Mutation prevalence tables for hereditary cancer derived from multigene panel testing

Abstract Multigene panel testing for cancer predisposition mutations is becoming routine in clinical care. However, the gene content of panels offered by testing laboratories vary significantly, and data on mutation detection rates by gene and by the panel is limited, causing confusion among clinicians on which test to order. Using results from 147,994 multigene panel tests conducted at Ambry Genetics, we built an interactive prevalence tool to explore how differences in ethnicity, age of onset, and personal and family history of different cancers affect the prevalence of pathogenic mutations in 31 cancer predisposition genes, across various clinically available hereditary cancer gene panels. Over 13,000 mutation carriers were identified in this high‐risk population. Most were non‐Hispanic white (74%, n = 109,537), but also Black (n  = 10,875), Ashkenazi Jewish (n  = 10,464), Hispanic (n  = 10,028), and Asian (n  = 7,090). The most prevalent cancer types were breast (50%), ovarian (6.6%), and colorectal (4.7%), which is expected based on genetic testing guidelines and clinician referral for testing. The Hereditary Cancer Multi‐Gene Panel Prevalence Tool presented here can be used to provide insight into the prevalence of mutations on a per‐gene and per‐multigene panel basis, while conditioning on multiple custom phenotypic variables to include race and cancer type.

The Penn II Risk Model, BRCA 1 and BRCA 2 Mutation Predictor), and Manchester (Evans et al., 2004;Evans, Lalloo, Wallace, & Rahman, 2005) models for breast cancers and MMRpro (Chen et al., 2006) and PREMM (Kastrinos et al., 2011) for Lynch syndrome. All of these models were developed on relatively small patient populations (<10,000), and each their own unique limitations. More recently, Color Genomics released a website allowing quick perusal of genetic results from 50,000 individuals (Color Data Portal), with filtering criteria to better reflect the clinical characteristics of a given patient.
Here, we describe the development and demonstrate the functionality of an open-access web-based tool that allows the end-user to query mutation prevalence across 49 genes and nine cancer indications with fine-grained control of demographic and clinical history factors taken from 147,994 individuals.  Table 2). Only individuals between 18 and 90 years old are included. For breast cancer, data from estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) statuses were included where available. Pathogenic mutations include variants with a classification of "pathogenic" or "likely pathogenic" based on a five tier variant classification scheme (Pesaran et al., 2016). Data were formatted into a custom R DataFrame

| Limitations of existing models
BOADICEA, BRCAPRO, Myriad II, IBIS, Penn II, and Manchester models for breast cancers are limited to the utility of predictions for BRCA1 and BRCA2, as they are usually the only genes accounted for in these predictions due to the relatively low frequency of pathogenic mutations in other genes, however, BOADACEA now also provides a pretest probability for ATM, PALB2, and CHEK2 mutations (Lee et al., 2019). These models were found to be reasonably accurate (Lindor et al., 2010) data is limited to that provided to the researchers and testing laboratory, although such a limitation is a reality in any cohort represented in a pretest probability model. In addition, while the size of the cohort contributing to this tool is orders of magnitude higher than that in most other currently available pretest probability models or tools, greater numbers of patients are still needed, particularly for ethnic minority populations, genes in which mutations are rare, and queries for highly specific patient characteristics.
Despite these limitations, this tool is representative of patients referred for hereditary cancer panels and is therefore highly relevant to current genetic testing practices. Continued efforts to update this tool and others like it will provide continuous benefits to patients and providers by supplying relevant information in a timely manner.
Thanks to large scale data sharing from commercial and academic entities, it is now possible to explore complex queries that more accurately reflect the clinical experience through a simple web-based interface that draws upon data from large cohorts of patients recently referred for hereditary cancer multi-gene panel testing.

CONFLICT OF INTERESTS
Amal Yussuf, Holly LaDuca, Laura P. Smith, June Fujimoto, Shuwei Li, and Jill S. Dolinsky are all employees of Ambry Genetics.

DATA AVAILABILITY STATEMENT
The application is located at https://www.ambrygen.com/preval ence-tool.