Hanson Heidi A, Leiser Claire L, Madsen Michael J, Gardner John, Knight Stacey, Cessna Melissa, Sweeney Carol, Doherty Jennifer A, Smith Ken R, Bernard Philip S, Camp Nicola J
Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
Utah Population Database, University of Utah, Salt Lake City, Utah.
Cancer Epidemiol Biomarkers Prev. 2020 Apr;29(4):807-815. doi: 10.1158/1055-9965.EPI-19-0912. Epub 2020 Feb 25.
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.
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.
Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [ = 1.6 × 10, logarithm of the odds (LOD) equivalent 6.64]. In the region, and 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.
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.
Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.
此前，基于家系的设计和高风险家系已证明在发现高风险和中度风险的种系乳腺癌易感基因方面具有价值。然而，遗传异质性是阻碍研究进展的主要障碍。需要新的策略和分析方法来取得进一步进展。多源数据库的日益普及为通过更好地描述疾病来解决异质性提供了一个契机。涉及基于家系设计的进展的特定资源包括包含家庭结构的资源，如犹他州人口数据库（UPDB）。为了说明多源数据库的广泛用途和潜在力量，我们描述了两种不同的基于家系的新方法，以减少UPDB中的异质性。
我们的第一种方法侧重于在基因定位中使用家系信息丰富的乳腺肿瘤表型。我们的第二种方法侧重于识别具有相似多效性的家系。我们使用一种新颖的受网络启发的聚类技术来探索高风险乳腺癌家系的多癌特征。
我们的第一种方法在2q13处鉴定出一个全基因组显著的乳腺癌位点[=1.6×10，优势对数（LOD）等效于6.64]。在该区域， 和 特别令人感兴趣，它们是参与炎症的关键细胞因子基因。我们的第二种方法识别出五种多癌风险模式。这些聚类包括预期的共聚集（如乳腺癌与前列腺癌、卵巢癌和黑色素瘤），还识别出新颖的模式，包括与子宫癌、甲状腺癌和膀胱癌的共聚集。
我们的结果表明，家系信息丰富的肿瘤表型可以定位乳腺癌基因，并且高风险乳腺癌家系存在各种不同的癌症多效性。
这两种方法都说明了基于人群的大型多源数据库在降低病因异质性方面的潜力。
