Risk Prediction for ER Negative Breast Cancer Recurrence

描述(申请人提供)：雌激素受体阴性(ER-)，早期乳腺癌(ESBC)患者表现出显著的临床异质性的结果。此外，在过去十年中，在改进预测或预测方面没有取得重大进展。我们已经完成了对ER-ESBC中拷贝数失衡(CNI)的广泛分析，并开发了第一个适用于ER-ESBC的实用、稳健的预后模型。这个项目的主要目标是验证并在必要时完善我们对ER-/ESBC的预测CNI模型。总体而言，该项目是对TCGA的补充，因为患者的随访时间要长得多，这是乳腺癌研究的要求，而且样本仅来自ESBC，而TCGA中的许多样本来自大型晚期肿瘤，这是由于研究设计的原因。我们研究的主要假设是，包括躯体事件或肿瘤‘基因’的信息将改善个体ER-/ESBC患者复发、远处转移、治疗反应和总存活率的风险辨别和预测模型校准。其次，我们假设ER-/ESBC的躯体事件模式将因提供重要公共卫生信息的流行病学因素(种族/民族、发病年龄、筛查行为)而不同。三个具体的目标包括验证和改进基于躯体事件的ER-/ESBC预测/预测模型，考虑到种群结构。在目标1中，我们将在三个独立的样本集中验证我们当前的模型作为固定模型进行预测。在目标2中，我们将利用先进的变量选择方法来评估是否可以通过考虑躯体事件和临床因素之间的交互作用来提高模型的准确性。在目标3中，我们将对这些模型进行比较分析，以评估信息内容的重叠、预测的准确性。我们将探索预测对包括紫杉烷和HER2靶向治疗在内的HER2+癌症的当代治疗反应的能力的模型。该项目的主要翻译目标是验证和改进我们的 反映当前治疗方案的ER-/ESBC预后CNI模型。第二个翻译目标是评估我们的CNI预后模型(S)在预测治疗反应方面的表现。重要的是，我们提出了新的变量选择方法，允许考虑躯体事件、流行病学因素和治疗对患者结果的联合影响，可以推广到其他标记物的发现工作。

DESCRIPTION (provided by applicant): Estrogen receptor-negative (ER-), early stage breast cancer (ESBC) patients show marked clinical heterogeneity with regard to outcomes. Further, there have been no major advances in improving prognostication or prediction over the last decade. We have completed an extensive analysis of copy number imbalances (CNI) in ER- ESBC and have developed the first practical, robust prognostic model applicable to ER-ESBC. The primary goal of this project is to validate, and if necessary, refine our prognostic CNI model for ER-/ESBC. Overall the project is complementary to the TCGA in that the follow-up for patients is much longer, a requirement for breast cancer studies, and the samples are solely from ESBC whereas many of the samples in the TCGA are from large, advanced tumors due to the study design. The overarching hypothesis of our study is that inclusion of information on somatic events or tumor 'genotype' will improve risk discrimination and prediction model calibration for individual ER-/ESBC patients for recurrence, distant metastasis, treatment response, and overall survival. Secondarily, we hypothesize that the pattern of somatic events in ER-/ESBC will differ by epidemiological factors (race/ethnicity, age of onset, screening behaviors) providing important public health information. Three specific aims encompass the validation and refinement of prognostic/predictive models based on somatic events for ER-/ESBC considering population structure. In aim 1, we will validate our current model as a fixed model in three independent sample sets for prognostication. In aim 2, we will take advantage of advanced methods for variable selection to evaluate whether or not we can improve model accuracy by considering interactions between somatic events and clinical factors. In aim 3, we will conduct comparative analyses of the models to assess overlap in information content, prognostic accuracy. We will explore the models for the ability to predict response to contemporary treatment with and without inclusion of HER2+ cancers including taxanes and HER2-targeted therapy. The primary translational goal of this project is to validate and refine our prognostic CNI model for ER-/ESBC to reflect current therapeutic protocols. A second translational goal is to assess the performance of our CNI prognostic model(s) in predicting treatment response. Importantly, we propose novel methods for variable selection that allow consideration of the joint effects of somatic events, epidemiologic factors, and treatment on patient outcomes that can be generalized to other marker discovery efforts.