Biological Breast Cancer Classification by qRT-PCR

通过 qRT-PCR 进行乳腺癌生物学分类

• 批准号: 7127620
• 负责人: MATTHEW J ELLIS
• 金额: $ 154.92万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-29 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7127620
• 关键词: Biological; Breast; Cancer; Classification; qRT

DESCRIPTION (provided by applicant): Breast cancer is a difficult disease to manage because it is comprised of a wide spectrum of tumor subtypes with different biological characteristics, therapeutic responses and clinical outcomes. A biomarker-based classification of breast cancer that effectively matches intrinsic biological characteristics with the most effective therapeutic protocol would be a significant advance. Gene expression analysis using a breast tumor "intrinsic" gene set has reproducibly identified five distinct subtypes of breast tumors: Luminal A (LumA), Luminal B (LumB), Normal Breast-like (NB), HER2-positive (HER2+) and Basal-like. Each subtype has a distinct biology and clinical behavior and evidence suggests that each has a unique drug sensitivity pattern. We have also shown that this classification identifies prognostic groups that are reproducible across different patient populations and are independent of standard clinical parameters. RNA expression profiling is the most robust and reproducible way to identify these biological subtypes and we believe that our proposed classification can be accomplished in an automated fashion without subjective interpretation. In order to generalize the clinical significance of these findings to larger populations we will develop an assay to allow classification from formalin-fixed, paraffin-embedded (FFPE) tissues so that RNA from aged blocks can be accurately profiled. Next, we will retrospectively validate our "intrinsic" gene set on uniformly treated cohorts of thousands of patients. Our goal is to develop a clinical test for these five subtypes using expression analysis by real-time quantitative RT-PCR. With the additional information that we expect to gain by profiling clinical samples from homogeneously treated patients and patients subjected to treatment randomization, we will be able to provide valuable prognostic information for patients with node negative breast cancer and predictive information for the efficacy of chemotherapy regimens for patients with node positive disease. It is our long term goal to develop a broadly applicable subtyping test for all early stage breast cancer patients.

描述（由申请人提供）： 乳腺癌是一种很难控制的疾病，因为它由具有不同生物学特征，治疗反应和临床结果的广泛肿瘤亚型组成。基于生物标志物的乳腺癌分类将有效地匹配内在的生物学特征与最有效的治疗方案将是一个重大进展。使用乳腺肿瘤“固有”基因组的基因表达分析可重复鉴定五种不同的乳腺肿瘤亚型：腔A（Luma），腔内B（腰），正常乳腺（NB），HER2阳性（HER2+）和基底样。每个亚型都有独特的生物学和临床行为，证据表明每种都有独特的药物敏感性模式。我们还表明，此分类确定了在不同患者人群中可再现的预后组，并且与标准临床参数无关。 RNA表达谱分析是识别这些生物亚型的最强大和可重复的方法，我们认为我们提出的分类可以自动化而无需主观解释。为了将这些发现的临床意义推广到较大的人群，我们将开发一种测定法，以允许对福尔马林固定的，石蜡包含的（FFPE）组织进行分类，以便可以准确介绍来自老年块的RNA。接下来，我们将回顾性地验证我们的“固有”基因设置为均匀治疗的数千名患者。我们的目标是使用实时定量RT-PCR使用表达分析为这五个亚型开发临床测试。通过分析同质治疗的患者和接受治疗随机治疗的患者的临床样本，我们预期会获得的其他信息，我们将能够为淋巴结阴性乳腺癌患者提供有价值的预后信息，并为淋巴结阳性疾病患者的化学疗法疗效提供预测性信息。我们的长期目标是为所有早期乳腺癌患者开发广泛适用的亚型测试。