Risk Prediction for ER Negative Breast Cancer Recurrence

ER 阴性乳腺癌复发的风险预测

基本信息

批准号：
9198211
负责人：
MELISSA L. BONDY
金额：
$ 59.31万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-01-01 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9198211
关键词：
Address Age Age of Onset Anthracyclines Behavior Biopsy Breast Cancer Patient Calibration Clinical Collection Combination Drug Therapy DNA Data Decision Making Detection Diagnosis Discrimination Disease Distant Metastasis ERBB2 gene Epidemiologic Factors Epidemiological Factors Estrogen receptor negative Ethnic Origin Event Exhibits Follow-Up Studies Formalin Freezing Funding Genomics Genotype Goals Heterogeneity Individual Joints Letters Malignant Neoplasms Marker Discovery Measures Methods Modeling Molecular Mutation Mutation Analysis Neoadjuvant Therapy Outcome Paraffin Embedding Pathologic Patient-Focused Outcomes Patients Pattern Performance Population Predictive Value Prognostic Factor Protocols documentation Public Health Race Recurrence Reproducibility Research Design Resolution Risk Sampling Series Somatic Mutation Structure Technology Testing The Cancer Genome Atlas Therapeutic Tissue Embedding Tissues Training Trastuzumab Validation base cancer recurrence clinical practice comparative experience follow-up improved lapatinib malignant breast neoplasm molecular marker novel predicting response predictive modeling predictive of treatment response prognostic prototype public health relevance relapse risk routine practice screening success targeted treatment taxane treatment response tumor

项目摘要

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.

描述（由申请人提供）：雌激素受体阴性（ER-）、早期乳腺癌（ESBC）患者在结果方面表现出明显的临床异质性。此外，过去十年在改善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 拷贝数不平衡 (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。该项目的主要目标是验证并在必要时完善我们的 ER-/ESBC 预后 CNI 模型。总体而言，该项目与 TCGA 是互补的，因为患者的随访时间更长，这是乳腺癌研究的要求，并且样本仅来自 ESBC，而 TCGA 中的许多样本来自大型晚期肿瘤，因为研究设计。我们研究的总体假设是，纳入体细胞事件或肿瘤“基因型”信息将改善个体 ER-/ESBC 患者复发、远处转移、治疗反应和总体生存的风险辨别和预测模型校准。其次，我们假设 ER-/ESBC 中的躯体事件模式将因流行病学因素（种族/族裔、发病年龄、筛查行为）而异，从而提供重要的公共卫生信息。三个具体目标包括考虑群体结构，基于 ER-/ESBC 体细胞事件验证和完善预后/预测模型。在目标 1 中，我们将在三个独立样本集中验证当前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我们是否可以通过考虑体细胞事件和临床因素之间的相互作用来提高模型的准确性。在目标 3 中，我们将对模型进行比较分析，以评估信息内容的重叠和预后准确性。我们将探索能够预测对包含或不包含 HER2+ 癌症（包括紫杉烷类药物和 HER2 靶向治疗）的当代治疗的反应的模型。该项目的主要翻译目标是验证和完善我们的 ER-/ESBC 的预后 CNI 模型反映了当前的治疗方案。第二个转化目标是评估我们的 CNI 预后模型在预测治疗反应方面的表现。重要的是，我们提出了变量选择的新方法，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，这些影响可以推广到其他标志物发现工作。