A novel molecular diagnostic approach to classify lung cancers and predict respon

描述(由申请人提供)：本申请涉及广泛的挑战领域(03)生物标记物的发现和验证，以及特定的挑战主题03-CA-101，癌症早期发现和治疗的指纹。癌症有效治疗的最基本障碍之一是疾病的异质性。由于导致特定肿瘤发生和发展的分子细节的不同，即使是通过标准病理分析看起来相似或相同的肿瘤，其病程也可能存在深刻的差异。随着针对特定信号通路或分子的新的治疗化合物的开发，这个问题变得更加尖锐。这种靶向治疗可能只对一小部分肿瘤非常有效。因此，如果要有效地使用这种疗法，预测哪些肿瘤会有反应的可靠方法是必不可少的。新的分子诊断方法可以对肿瘤进行分类，从而预测对特定治疗的反应，并提供关于治疗后疾病扩散或复发可能性的预后信息，这为更有效的癌症治疗带来了巨大的希望。这种方法如果可行，将允许医生和患者选择最有效的疗程，同时避免无效或不必要的治疗，这些治疗会降低患者的生活质量，并给医疗系统带来经济负担。肿瘤的许多关键生物学活动是由酪氨酸磷酸化控制的，众所周知，酪氨酸激酶的异常激活是许多肿瘤发生的基础。因此，描绘肿瘤酪氨酸磷酸化的整体状态可能会提供丰富的信息，可以用来预测肿瘤的行为。然而，目前分析酪氨酸磷酸化的方法不适用于对大量人类癌症样本的全面分析。在这项提案中，我们将使用一种新的磷酸蛋白质组学平台SH2图谱，来分析来自人类患者的非小细胞肺癌(NSCLC)样本。非小细胞肺癌是一种毁灭性的疾病，在美国每年导致超过16万人死亡。某些酪氨酸激酶在非小细胞肺癌中经常被激活，针对这些酶的新药已经开发出来。在这个项目中，我们将测试SH2图谱是否可以用来对NSCLC进行分类，以便预测和预后。如果成功，这些研究将为临床测试的开发奠定基础，这些测试可以用来指导肺癌和其他肿瘤的更有效治疗。 公共卫生相关性：肿瘤在病程和对特定治疗的反应方面有很大的不同。可用于对肿瘤进行分类和预测其行为的分子诊断方法具有巨大的潜力，既可以提高治疗效果，又可以减少与不必要或无效治疗相关的痛苦和成本。在这项提案中，我们将使用一种新的方法来描述肺癌中酪氨酸磷酸化的全球状态，并评估它是否提供了可用于指导治疗的有用信息。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and specific Challenge Topic 03-CA-101, Fingerprints for the Early Detection and Treatment of Cancer. One of the most fundamental hurdles for the effective treatment of cancer is the heterogeneity of the disease. Because of differences in the molecular details responsible for the development and progression of a particular tumor, there can be profound differences in the course of disease even for tumors that appear similar or identical by standard pathological analysis. This problem is becoming more acute as new therapeutic compounds are developed that target specific signaling pathways or molecules. Such targeted therapies may be highly effective for only a small percentage of tumors. Reliable methods to predict which tumors will respond are thus essential if such therapies are to be used effectively. New molecular diagnostic methods to classify tumors, and thus predict response to specific therapies and provide prognostic information on the likelihood that the disease will spread or recur after therapy, hold great promise for more effective cancer therapy. Such methods, if available, will allow the physician and patient to choose the most effective course of treatment, while avoiding ineffective or unnecessary treatments that diminish quality of life for patients and financially burden the healthcare system. Many key biological activities of tumors are controlled by tyrosine phosphorylation, and the dysregulated activation of tyrosine kinases is well known to underlie the development of many tumors. Thus profiling the global state of tyrosine phosphorylation of a tumor is likely to provide a wealth of information that can be use to predict the behavior of the tumor. However current methods to analyze tyrosine phosphorylation are not amenable to the comprehensive analysis of large numbers of human cancer specimens. In this proposal, we will use a novel phosphoproteomics platform, SH2 profiling, to profile non-small cell lung carcinoma (NSCLC) samples from human patients. NSCLC is a devastating disease that kills over 160,000 people per year in the U.S. Certain tyrosine kinases are frequently activated in NSCLC, and new drugs have been developed that target these kinases. In this project we will test whether SH2 profiling can be used to classify NSCLC for prediction and prognosis. If successful, these studies will set the stage for development of clinical tests that can be used to guide more effective treatment for lung cancer and other tumors. PUBLIC HEALTH RELEVANCE: Tumors vary greatly in the course of disease and in how they respond to specific therapies. Molecular diagnostic methods that can be used to classify tumors and predict their behavior have enormous potential to both increase the effectiveness of treatment, and decrease the suffering and cost associated with unnecessary or ineffective treatments. In this proposal, we will us a novel method to profile the global state of tyrosine phosphorylation in lung cancers, and assess whether it provides useful information that could be used to guide treatment.