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。发病率在增加，尤其是 对于侵袭性的、研究不足的肿瘤，预后较差，更常见于非裔美国人 (美国科学促进会)。EC是所有癌症中存活率差距最大的癌症之一：AAS的死亡率是其他癌症的2倍 种族/民族群体。在考虑了分期、组织学、合并症和治疗后，这种差异仍然存在。 侵袭性肿瘤的病因和2倍的生存差距是EC的巨大知识鸿沟。巨蟹座 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。使用外显子序列 数据，TCGA定义了4个新的具有预后意义的肿瘤亚型，并表明这些数据可以提炼 超越经典组织学的亚型分类。但TCGA多用于预后较好的子宫内膜样瘤 (&gt；90%)和白人女性中的肿瘤--只有46个AA--来定义这些亚型。我们对AA与 这些稀疏数据中的非AA肿瘤提示AA更多地具有突变特征，提示不良 结果。我们假设再生障碍性贫血和非再生障碍性贫血肿瘤的体细胞差异可能有助于解释巨大的存活率 贫富差距。在这里，我们将使用迄今为止最大、最多样化的种群--包括1,011个再生障碍性贫血和2,043个非再生障碍性贫血 子宫内膜癌联盟(E2C2)的流行病学病例--研究不同地区的基因组变异 子宫内膜肿瘤的全谱，不同肿瘤类型的不同危险因素概况，以及潜在的 肿瘤生物学导致的生存差距扩大了2倍。我们将：定义变异的景观和小说 应用全外显子组序列数据对3,054例子宫内膜肿瘤进行亚型比较 再障与非再障病例比较。这将使用点突变、INDELs和拷贝数的详尽基因组图谱 改装。接下来，我们将根据肿瘤分子亚型确定风险因素相关性的差异。 3054个病例和3054个匹配的对照组。尽管有许多已知的EC风险因素，TCGA的设计并不是为了 结合身体的变化来研究这些。我们将结合病例中的肿瘤概况数据和关于 病例和对照中已知的种系遗传和流行病学危险因素，以研究不同的危险因素特征 按肿瘤亚型分类。最后，我们将确定肿瘤分子亚型在多大程度上解释了 再生障碍性贫血和非再生障碍性贫血患者的2倍生存差异：在表征了肿瘤基因组后，我们将使用 中介分析以确定腹主动脉和非腹主动脉中肿瘤分子特征解释的程度 生存差距。利用E2C2资源和协作，我们将确定生物学特性和风险 EC的组成亚型的概况，包括侵袭性肿瘤，以及推动 生存差距。从长远来看，这可以导致改进风险预测工具，改进疾病目标 预防和治疗，以及减少长期存在的死亡率种族差距的战略。我们的研究将 还可以建立一个独特的平台，在这个平台上进行欧共体未来的基于人口的组学研究。

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.