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

2020 
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 multi-source 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 multi-source databases, we describe two 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 genomewide significant breast cancer locus at 2q13 (p=1.6x10-8, LOD equivalent 6.64). In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected co-aggregations (such as, breast with both prostate, ovarian, and melanoma), and also identify novel patterns, including 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 etiological heterogeneity that large, population-based multi-source databases can provide.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    65
    References
    6
    Citations
    NaN
    KQI
    []