Developing Biomarker-Based Prognostics In Breast Cancer

描述（由申请人提供）：候选人的目标是建立在他的背景，在临床外科肿瘤学和人类分子遗传学，成为一个领导者的翻译新的基因组技术，以照顾乳腺癌患者。通过当前提案概述的步骤，候选人将获得1）基因组科学，2）生物信息学和统计学以及3）临床试验设计方面的必要额外专业知识，以改善基于生物标志物的预后模型并在前瞻性临床试验中验证它们。候选人在基因组学和生物信息学领域的高级研究人员中处于有利地位，他们最近在该领域做出了重大贡献。他在乳腺肿瘤学方面的临床专业知识，他对生物标志物研究组织采集的独特观点，以及他通过美国外科医生学会肿瘤组获得相关科学发展和临床试验的独特途径，为他提供了弥合新知识开发与临床试验验证之间差距的独特机会。候选人和他的导师已经构建了一个科学发展和研究计划，该计划围绕测试假设，即确定基因表达的生物学变异性以及将蛋白质生物标志物表达纳入基于基因表达的预测模型将提高这些模型预测乳腺癌结果的能力，包括腋窝淋巴结转移和疾病复发。具体而言，目标是： 具体目标1：测量绝经前和绝经后女性中TIN0M0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改变乳腺癌腋窝转移和复发的基于表达的模型预测。 具体目标二：确定腋窝淋巴结状态的增强病理分期是否提高了基于基因表达的预测模型的准确性。 具体目标3：为了确定将蛋白质生物标志物数据并入基于基因表达的预测树模型中是否提高了模型的预测准确性， 具体目标4：在多机构试点研究中前瞻性测试基于基因表达和蛋白质生物标志物的预测树模型。

DESCRIPTION (provided by applicant): The candidate's objective is to build upon his background in clinical surgical oncology and human molecular genetics to become a leader in the translation of new genome technologies to the care of patients with breast cancer. Through the steps outlined current proposal the candidate will obtain necessary additional expertise in 1) genome science, 2) bioinformatics and statistics, and 3) clinical trial design to improve biomarker based prognostic models and validate them in prospective clinical trials. The candidate is well positioned in a committed group of senior investigators in genomics and bioinformatics who have made significant recent contributions in this field. His clinical expertise in breast oncology, his unique perspective on tissue acquisition for biomarker studies, and his unique access to correlative science development and clinical trials through the American College of Surgeons Oncology Group affords him the unique opportunity to bridge the gap between development of new knowledge and its validation in clinical trials. The candidate and his mentors have constructed a program of scientific development and investigation centered around testing the hypothesis that ascertainment of biologic variability in gene expression as well as incorporating protein biomarker expression to gene expression-based predictive models will improve the ability of these models to predict breast cancer outcomes, including metastasis to axillary lymph nodes and disease recurrence. Specifically, the aims are: Specific Aim 1: To measure variability in gene expression in TIN0M0 hormone receptor positive breast cancer in pre- and postmenopausal women and ascertain whether this variability changes expression-based model predictions of axillary metastasis and recurrence from breast cancer. Specific Aim 2: To determine whether enhanced pathologic staging of axillary lymph node status improves the accuracy of gene expression-based predictive models. Specific Aim 3: To determine whether incorporating protein biomarker data into a gene expression-based predictive tree model improves the predictive accuracy of the model Specific Aim 4: To prospectively test a gene expression and protein biomarker-based predictive tree model in a multi-institutional pilot study.