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）： 乳腺癌是一种难以管理的疾病，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，有效地将内在生物学特征与最有效的治疗方案相匹配，将是一个重大的进步。使用乳腺肿瘤“内在”基因集的基因表达分析已经可重复地鉴定了乳腺肿瘤的五种不同亚型：管腔A（LumA）、管腔B（LumB）、正常乳腺样（NB）、HER 2阳性（HER 2+）和基底样。每种亚型具有不同的生物学和临床行为，并且有证据表明每种亚型具有独特的药物敏感性模式。我们还表明，这种分类确定了预后组，这些预后组在不同的患者人群中是可重复的，并且不依赖于标准的临床参数。RNA表达谱是鉴定这些生物亚型的最稳健和可重复的方法，我们相信我们提出的分类可以以自动化的方式完成，而无需主观解释。为了将这些发现的临床意义推广到更大的人群，我们将开发一种检测方法，以允许从福尔马林固定的石蜡包埋（FFPE）组织中进行分类，从而可以准确地分析来自老化组织块的RNA。接下来，我们将回顾性地验证我们的“内在”基因集在数千名患者的统一治疗队列中。我们的目标是通过实时定量RT-PCR的表达分析来开发这五种亚型的临床测试。通过分析来自均质治疗患者和接受治疗随机化的患者的临床样本，我们期望获得额外的信息，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提供化疗方案疗效的预测信息。我们的长期目标是为所有早期乳腺癌患者开发一种广泛适用的亚型检测。