Claus et.al. 1990)

• Kaplan-Meier estimates of cumulative risk– By age of onset of breast cancer in 1o relative

• Hazard ratios for other family histories

– Sister and mother affected RR 5.9 (3.9-8.9)

– Two sisters affected RR 3.6 (2.1-6.1)

– One mother, 2 sisters RR 17 (9.4-31)

Meta-AnalysisPharoah et.al.1997

• Applicable to limited situations

– Affected 1o relative RR 2.1 (2.0-2.2)– Mother and sister RR 3.6 (2.5-5.0)– Sister affected <50 RR 2.7 (2.4-3.2)– Mother affected <50 RR 2.0 (1.7-2.4)

Estimation of Risk Using Models

Advantages

– Data can be widened to a greater range of situations

Disadvantages

– Can generalise to situations where data is not applicable

– Risks are often based on a small number of data points

– Risks calculated are population specific

Modelling of Breast Cancer Risk

“Gail” Model (BCDDP data)– Incorporates age at menarche, parity, and affected 1o relatives

– Curves given to estimate 10, 20 and 30 year risk

– No mode of inheritance assumed

CASH data model– Using age of onset and first degree relative data only

– Segregation analysis suggests dominant major locus

– Give cumulative risk curves based on relatives and age of onset

Linkage/Computer Analysis

Likelihood of developing breast cancer

=

Likelihood of dominant mutation in family

+ Likelihood of carrying mutation

+ Likelihood of developing cancer if mutation carrier(Mendel to determine LOD score, CASH data

penetrance figures/ current age)

Linkage/Computer Risk Analysis

• Advantages– Generates a risk for a complex situation

• Uses age of onset• Uses unaffected individuals

– Easy to apply– Removes subjective element

• Disadvantages– Assumes single dominantly inherited gene– Assumes one set of penetrance values for a single gene– Prone to “rubbish in, rubbish out” phenomena– Essentially unvalidated

So Which is Best ?

20 Year Risk ComparisonRelative Risk

(Absrisk)CASH

(Empirical)GRAIL Cyrillic 3

SituationA (M+S)

13.7% 11%/21%* 8.5% 7.4%

SituationB (3 relatives)

(12.6%) (5.3%) (4%) 8.1%

SituationC (ov +Br)

- 14.4% - 6.3%

SituationD (Bilat. Br.)

9.7%/16.5 10%* 8.5% 10.3%

SituationE (2X2o Br.)

6.7% - - 5.1%

SituationF (Br/Ov)

- 14.4% - 7.4%

Conclusions

• Different systems for risk estimation can give different results.

• Empirical risk calculation systems can only apply to well defined situations.

• Computerised risk assessment is based on assumptions that are not necessarily valid.

Where do we start screening ?

Current Guidelines

• SIGN Guideline - 3 times population risk

– 1o relative with bilateral breast cancer*– 1o relative with breast cancer <40– 1o male relative with breast cancer– 1o relative with breast and ovarian– 2 first or second degree relatives with breast cancer < 60– 3 first or second degree relatives with breast cancer

Bilateral breast cancer in 1o relative

• 2-5% of breast cancer is bilateral• CASH data (USA families)

– risk same as if unilaterally affected relative

• Tulinius et.al. 1992 (Icelandic families)– RR 4.4* (3.39-6.49), RR 9 if first onset <45

• Houlston (British families, OPCS data)– RR 4.78 (0.12 to 26.62) postmenopausal onset– RR 7.78 (0.94 to 28.08) premenopausal onset

10 Year Risk Estimates

Relative RiskAge

1 2 3 5 10

20 0.1 0.1 0.2 0.3 0.6

30 0.5 0.9 1.4 2.3 4.6

40 1.5 3.0 4.5 7.4 14

50 2.5 5.0 7.4 12 23

60 2.5 4.9 7.3 12 22

70 2.4 4.7 7.0 11 21

Conclusions

• Setting the criteria for screening should depend on

– Estimation of absolute risk (age dependant)

– Effectiveness of screening (may be age specific)

– Resources available

– A sensible risk estimation system

Future Research

• Validation of risk estimation

– Detailed comparison of methods of risk analysis– Application to pedigrees with known outcome

• (A retrospective-prospective study !)

Future Research

• Validation of screening protocols for high risk individuals.

Future Research(Long term)

• Audit of effectiveness of screening protocols and accuracy of risks calculated.

• This will be greatly facilitated by an effective computerised database.