RNA Expression/CGH Profiles to Predict Breast Cancer Gro

简介(申请人提供)：选择有微转移的乳腺癌患者 诊断对于决定谁应该和谁不应该接受有毒和昂贵的辅助化疗以根除转移细胞至关重要。腋窝淋巴结状态是目前可用的最好的标记物，但仍有大约25%的患者被错误分类，而且对单个基因产物的分析从来没有足够强大，无法用于常规临床应用。因此，我们建议将两种新的分子图谱技术应用于一组独特的冷冻肿瘤样本，这些样本来自没有辅助治疗和非常长时间的临床随访(&gt；12年)的结节阴性患者，以便产生临床上有用的图谱，更准确地预测长期结果。我们假设从未复发的患者会有转移抑制基因的表达，反之，复发患者的肿瘤会过度表达与继发部位的扩散和肿瘤生长有关的基因。这些研究还将为生物学研究确定在转移过程中重要的基因和途径，并将为这一应用中专注于特定分子途径相互作用的其他项目提供关键数据。我们的具体目标是：1)确定能够准确预测淋巴结阴性乳腺癌复发的RNA表达谱。我们的120个肿瘤训练集将比较来自AT术后无远端复发患者的原发肿瘤标本 与那些复发的人相比，至少12年。多基因预测图谱将使用以下方法识别 有监督的统计基因选择以及更多探索性的非监督方法，并交叉验证。随后将对另外150个获得12年随访的肿瘤进行分析，作为一项验证研究。2)确定预测复发的DNA图谱，并将该图谱与RNA表达相结合。来自AIM 1的独特的未经治疗的肿瘤亚群的基因组DNA也将通过阵列比较基因组杂交(CGH)进行分析，以获得预测复发的DNA图谱。然后将预后DNA图谱与AIM 1的预后RNA图谱进行比较和整合，以确定对乳腺癌转移最具预测性的遗传成分。3)建立肿瘤转移相关基因功能的生物学模型，以确定关键的转移信号通路。乳腺癌细胞株将被设计为表达荧光素酶报告基因，并稳定地转染AIM 1中确定的单个候选基因。通过在裸鼠体内建立转基因细胞系异种移植模型，将测试选定的基因对远处转移部位的生长、侵袭和定植的影响能力。将检查信号通路的冗余，并 将确定具有转移潜力的关键候选人。

DESCRIPTION (provided by applicant): Selecting breast cancer patients with micrometastases at diagnosis is crucial for deciding who should and who should not receive toxic and expensive adjuvant chemotherapy to eradicate the metastatic cells. Axillary nodal status, the best marker available, still misclassifies about 25 percent of patients, and assay of individual gene products has never been powerful enough for routine clinical use. We therefore propose to apply two new molecular profiling techniques to a unique set of frozen tumor samples from node-negative patients with no adjuvant therapy and very long clinical follow-up (>12 years), in order to generate clinically useful profiles that more accurately predict long-term outcome. We hypothesize that patients who have never recurred will have metastasis suppressor-like gene expression, and conversely that tumors from patients who experience a recurrence will overexpress genes involved in dissemination and tumor growth at the secondary site. These studies will also identify genes and pathways important in the metastatic process for biological studies, and will provide key data for the other projects in this application focusing on interactions of particular molecular pathways. Our specific aims are: 1) To identify an RNA expression profile that accurately predicts recurrence of node-negative breast cancer. Our training set of 120 tumors will compare primary tumor specimens from patients with no distant recurrences after at least 12 years vs. those who have recurred. Multigene predictive profiles will be identified using supervised statistical gene selection as well as more exploratory unsupervised methods, and crossvalidated. A further 150 tumors with >12 year follow-up will then be analyzed as a validation study. 2) To identify a DNA profile that predicts recurrence, and integrate this profile with RNA expression. Genomic DNA from the unique untreated tumor subsets from Aim 1 will also be analyzed by array comparative genomic hybridization (CGH) to obtain a DNA profile predictive of recurrence. The prognostic DNA profiles will then be compared and integrated with the prognostic RNA profiles from Aim 1, to identify the genetic components most predictive for metastasis of breast cancer. 3) To develop biological models of metastasis-associated gene function to identify critical metastatic signaling pathways. Breast cancer cell lines will be engineered to express a luciferase reporter, and stably transfected with individual gene candidates identified in Aim 1. The selected genes will be tested for their ability to affect growth, invasion, and colonization at distant metastatic sites, using transfected cell line xenograft models in athymic nude mice. Redundancy of signaling pathways will be examined, and critical candidates with metastatic potential will be determined.