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Incorporating tumor immunohistochemical markers in BRCA1 and BRCA2 carrier prediction

Breast Cancer Research200810:401

https://doi.org/10.1186/bcr1866

Published: 20 March 2008

Keywords

Breast CancerEstrogen ReceptorProgesterone ReceptorMarker StatusBRCA2 Carrier

The pathology of a patient's breast carcinoma can be highly indicative of BRCA1 mutation status. Compared to sporadic and BRCA2 deficient breast carcinomas, BRCA1 deficient carcinomas tend to be estrogen receptor (ER) negative, progesterone receptor (PR) negative, HER2 negative, cytokeratin (CK)5/6 positive, and CK14 positive [1, 2]. BRCAPRO is an accurate, widely used risk prediction model that estimates the probability that an individual carries a deleterious germline mutation in BRCA1 or BRCA2 based upon their personal and/or family history of breast and ovarian cancer. Recently, when BRCAPRO carrier probabilities were updated using a patient's pathological sub-type, in a two-step process, risk estimation was improved [3]. Here we describe how we have substantially improved BRCAPRO by directly integrating marker information into the estimation of carrier probabilities and cancer risk.

The theory underlying BRCAPRO is described elsewhere [4, 5]. Briefly, the model transforms information on mutation frequency, disease penetrance, and Mendelian transmission patterns into gene carrier probabilities through application of Bayes' rule. For unaffected individuals the model predicts cancer risk from a weighted average of the penetrance for mutation carriers and non-carriers, with the estimated carrier probabilities as weights. The derived conditional probability of the marker status given carrier status used in our calculations were obtained from published data [1, 2] and are presented in Table 1. These conditional probabilities are derived from a single study and from a highly selected group of high-risk breast cancer families and thus should be interpreted with some care. The following assumptions were made about the use of markers in combination. First, for ER negative tumors, carrier probabilities were updated using CK5/6 and CK14 status, if available. PR status does not influence carrier predictions if ER status is included because of a strong correlation between ER and PR. Second, Her-2 neu status was not used because it was not predictive of marker status after accounting for ER [1]. Third, marker information was assumed not to be associated with BRCA2 mutation status, because BRCA2 and sporadic tumors have similar marker profiles. Updating BRCA1 probability can have a residual impact on the BRCA2 carrier probability.
Table 1

Conditional probability of marker status given carrier status

Marker status

Marker status given carrier status

ER

CK14

CK5/6

PR

BRCA1

Non-BRCA1

+

.

.

.

0.1

0.65

-

+

+

.

0.438

0.016

-

+

-

.

0.124

0.048

-

-

+

.

0.134

0.024

-

-

-

.

0.209

0.24

.

.

.

+

0.21

0.63

Estimates obtained from Lakhani and colleagues [1]. Plus signs (+) denote positive; hyphens (-) denote negative; periods (.) denote missing.

Our updated software package is freely available from [6, 7]. A clinical example of a 54 year old female counselee with breast cancer at 45 whose mother had breast cancer at age 63 and no other family history, under various marker scenarios, is presented in Table 2. Without marker data, the counselee's carrier probabilities for BRCA1 and BRCA2 are 2.2% and 2.3%, respectively. These probabilities are 5.5% if her tumor is ER negative or 0.35% if ER positive. Changes in carrier probability generally correspond to markedly different clinical recommendations regarding genetic testing and cancer prevention. Including this information greatly impacts BRCAPRO carrier probabilities and improves distinction between BRCA1 and non-BRCA1 breast tumors.
Table 2

Example of clinical application: BRCA1 and BRCA2 carrier probabilities (%) under selected marker profile scenarios

Cancer history

  

Counselee: 54 year old with breast cancer at 45

Mother: breast cancer at 63

Estimated carrier probabilities (%)

ER

CK14

CK5/6

PR

ER

CK14

CK5/6

PR

BRCA1

BRCA2

.

.

.

.

.

.

.

.

2.2

2.3

-

.

.

.

.

.

.

.

5.5

2.3

+

.

.

.

.

.

.

.

0.35

2.4

.

.

.

-

.

.

.

.

4.6

2.3

.

.

.

+

.

.

.

.

0.75

2.4

-

+

+

.

.

.

.

.

38

1.5

-

.

.

.

-

.

.

.

11

2.1

-

.

.

.

+

.

.

.

2.1

2.3

-

+

+

.

-

+

+

.

36

1.5

-

+

-

.

-

+

-

+

2.1

2.3

No other family history is available. Plus signs (+) denote positive; hyphens (-) denote negative; periods (.) denote missing.

Abbreviations

CK: 

cytokeratin

ER: 

estrogen receptor

PR: 

progesterone receptor.

Declarations

Authors’ Affiliations

(1)
The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, USA
(2)
Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
(3)
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
(4)
Department of Pathology, Johns Hopkins School of Medicine, Baltimore, USA
(5)
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA

References

  1. Lakhani SR, Reis-Filho JS, Fulford L, Penault-Llorca F, van der Vijver M, Parry S, Bishop T, Benitez J, Rivas C, Bignon YJ, Chang-Claude J, Hamann U, Cornelisse CJ, Devilee P, Beckmann MW, Nestle-Krämling C, Daly PA, Haites N, Varley J, Lalloo F, Evans G, Maugard C, Meijers-Heijboer H, Klijn JG, Olah E, Gusterson BA, Pilotti S, Radice P, Scherneck S, Sobol H, et al: Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin Cancer Res. 2005, 11: 5175-5180. 10.1158/1078-0432.CCR-04-2424.View ArticlePubMedGoogle Scholar
  2. Lakhani SR, Van DV, Jacquemier J, Anderson TJ, Osin PP, McGuffog L, Easton DF: The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002, 20: 2310-2318. 10.1200/JCO.2002.09.023.View ArticlePubMedGoogle Scholar
  3. James PA, Doherty R, Harris M, Mukesh BN, Milner A, Young MA, Scott C: Optimal selection of individuals for BRCA mutation testing: a comparison of available methods. J Clin Oncol. 2006, 24: 707-715. 10.1200/JCO.2005.01.9737.View ArticlePubMedGoogle Scholar
  4. Chen S, Wang W, Broman KW, Katki HA, Parmigiani G: Bayes-Mendel: and R environment for Mendelian risk prediction. Stat Appl Genet Mol Biol. 2004, 3: Article21-PubMedPubMed CentralGoogle Scholar
  5. Parmigiani G, Berry D, Aguilar O: Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet. 1998, 62: 145-158. 10.1086/301670.View ArticlePubMedPubMed CentralGoogle Scholar
  6. The BayesMendel R Package Archive. [http://astor.som.jhmi.edu/BayesMendel/Rpackage.html]
  7. CancerGene. [http://www.utsouthwestern.edu//utsw/cda/dept47829/files/65844.html]

Copyright

© BioMed Central Ltd 2008

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