Improving genomic prediction models in breast cancer

改进乳腺癌基因组预测模型

• 批准号: 6945375
• 负责人: JOHN A. OLSON
• 金额: $ 13.86万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-02 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6945375
• 关键词: biomarker; biopsy; breast neoplasm /cancer diagnosis; clinical research; diagnosis design /evaluation; estrogen receptors; female; gene expression; human subject; ischemia; lymph nodes; menopause; neoplasm /cancer relapse /recurrence; outcomes research; patient oriented research; postmenopause; women' s health

DESCRIPTION (provided by applicant): Breast cancer is a heterogeneous disease with variable biology and often-unpredictable clinical outcomes. Genomic technologies have shown promise to better discriminate between patients of similar clinicopathologic stage and in some cases predict clinical outcomes. We and others have published studies of array-based predictive modeling of breast cancer outcomes, including ER status, lymph node status, and disease recurrence. While these initial studies raise hope for genomic-based personalized outcome predictions, significant questions must be answered before expression array-based prognostics can be incorporated into clinical medicine. For example, does normal variation in breast tumor gene expression affect expression-based outcome predictions? Our recent data suggest that replicate samples of similar tumor taken from the same breast tumor at two different times show consistent patterns of global gene expression. On the other hand, replicate tumor specimens of different cellular heterogeneity or those subjected to different periods of ischemia have significant differences in global gene expression as well. Importantly, this variation, which can reasonably be expected to occur under routine clinical conditions, gives different model predictions of outcome in some cases. We now believe that understanding variation in gene expression and incorporating knowledge about this variation in genomic-based models may provide useful information to improve the accuracy of these models. In this proposal, we will test this hypothesis that ascertainment of biologic and process variability in breast cancer gene expression will improve the ability of gene expression-based models to predict breast cancer outcomes, including ER status, metastasis to axillary lymph nodes and disease recurrence. Specifically, we aim to: Specific Aim 1: To determine whether gene expression data from core biopsy specimens of different cellular content give similar predictions in a gene expression-based predictive model of ER status, axillary lymph node metastasis and recurrence in breast cancer. Specific Aim 2: To identify changes in breast tumor gene expression with tissue ischemia and determine the impact of these changes on gene expression-based predictive models of ER status, axillary lymph node status and recurrence in breast cancer. Specific Aim 3: To measure variability in gene expression in T1N0M0 hormone receptor positive breast cancer in pre- and postmenopausal women and ascertain whether this variability changes expression-based model predictions of ER status, axillary lymph node status and recurrence in breast cancer.

描述（由申请人提供）：乳腺癌是一种异质疾病，具有可变的生物学和通常无法预测的临床结局。基因组技术已显示出有望更好地区分相似临床病理阶段的患者，在某些情况下可以预测临床结局。我们和其他人已经发布了基于阵列的乳腺癌预后预测建模的研究，包括ER状态，淋巴结状态和疾病复发。尽管这些初步研究给基于基因组的个性化结果预测带来了希望，但必须在将基于表达阵列的预后纳入临床医学之前回答重大问题。例如，乳腺肿瘤基因表达的正常变异是否会影响基于表达的结果预测？我们最近的数据表明，在两个不同时间从同一乳腺肿瘤采集的相似肿瘤的样本显示出一致的全球基因表达模式。另一方面，复制不同细胞异质性的肿瘤标本或遭受不同缺血周期的肿瘤标本在全球基因表达上也具有显着差异。重要的是，在常规临床条件下可能会合理地发生这种变异，在某些情况下给出了结果的不同模型预测。我们现在认为，了解基因表达的变化并纳入了基于基因组模型中这种变异的知识可能会提供有用的信息来提高这些模型的准确性。在此提案中，我们将检验这一假设，即乳腺癌基因表达中生物学和过程变异性的确定将提高基因表达模型预测乳腺癌结果的能力，包括ER状态，转移到腋窝淋巴结和疾病复发的能力。具体来说，我们的目标是： 具体目的1：确定来自不同细胞含量的核心活检标本中的基因表达数据是否在基于基因表达的ER状态，腋窝淋巴结转移和乳腺癌复发的基于基因表达的预测模型中提供了相似的预测。 特定目的2：确定乳腺肿瘤基因表达的变化与组织缺血，并确定这些变化对基于基因表达的ER状态，腋窝淋巴结状态和乳腺癌中复发的预测模型的影响。 特定目的3：测量绝经前和绝经后妇女中T1N0M0激素受体阳性乳腺癌基因表达的变异性，并确定这种变异性是否会改变基于表达的ER状态，腋窝淋巴结状态和乳腺癌中的复发。