The Genetic Origin of Hereditary Prostate Cancer

前列腺癌是退伍军人中最常见的癌症。在全国范围内，它的死亡率是乳腺癌的76%。 癌症；每41个男人中就有一个会死于癌症。国家精准前列腺癌综合癌症网络指南 癌症治疗包括晚期前列腺癌患者的肿瘤测序，以及生殖系 对有前列腺癌家族史的男性进行测序。它的目的是辨别遗传和遗传 指导精确治疗的躯体驱动因素，尽管也可能揭示家族性癌症风险。但是 指导前列腺癌预后和治疗决策的知识库仍然非常有限。 尽管前列腺癌是所有常见癌症中可遗传风险最大的，但近90%的原因 它的哪种遗传形式仍不清楚。与乳腺癌和其他常见癌症不同的是， 遗传性前列腺癌的病因阻碍了传统的家系连锁调查。目标是 这项研究的目的是使用一种新的替代方案来进一步阐明遗传性前列腺癌的不同原因 家族性病例对照研究设计。我们比较了独立的病例，每个病例都有很强的家族病史 前列腺癌，以筛查的对照组，每个人都有前列腺癌家族史。我们的目标 利用密集的遗传数据来识别具有从共同基因遗传的给定疾病基因的病例 远祖，尽管他们之间的血统关系尚不清楚。我们识别疾病 通过识别病例间祖先基因组片段的过度共享来识别基因，重建单倍型 含有患前列腺癌风险很高的突变，并识别相关的区域遗传变异和 潜在的候选因果突变。我们介绍了一种新的算法来最好地识别前哨变体 检测独立的风险信号，形成临床预测模型。我们进一步描述了 家族性疾病的早期诊断是由遗传基因决定的，有些基因还会影响 咄咄逼人。我们通过在独立的家族性病例对照研究人群中进行复制来识别 假阳性观察为真，我们评估额外的现有病例对照和生物库数据集以 概括相关的观察结果。这项研究将完善精准肿瘤学的知识库和 退伍军人的家族癌症护理。

Prostate cancer is the most common cancer among Veterans. Nationally, its mortality rate is 76% that of breast cancer; one in 41 men will die of it. National Comprehensive Cancer Network Guidelines for precision prostate cancer care have incorporated tumor sequencing for men with advanced-stage prostate cancer, and germline sequencing of men with a family history of prostate cancer. Its purpose is to discern inherited as well as somatic drivers to guide precision therapy, though also potentially uncovering familial cancer risks. But the knowledge base to guide prognostic and therapeutic decision-making in prostate cancer remains very limiting. Even though prostate cancer has the greatest heritable risk of all common cancers, the causes of nearly 90% of its inherited forms remain unknown. In contrast to breast and other common cancers, the broad diversity of hereditary prostate cancer's causes has impeded traditional pedigree-based investigation by linkage. The goal of this study is to further elucidate diverse causes of hereditary prostate cancer using a novel, alternative familial case-control study design. We compare independent cases, each with a strong family history of prostate cancer, to screened controls, each with a negative family history of prostate cancer. Our Aims capitalize upon dense genetic data to identify cases that share a given disease gene inherited from a common distant ancestor, even though genealogical relationships among them are not known. We identify disease genes by discerning excess sharing of ancestral genomic segments among cases, reconstructing haplotypes harboring mutations with strong risk of prostate cancer, and discerning correlated regional genetic variants and underlying candidate causal mutations. We introduce a novel algorithm to identify sentinel variants best detecting the independent risk signals, and forming a clinically predictive model. We further delineate which genetic loci are responsible for the early age of diagnosis characterizing familial disease, some also impacting aggressiveness. We employ replication across independent familial case-control study populations to discern true from false positive observations, and we evaluate additional existing case-control and biobank data sets to generalize pertinent observations. This study will improve the knowledge base for precision oncology and familial cancer care of Veterans.