Comprehensive molecular characterization of endometrial cancer, etiologic heterogeneity, and racial disparities

摘要 子宫内膜癌（EC）是美国最常见的妇科癌症。发病率正在上升，特别是 对于侵袭性、研究不足的肿瘤，预后不良，更常见于非裔美国人， （AA）。EC是所有癌症中生存差异最大的癌症之一：AA的死亡率比其他癌症高出2倍以上。 种族/民族团体。在考虑了分期、组织学、合并症和治疗后，差异仍然存在。 侵袭性肿瘤的病因学和2倍的生存差异是EC中巨大的知识缺口。癌症 基因组图谱（TCGA）在阐明子宫内膜肿瘤生物学方面取得了里程碑式的成就。使用外显子组序列 数据，TCGA定义了4种新的具有预后意义的肿瘤亚型，并显示这些数据可以细化 超越经典组织学的亚型分类。但TCGA多用于预后良好的类胶质瘤 （>90%）和白色女性的肿瘤（只有46个AA）来定义这些亚型。我们对AA与 在这些稀疏的数据中，非AA肿瘤表明AA更经常具有突变特征，提示不良的免疫反应。 成果。我们假设AA与非AA肿瘤的体细胞差异可能有助于解释大的生存率 差距在这里，我们将使用迄今为止最大，最多样化的人群-包括1，011个AA和2，043个非AA 子宫内膜癌流行病学联盟（E2 C2）的病例-研究子宫内膜癌患者的基因组变异， 子宫内膜肿瘤的全谱，不同肿瘤类型的不同风险因素特征，以及潜在的 肿瘤生物学导致了2倍的生存差异。我们将：定义突变景观和小说 使用3，054例子宫内膜肿瘤的全外显子组序列数据， AA与非AA病例。这将使用点突变、插入缺失和拷贝数的详尽基因组分析。 改变。接下来，我们将通过肿瘤分子亚型识别风险因素相关性的差异， 3，054例病例和3，054例对照。尽管有许多已知的EC风险因素，TCGA的设计并不 研究这些与躯体变化的关系。我们将把病例中的联合收割机肿瘤分析数据与以下信息结合起来： 在病例和对照中研究不同的风险因素特征 肿瘤亚型。最后，我们将3）确定肿瘤分子亚型在多大程度上解释了 2-AA和非AA病例的生存差异倍数：在描述了肿瘤基因组后，我们将使用 介导分析，以确定AA和非AA中的肿瘤分子谱在多大程度上解释了 生存差距。利用E2 C2资源和合作，我们将描述生物学和风险 EC的组分亚型的概况，包括侵袭性肿瘤，以及驱动EC的体细胞差异。 生存差距。从长远来看，这可以导致完善的风险预测工具，改善疾病的靶向 预防和治疗，以及减少长期存在的种族死亡率差异的战略。我们的研究将 也建立了一个独特的平台上进行未来的人口为基础的组学研究EC。

ABSTRACT Endometrial cancer (EC) is the most common gynecologic cancer in the US. Incidence is increasing, especially for aggressive, understudied tumors that confer poor prognosis and are more often seen in African Americans (AAs). EC has one of the largest survival disparities of all cancers: AAs have >2-fold higher mortality vs. other racial/ethnic groups. The disparity remains after accounting for stage, histology, comorbidities, and treatment. The etiology of aggressive tumors and 2-fold survival disparity are large knowledge gaps in EC. The Cancer Genome Atlas (TCGA) achieved milestones in clarifying endometrial tumor biology. Using exome sequence data, TCGA defined 4 new tumor subtypes with prognostic significance and showed these data can refine subtype classification beyond classic histology. But TCGA used mostly good prognosis endometrioid tumors (>90%) and tumors in white women—with only 46 AAs—to define these subtypes. Our pilot analysis of AA vs. non-AA tumors in these sparse data suggested AAs more often had mutational features suggestive of poor outcomes. We hypothesize somatic differences in AA vs. non-AA tumors may help explain the large survival disparity. Here, we will use the largest, most diverse population to date—including 1,011 AA and 2,043 non-AA cases in the Epidemiology of Endometrial Cancer Consortium (E2C2)—to study genomic variation across the full spectrum of endometrial tumors, distinct risk factor profiles across tumor types, and the role of underlying tumor biology in driving the 2-fold survival disparity. We will: define the mutational landscape and novel tumor subtypes using whole-exome sequence data in 3,054 endometrial tumors and compare these in AA vs. non-AA cases. This will use exhaustive genomic profiling of point mutations, indels, and copy number alterations. Next, we will identify differences in risk factor associations by tumor molecular subtypes in 3,054 cases and 3,054 matched controls. Despite many known EC risk factors, TCGA was not designed to study these in concert with somatic changes. We will combine tumor profiling data in cases with information on known germline genetic and epidemiologic risk factors in cases and controls to study distinct risk factor profiles by tumor subtypes. Finally, we will 3) determine the extent to which tumor molecular subtypes explain the 2-fold survival disparity in AA and non-AA cases: Having characterized tumor genomes, we will use mediation analysis to determine the extent to which tumor molecular profiles in AAs and non-AAs explain the survival disparity. Leveraging E2C2 resources and collaborations, we will characterize the biology and risk profiles of the component subtypes of EC, including aggressive tumors, and somatic differences that drive the survival disparity. Long-term this can lead to refined risk prediction tools, improved targeting of disease prevention and treatment, and strategies to reduce longstanding racial disparities in mortality. Our study will also build a unique platform on which to perform future population-based -omics studies of EC.