Person:

Parmigiani, Giovanni

Purpose: Health care providers need simple tools to identify patients at genetic risk of breast and ovarian cancers. Genetic risk prediction models such as BRCAPRO could fill this gap if incorporated into Electronic Medical Records or other Health Information Technology solutions. However, BRCAPRO requires potentially extensive information on the counselee and her family history. Thus, it may be useful to provide simplified version(s) of BRCAPRO for use in settings that do not require exhaustive genetic counseling. Methods: We explore four simplified versions of BRCAPRO, each using less complete information than the original model. BRCAPROLYTE uses information on affected relatives only up to second degree. It is in clinical use but has not been evaluated. BRCAPROLYTE-Plus extends BRCAPROLYTE by imputing the ages of unaffected relatives. BRCAPROLYTE-Simple reduces the data collection burden associated with BRCAPROLYTE and BRCAPROLYTE-Plus by not collecting the family structure. BRCAPRO-1Degree only uses first-degree affected relatives. We use data on 2713 individuals from seven sites of the Cancer Genetics Network and MD Anderson Cancer Center to compare these simplified tools with the Family History Assessment Tool (FHAT) and BRCAPRO, with the latter serving as the benchmark. Results: BRCAPROLYTE retains high discrimination, however, because it ignores information on unaffected relatives, it overestimates carrier probabilities. BRCAPROLYTE-Plus and BRCAPROLYTE-Simple provide better calibration than BRCAPROLYTE, so they have higher specificity for similar values of sensitivity. BRCAPROLYTE-Plus performs slightly better than BRCAPROLYTE-Simple. The Areas Under the ROC curve are 0.783 (BRCAPRO), 0.763 (BRCAPROLYTE), 0.772 (BRCAPROLYTE-Plus), 0.773 (BRCAPROLYTE-Simple), 0.728 (BRCAPRO-1Degree), and 0.745 (FHAT). The simpler versions, especially BRCAPROLYTE-Plus and BRCAPROLYTE-Simple, lead to only modest loss in overall discrimination compared to BRCAPRO in this dataset. Conclusion: Simplified implementations of BRCAPRO can be used for genetic risk prediction in settings where collection of complete pedigree information is impractical.

BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee’s family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase some of the most notable features of the software resulting from these recent changes. We provide examples highlighting each feature, using artificial pedigrees motivated by complex clinical examples. We illustrate how BRCAPRO is a comprehensive software for genetic risk prediction with many useful features that allow users the flexibility to incorporate varying amounts of available information.

Genetic risk prediction models such as BRCAPRO are used routinely in genetic counseling for identification of potential BRCA1 and BRCA2 mutation carriers. They require extensive information on the counselee and her family history, and thus are not practical for primary care. To address this gap, we develop and test a two-stage approach to genetic risk assessment by balancing the tradeoff between the amount of information used and accuracy achieved. The first stage is intended for primary care wherein limited information is collected and analyzed using a simplified version of BRCAPRO. If the assessed risk is sufficiently high, more extensive information is collected and the full BRCAPRO is used (stage two: intended for genetic counseling). We consider three first-stage tools: BRCAPROLYTE, BRCAPROLYTE-Plus, and BRCAPROLYTE-Simple. We evaluate the two-stage approach on independent clinical data on probands with family history of breast and ovarian cancers, and BRCA genetic test results. These include population-based data on 1344 probands from Newton-Wellesley Hospital and mostly high-risk family data on 2713 probands from Cancer Genetics Network and MD Anderson Cancer Center. We use discrimination and calibration measures, appropriately modified to evaluate the overall performance of a two-stage approach. We find that the proposed two-stage approach has very limited loss of discrimination and comparable calibration as BRCAPRO. It identifies a similar number of carriers without requiring a full family history evaluation on all probands. We conclude that the two-stage approach allows for practical large-scale genetic risk assessment in primary care.