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）患者在结局方面显示出明显的临床异质性。此外，在过去十年中，改善预后或预测没有任何重大进展。我们已经完成了ESBC中拷贝数不平衡（CNI）的广泛分析，并开发了第一个适用于ER-ESBC的实用，可靠的预后模型。该项目的主要目标是验证，如有必要，请完善我们的ER-/ESBC的预后CNI模型。总体而言，该项目与TCGA相辅相成，因为患者的随访时间更长，对乳腺癌研究的需求，而样品仅来自ESBC，而TCGA中的许多样本来自研究设计，来自大型，晚期肿瘤。我们研究的总体假设是，包含有关体细胞事件或肿瘤“基因型”的信息将改善风险歧视和预测模型模型校准，用于复发，远处转移，治疗反应和全部生存期的单个ER-/ESBC患者。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/种族，发病年龄，筛查行为）而有所不同，从而提供重要的公共健康信息。三个特定的目的包括基于体细胞事件的预后/预测模型的验证和改进，以考虑人口结构。在AIM 1中，我们将在三个独立的样本集中验证当前模型作为固定模型进行预测。在AIM 2中，我们将利用可变选择的高级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准确性。在AIM 3中，我们将对模型进行比较分析，以评估信息内容的重叠，预后准确性。我们将探索模型，以预测有或不包括HER2+癌症在内的当代治疗的能力，包括紫杉烷和HER2靶向疗法。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模型反映当前的治疗方案。第二个翻译目标是评估我们的CNI预后模型在预测治疗反应时的性能。重要的是，我们提出了可变选择的新方法，以考虑体细胞事件，流行病学因素和对患者预后的治疗的关节作用，这些方法可以推广到其他标记发现工作。