Cancer Research UK Genetic Epidemiology Unit, Strangeways Research Laboratory, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Cancer Genomics Laboratory, Oncology and Molecular Endocrinology Research Center, Centre Hospitalier, Universitaire de Québec and Laval University, Québec, Canada

Other members of the INHERIT (INterdisciplinary HEalth Research International Team on BReast CAncer susceptibility) BRCAs involved in clinical aspects of the program are listed in the Acknowledgments section

Abstract

Introduction

Several genetic risk models for breast and ovarian cancer have been developed, but their applicability to specific populations has not been evaluated. We used data from French-Canadian families to evaluate the mutation predictions given by the BRCAPRO and BOADICEA models. We also used this data set to estimate the age-specific risks for breast and ovarian cancer in

Methods

A total of 195 families with multiple affected individuals with breast or ovarian cancer were recruited through the INHERIT (INterdisciplinary HEalth Research International Team on BReast CAncer susceptibility) BRCAs research program. Observed

Results

The BOADICEA model predicted accurately the number of

Conclusion

The BOADICEA model predicts accurately the carrier probabilities in French-Canadian families and may be used for counselling in this population. None of the penetrance estimates was significantly different from previous estimates, suggesting that previous estimates may be appropriate for counselling in this population.

Introduction

Population isolates or founder populations provide a particular challenge for risk models, because the frequency of mutations may be altered by the occurrence of specific founder mutations that may attain relatively high frequency. Notable examples of this are the founder

A number of models that predict

In the present study we used data from families of French-Canadian origin identified through the INHERIT (INterdisciplinary HEalth Research International Team on BReast CAncer susceptibility) BRCAs integrated clinical research program to assess the BRCAPRO and BOADICEA models of genetic susceptibility to breast cancer

Materials and methods

Ascertainment of families

High-risk French-Canadian breast and/or ovarian families were recruited into a research project started in 1996, which subsequently evolved into a large ongoing interdisciplinary research program designated INHERIT BRCAs

Following pre-test education sessions and detailed analysis of familial history, individuals were recruited if their family met one or more of the following strict criteria: the family had at least four individuals with breast and/or ovarian cancer diagnosed at any age in first-degree or second-degree relatives; the family had three first-degree relatives affected with breast and/or ovarian cancer at any age; or the family carried a deleterious mutation already identified in the

A total of 191 families were recruited before 1 January 2003, and data from these families were used to assess the ability of the models to predict

Mutation testing

Individuals were assumed to be

Once a signed informed consent form had been obtained from each participant, 40 ml blood was drawn and genomic DNA extracted using the guanidine hydrochloride-proteinase k method ^{®}. No additional analysis was performed for 30 families. Testing services were performed according to the Memorandum of Understanding of the US National Cancer Institute (NCI) for NCI-funded research testing services for

Statistical methods

Penetrance estimation

We used data from the families in which deleterious

The parameters were estimated by maximum likelihood using a modified segregation analysis implemented in the computer program MENDEL _{i}(t) = λ_{0}(t)RH(t), where λ_{0}(t) is the baseline incidence rate for noncarriers and RH(t) is the relative hazard at age t for carriers compared with noncarriers.

Nonmutation carriers were assumed to be susceptible to the population incidence rates for Quebec during 1978–1981 (International Agency for Research on Cancer, Cancer Incidence in Five Continents, Volume V, IARC Scientific publications, Lyon, 1987). The models were parameterized in terms of the age-specific log relative hazard for breast and ovarian cancer compared with the population risks. More details on this method can be found in the report by Antoniou and coworkers

To correct for ascertainment, we employed the sequential ascertainment correction scheme described by Cannings and Thompson

To assess the consistency of the Quebec families with genetic models that incorporate the effects of

Mutation prediction models

BOADICEA is a computerized risk assessment program that can be used to compute the probability of detecting a

The BRCAPRO program computes the probability that an individual carries a

To provide a more direct comparison between the models, two sets of predictions were carried out under the BOADICEA model: taking into account the entire family as reported, which we refer to as 'full pedigrees'; and taking into account only the first-degree and second-degree relatives of the first screened individual.

Model comparisons

The models were calibrated by comparing the observed and expected numbers of mutations. The models were also evaluated in terms of the Brier score (BS)

Where _{i }is the probability of detecting a _{i }is 0 if the proband was found not to carry a mutation and 1 if the proband was found to carry a

The models were also evaluated in terms of their ability to predict accurate probabilities (reliability), and the ability of the predictions to separate correctly the carriers and noncarriers (resolution/discrimination). These were assessed in terms of the attributes diagrams. This involved ranking the predicted probabilities and then dividing them into quantiles. The average predicted probability was computed for each quantile and this was then plotted against the observed mutation frequency in the quantile. Ninety-five per cent confidence intervals (CIs) for the observed frequencies were computed using a binomial distribution.

Receiver operating characteristic (ROC) curves were used to assess the specificity and sensitivity of the various models. Sensitivity here is defined as the proportion of tested individuals with mutations with a detection or carrier probability higher than a given value (cutoff), and specificity is the proportion of individuals without mutations with a detection probability lower than the cutoff. The ROC curves plot the sensitivity against the specificity at all possible cutoffs and show the trade-off between the two measures. The area under the ROC curve is a measure of the accuracy of a model, such that the higher the area, the more accurate is the model (an area of 1 represents a perfect fit, and an area of 0.5 represents no predictive value).

Results

A total of 191 families were recruited before January 2003. Three of those families were recruited because of prior knowledge of a mutation segregating within the family and were therefore excluded from this analysis. Table

Summary statistics for the 188 families used in predicting the mutation status of the first screened individual

Full pedigrees

First- and second-degree relatives only

Mean number of individuals per family

27.9

15.9

Number of BC cases^{a}

993

612

Number of OC cases

66

54

Number of bilateral BC cases

70

58

Cases with BC and OC

26

12

Number of male BC cases

19

11

Median age at BC diagnosis (years)

51

51

Median age at OC diagnosis (years)

57

58

^{a}Corresponds to females only. BC, breast cancer; OC, ovarian cancer.

BC, breast cancer; OC, ovarian cancer.

Observed and predicted number of mutations in the first screened individual by cancer status

Cancer status

Model

^{a}

^{a}

Noncarriers

Unaffected

Observed

3

5

96

BOADICEA

Full pedigrees

3.49

6.30

94.21

Second-degree relatives

2.46

4.10

97.44

BRCAPRO

10.74

4.20

89.06

Breast cancer

Observed

6

12

55

BOADICEA

Full pedigrees

8.68

13.17

51.14

Second-degree relatives

7.42

8.75

56.83

BRCAPRO

26.29

10.90

36.97

Ovarian cancer

Observed

5

2

4

BOADICEA

Full pedigrees

3.52

0.63

6.85

Second-degree relatives

2.27

0.41

8.31

BRCAPRO

5.12

0.56

5.32

Totals

Observed

14

19

155

BOADICEA

Full pedigrees

15.69

20.10

152.21

Second-degree relatives

12.15

13.26

162.59

BRCAPRO

42.16

15.67

130.35

^{a}Not mutually exclusive under BRCAPRO.

Among all the first screened individuals, 14 ^{2 }goodness-of-fit test for comparing the total number of observed mutations with the predicted number was not significant for BOADICEA but was highly significant for BRCAPRO (^{-6}). When information on all available relatives was used, the predicted numbers under BOADICEA were close to the observed numbers for both unaffected probands and among the breast and ovarian cancer patients. BRCAPRO over-predicted the number of

Table

Observed and expected number of mutations among the first screened individuals by number of cancers in the family

Number of cancers, age under 70 years

Model

Noncarriers

Observed

Expected

Observed

Expected

Observed

Expected

≥3 BC, ≥1 OC

BOADICEA

Full pedigrees

9

6.92

4

3.80

21

23.28

Second-degree relatives

6

3.12

1

1.49

7

9.38

BRCAPRO

6

7.80

1

2.35

7

3.98

≥4 BC, 0 OC

BOADICEA

Full pedigrees

2

6.20

14

14.75

90

85.05

Second-degree relatives

2

4.11

12

8.60

57

58.29

BRCAPRO

2

19.40

12

8.27

57

43.37

3 BC, 0 OC

BOADICEA

Full pedigrees

0

0.82

1

1.20

25

23.97

Second-degree relatives

1

1.09

2

1.77

37

37.13

BRCAPRO

1

5.43

2

2.97

37

31.60

2 BC, any OC

BOADICEA

Full pedigrees

3

1.75

0

0.34

19

19.91

Second-degree relatives

5

3.83

4

1.40

54

57.79

BRCAPRO

5

9.53

4

2.08

54

51.50

BC, breast cancer; OC, ovarian cancer.

The BS for the probability of detecting a

Resolution and reliability

Figure

Attributes diagram

Attributes diagram. Shown is an attributes diagram comparing the predicted carrier probabilities and the observed carrier frequencies for

The average predicted probability in each sextile was very close to the observed frequency under the BOADICEA model when the 'full pedigrees' were considered, indicating that under such circumstances the model discriminates between carriers and noncarriers and it gives accurate probabilities. When only the second-degree relatives were considered the average predicted probability was slightly lower than the observed frequency in each sextile, indicating somewhat lower discrimination. However, the 95% CIs of the observed frequencies for all sextiles include the average predicted probabilities. BRCAPRO provided very good discrimination between carriers and noncarriers, but it did not give accurate probabilities, especially at the two upper sextiles. In these cases the average predicted probabilities were much higher than the observed frequencies and outside their 95% CIs.

The ROC curves under BOADICEA using the full pedigrees and under BRCAPRO are shown in Fig.

ROC curves

ROC curves. Shown are ROC curves for the BOADICEA and BRCAPRO predictions of carrying either a

Risks for breast and ovarian cancers

Table

Estimated cumulative risks of breast and ovarian cancer in

Age (years)

BC

OC

BC or OC

BC

OC

BC or OC

30

3

0

4

0.3

0

0.4

40

13

0

14

13

0.2

26

50

20 (0–45)

1 (0–10)

23 (0–48)

21 (0–55)

0.4 (0–2)

35 (0–64)

60

71

38

83

33

49

55

70

72 (0–93)

38 (0–78)

83 (34–96)

75 (0–97)

49 (0–81)

89 (34–98)

Shown are percentage cumulative risks (95% confidence interval). BC, breast cancer; OC, ovarian cancer.

Data from 27 families were used in the

Discussion

In the present study we used data from French-Canadian families included in the INHERIT program to evaluate the mutation risk prediction models BOADICEA and BRCAPRO

Penetrance was estimated using data from families segregating

The BOADICEA and BRCAPRO genetic models for breast cancer susceptibility were used to compute expected numbers of mutations within subcategories of families. Empirical models such as the Myriad II model

The total number of mutations was significantly over-predicted by BRCAPRO. This was due to the over-prediction of

The Brier scores also indicate that BRCAPRO is not accurate in predicting individual carrier probabilities, whereas the BOADICEA predictions are compatible with the observations. On the other hand the ROC curves indicate that BRCAPRO and BOADICEA both discriminate well between carriers and noncarriers. Thus, BRCAPRO and BOADICEA perform similarly in terms of ranking individuals by carrier probability, but the absolute carrier probabilities are only reliable for BOADICEA. To achieve comparable sensitivity and specificity, the cutoffs under the two models are therefore quite different. Users of the models must be aware of these issues when deciding whether to refer an individual for testing on the basis of carrier or mutation detection probabilities given by a particular model.

This is the first time the updated version of BOADICEA has been used to evaluate its prediction of mutation status in an independent data set. The version used here varies from the previous reported model

Even though the BOADICEA model was developed using data from the UK, it seems to fit the occurrence of breast cancer in French-Canadian high-risk families. As demonstrated earlier, the penetrance estimates derived from these families are not significantly different from those used in BOADICEA. The good fit to these data suggests that the overall

Conclusion

In the present study we estimated breast and ovarian cancer risks conferred by

Abbreviations

BOADICEA = Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm; BS = Brier score; CI = confidence interval; INHERIT = INterdisciplinary Health Research International Team; NCI = National Cancer Institute; ROC = receiver operating characteristic; VUS = variants of unspecified significance.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

ACA was responsible for the analysis and data cleaning, and led the manuscript preparation. FD is a co-principal investigator in INHERIT BRCAs, initiated and coordinated collaborative efforts, was involved in the study design, and participated in the manuscript preparation. PS was responsible for data cleaning. JS was responsible for supervising mutation screening and laboratory work, initiated the study in high-risk French-Canadian families in 1996 (CBCRA grant), obtained further funding to start the international integrated research program INHERIT BRCAs, and was involved in revising the manuscript. DFE is a co-principal investigator of INHERIT BRCAs, and was involved in the development of the analytical design and in manuscript preparation.

Acknowledgements

The authors are indebted to the participants for their generosity and providing DNA samples. We would also like to thank Nathalie Bolduc, Claire Brousseau, Dr Sylvie Délos, Marie-Andrée Lajoie, Pascale Léger, Hélène Malouin, Josée Rhéaume, Andrée McMillan and Tina Babineau for genetic counselling and clinical data management; and Dr Martine Dumont, Gilles Leblanc, Carolle Samson and Martine Tranchant for mutation screening and skilful technical assistance. We also appreciate the advice received from ethics committees. This work was supported by the Canadian Institutes of Health Research (CIHR) for the INHERIT BRCAs research program, Fonds de la Recherche en Santé du Québec (FRSQ)/Réseau de Médecine Génétique Appliquée (RMGA) and the Canadian Breast Cancer Research Alliance. ACA is funded by Cancer Research UK; FD is a recipient of a Research Career Award in the Health Sciences by IRSC/Rx&D HRF; PS was funded by the INHERIT BRCAs program; JS is Chair holder of the Canada Research Chair in Oncogenetics; and DFE is a Principal Research Fellow of Cancer Research UK.

The INHERIT BRCAs members are as follows: Paul Bessette (Service de Gynécologie, Centre Hospitalier Universitaire de Sherbrooke, Fleurimont, Quebec, Canada); Peter Bridge (Molecular Diagnostic Laboratory, Alberta Children's Hospital, Calgary, Canada); Jocelyne Chiquette and Louise Provencher (Clinique des Maladies du sein Deschênes-Fabia, Hôpital du saint-Sacrement, Quebec, Canada); Rachel Laframboise (Service de Médecine Génétique, CHUQ, Pavillon CHUL, Quebec, Canada); Jean Lépine, Centre Hospitalier Regional de Rimouski, Rimouski, Canada); Bernard Lespérance and Roxane Pichette (Service d'hémato-oncologie, Hôpital du Sacré-Coeur, Montréal, Canada); Marie Plante (Service de Gynécologie, CHUQ, L'Hôtel-Dieu de Québec, Quebec, Canada); and Patricia Voyer (Clinique des maladies du sein, Carrefour de Santé de Jonquière, Jonquière, Canada).