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）生物标志物的发现和验证，以及特定的挑战主题03- ca -101，癌症早期检测和治疗的指纹。有效治疗癌症的最根本障碍之一是这种疾病的异质性。由于负责特定肿瘤发生和发展的分子细节的差异，即使在标准病理分析中看起来相似或相同的肿瘤，在疾病过程中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治疗化合物的开发，这个问题变得更加严重。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，预测哪些肿瘤会产生反应的可靠方法是必不可少的。新的分子诊断方法对肿瘤进行分类，从而预测对特定治疗的反应，并提供有关疾病在治疗后扩散或复发可能性的预后信息，为更有效的癌症治疗带来了巨大的希望。这些方法，如果可行，将允许医生和病人选择最有效的治疗过程，同时避免无效或不必要的治疗，降低病人的生活质量和财政负担的医疗保健系统。肿瘤的许多关键生物活性是由酪氨酸磷酸化控制的，酪氨酸激酶的失调激活是许多肿瘤发展的基础。因此，分析肿瘤的酪氨酸磷酸化的整体状态可能提供丰富的信息，可用于预测肿瘤的行为。然而，目前分析酪氨酸磷酸化的方法不适合对大量人类癌症标本进行全面分析。在这项提议中，我们将使用一种新的磷酸化蛋白质组学平台，SH2分析，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，每年在美国导致超过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，并且已经开发出针对这些激酶的新药。在这个项目中，我们将测试SH2谱分析是否可以用于非小细胞肺癌的分类，以预测和预后。如果成功，这些研究将为临床试验的发展奠定基础，这些临床试验可用于指导肺癌和其他肿瘤的更有效治疗。

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.