Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database

Background: Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New-strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB. Methods: Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families. Results: Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 x 10(-8), logarithm of the odds (LOD) equivalent 6.64]. In the region, 11,14 and II:1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi -cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers. Conclusions: Our results suggest pedigree -informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high -risk breast cancer pedigrees. Impact: Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population -based multisource databases can provide.