Abstract:  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. 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 2,713 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. 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. Thus, we conclude that simplified implementations of BRCAPRO can be used for genetic risk prediction in settings where collection of complete pedigree information is impractical. 

BiswasHughes2013BCRTSimplifyingClinicalUseOfTheGeneticRiskPredictionModelBRCAPRO

Models

Biswas S, Atienza P, Chipman J, Hughes KS, Barrera AM, Amos CI, Arun B, Parmigiani G. Simplifying clinical use of the genetic risk prediction model BRCAPRO. Breast Cancer Res Treat. 2013 Jun;139(2):571-9. doi: 10.1007/s10549-013-2564-4. Epub 2013 May 21.

Chipman J, Drohan B, Blackford A, Parmigiani G, Hughes KS, Bosinoff P. Providing Access to Risk Prediction Tools via the HL7 XML-Formatted Risk Web Service. Breast Cancer Research and Treatment. (In Press)

Guidelines

Murphy CD, Lee JM, Drohan B, Euhus DM, Kopans DB, Rafferty EA, Specht MA, Smith BL, Hughes KS. The American Cancer Society Guidelines for Breast Screening with Magnetic Resonance Imaging: An Argument for Genetic Testing. Cancer, 2008 Dec 1;113(11):3116-20.

Ozanne EM, Drohan B, Bosinoff P, Semine A, Jellinek M, Cronin C, Millham F, Dowd D, Rourke T, Block C, Hughes KS. Which Risk Model to Use? Clinical Implications of the ACS MRI Screening Guidelines. Cancer Epidemiol Biomarkers Prev. 2013 Jan;22(1):146-9. doi: 10.1158/1055-9965.EPI-12-0570. Epub 2012 Oct 23.

Jonathan Chipman • Brian Drohan •Amanda Blackford• Giovanni Parmigiani •Kevin Hughes• Phil Bosinoff

Abstract Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging.  Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics’ needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called ‘‘Risk Service’’, which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service.  The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data  using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator.  This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future research, thus building a rich multicenter resource.

ChipmanHughes2013BCRTProvidingAccessToRiskPredictionToolsViaTheHL7XML-FormattedRiskWebService