Risk Prediction for ER Negative Breast Cancer Recurrence

ER 阴性乳腺癌复发的风险预测

• 批准号: 8420149
• 负责人: MELISSA L. BONDY
• 金额: $ 66.58万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8420149
• 关键词: Address; Age; Age of Onset; Allelotyping; Anthracyclines; Behavior; Biopsy; Calibration; Cancer Patient; Clinical; Clinical Treatment; 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; 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; Patients; Pattern; Performance; Population; Predictive Value; Prognostic Factor; Protocols documentation; Public Health; Race; Recurrence; Relapse; Reproducibility; Research Design; Resolution; Risk; Roche brand of trastuzumab; Sampling; Sampling Studies; Series; Somatic Mutation; Staging; Structure; Taxane Compound; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Tissues; Training; Validation; base; cancer recurrence; clinical practice; comparative; experience; follow-up; improved; lapatinib; malignant breast neoplasm; molecular marker; novel; predictive modeling; prognostic; prototype; public health relevance; response; routine practice; sample collection; screening; success; taxane; treatment response; tumor

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）患者在结局方面表现出明显的临床异质性。此外，在过去的十年里，在改进解释或预测方面没有取得重大进展。我们已经完成了对ER-ESBC中拷贝数不平衡（CNI）的广泛分析，并开发了适用于ER-ESBC的第一个实用、稳健的预后模型。该项目的主要目标是验证，如果必要的话，完善我们的ER-/ESBC的预后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，并且样本仅来自ESBC，而TCGA中的许多样本来自大型晚期肿瘤。我们研究的首要假设是，纳入关于体细胞事件或肿瘤“基因型”的信息将改善个体ER-/ESBC患者复发、远处转移、治疗反应和总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（人种/种族、发病年龄、筛查行为）而异，从而提供重要的公共卫生信息。三个具体的目标包括验证和完善的预后/预测模型的基础上，考虑人口结构的ER-/ESBC的体细胞事件。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。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躯体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，预测准确性。我们将探索模型预测对包括和不包括HER 2+癌症（包括紫杉烷类和HER 2靶向治疗）的当代治疗的反应的能力。这个项目的主要目标是验证和完善我们的 ER-/ESBC的预后CNI模型，以反映当前的治疗方案。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性能。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局的联合影响，这些影响可以推广到其他标志物发现工作。