NOVEL BIOMARKERS FOR AROMATASE INHIBITOR THERAPY

描述(申请人提供)：雌激素受体阳性(ER)乳腺癌表现出高度不同的内分泌治疗反应。我们无法准确预测疾病的临床进程，导致与高发病率和医疗保健成本相关的低效临床算法。对ER乳腺癌基因组的分析揭示了广泛的体细胞突变和基因拷贝数变化，这些突变和基因拷贝数变化导致了基因表达和功能的异常，这是临床观察到的不同表型的基础。为了加快分子分类的创建，这些分类可以转化为临床上有用的预后和预测性测试，我们已经进行了一系列新辅助内分泌治疗试验。十多年前设计的最初研究的数据表明，对内分泌治疗最初几周至几个月内细胞反应的分析可以用来确定接受辅助内分泌治疗的ER乳腺癌的长期预后。特别是，对“正在治疗”的标本的增殖(Ki67)和ER状态的测量提供了独立于病理分期和分级的预后信息，并构成了术前内分泌预后指数(PEPI)的基础。在R01的初始阶段，我们通过对来曲唑用于临床2期和3期ER乳腺癌的手术前试验(POL研究)的肿瘤样本的分析，扩展了这项开创性工作，并确定了预测芳香酶抑制剂(AI)治疗反应的治疗前和治疗中的基因表达特征。为了验证在POL试验中发现的签名，并深化我们的生物标记物发现工作，我们提出了一项基于ACOSOG Z1031研究的新研究计划，该研究是在2009年年中之前完成375名患者的收益的新佐剂环境中，对三种已获批准的人工智能进行随机比较。在目标1中，我们建议验证两个“正在治疗”的预后标志，基于ER和Ki67的简单PEPI方法和50基因qRT-PCR内在亚型分析(PAM50)，这已经被证明在POL研究中应用于letrozole暴露的肿瘤时也可以预测不良反应和复发。为了解决从实用的角度来看基线表达签名仍然是可取的批评，在目标2中，我们将使用mRNA基因表达谱来进一步发现和验证预测AI治疗反应性差和复发风险增加的预处理生物标记物。在目标3和4中，我们将通过分析与AI耐药的生物标记物相关的基因复制异常和突变来确定内分泌治疗耐药的基本分子基础。在拨款的最后一年，当无复发生存数据可用时，我们将重新分析整个数据集，并创建一个最终模型，该模型可能使用所有三种数据类型(表达、基因复制和突变)来最佳预测结果。这项应用将提供一套福尔马林固定的组织耐受生物标记物方法，可以在随机辅助治疗试验的背景下进一步验证。我们的长期目标是生成一份为临床结果注释的管腔乳腺癌图谱，以便ER乳腺癌的治疗可以充满信心地个性化定制，并加快新的靶向治疗的出现。公共卫生相关性：雌激素受体阳性(ER)乳腺癌的主要治疗方法是内分泌治疗，但肿瘤的反应差异很大。我们无法可靠地预测肿瘤对癌症的敏感性，这往往会导致额外的治疗，特别是化疗，以尝试并确保最佳结果，尽管这些昂贵且耐受性差的药物对许多有反应性疾病的患者来说并不是必要的。这一应用将提供一套临床测试，不仅将预测内分泌治疗的反应，还将确定内分泌治疗耐药肿瘤的全部分子缺陷，以便设计新的、更有效的靶向治疗。

DESCRIPTION (provided by applicant): Estrogen receptor positive (ER+) breast cancers exhibit highly variable responsiveness to endocrine therapy. Our inability to predict accurately the clinical course of the disease leads to inefficient clinical algorithms associated with high morbidity and health care costs. An analysis of the ER+ breast cancer genome reveals a wide range of somatic mutations and gene copy number changes that drive abnormalities in gene expression and function that underlie the diverse phenotypes observed clinically. To speed the creation of molecular classifications that can be translated into clinically useful prognostic and predictive tests we have executed a series of neoadjuvant endocrine therapy trials. The data from the initial studies designed over a decade ago demonstrated that an analysis of the cellular response to endocrine therapy within the first few weeks to months of treatment can be used to determine the long term prognosis of ER+ breast cancer under treatment with adjuvant endocrine therapy. In particular, measurements of proliferation (Ki67) and ER status of "on treatment" samples provides prognostic information that is independent of pathological stage and grade and forms the basis for a preoperative endocrine prognostic index (PEPI). In the initial phase of this R01, we extended this pioneering work through an analysis of tumor samples from a Phase 2 trial of preoperative letrozole for clinical stage 2 and 3 ER+ breast cancer (POL study) and identified both pre-treatment and on- treatment gene expression signatures that predict response to aromatase inhibitor (AI) treatment. To validate the signatures discovered in the POL trial and to deepen our biomarker discovery efforts we present a new research plan based on the ACOSOG Z1031 study, a randomized comparison between the three approved AIs in the neoadjuvant setting that is due to complete accrual of 375 patients by mid-2009. In Aim 1 we propose to validate two "on-treatment" prognostic signatures, the simple PEPI approach based on ER and Ki67 and a 50 gene qRT-PCR intrinsic subtype assay (PAM50) that has already been shown to also predict poor response and relapse when applied to letrozole exposed tumors in the POL study. To address the criticism that a baseline expression signature would still be preferable from a practical standpoint, in Aim 2 we will use mRNA gene expression profiling to further discover and validate pretreatment biomarkers that predict poor responsiveness to AI therapy and elevated relapse risk. In Aims 3 and 4 we will determine the fundamental molecular basis for endocrine therapy resistance by analyzing gene copy aberrations and mutations associated with biological markers of AI resistance. In the last year of the grant we will reanalyze the entire data set when relapse-free survival data becomes available and create a final model that potentially uses all three data types (expression, gene copy and mutation) to optimally predict outcomes. This application will deliver a suite of formalin-fixed tissue-tolerant biomarker approaches that can be further validated in the context of randomized adjuvant therapy trials. Our long term goal is to generate a luminal breast cancer atlas that is annotated for clinical outcomes so that the treatment of ER+ breast cancer can be individually tailored with confidence and the emergence of new targeted treatments can be accelerated. PUBLIC HEALTH RELEVANCE: The principle treatment for estrogen receptor positive (ER+) breast cancer is endocrine therapy but tumor response is highly variable. Our inability to reliably predict tumor sensitivity to often leads to additional treatments, particularly chemotherapy to try and ensure the best outcome, even though these costly and poorly tolerated drugs are not necessary for many patients with responsive disease. This application will deliver a suite of clinical tests that will not only predict response to endocrine therapy but will define the full repertoire of molecular defects in endocrine therapy resistant tumors so new and more effective targeted therapies can be designed.