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疾病的异质性。由于负责特定肿瘤的发展和进展的分子细节的差异，即使对于通过标准病理学分析看起来相似或相同的肿瘤，疾病过程也可能存在深刻的差异。随着靶向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些疗法，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的可能性的预后信息，为更有效的癌症治疗带来了巨大的希望。如果可用，这些方法将允许医生和患者选择最有效的治疗过程，同时避免降低患者生活质量和给医疗保健系统带来经济负担的无效或不必要的治疗。肿瘤的许多关键生物学活性由酪氨酸磷酸化控制，并且众所周知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可用于预测肿瘤行为的丰富信息。然而，目前分析酪氨酸磷酸化的方法不适合于大量人类癌症标本的全面分析。在这项提案中，我们将使用一种新的磷酸化蛋白质组学平台，SH 2分析，从人类患者的非小细胞肺癌（NSCLC）样本。NSCLC是一种毁灭性的疾病，在美国每年导致超过160，000人死亡。某些酪氨酸激酶在NSCLC中经常被激活，并且已经开发了靶向这些激酶的新药。在这个项目中，我们将测试SH 2谱是否可以用于分类NSCLC的预测和预后。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。 公共卫生相关性：肿瘤在病程和对特定治疗的反应方面差异很大。可用于对肿瘤进行分类并预测其行为的分子诊断方法具有巨大的潜力，可以提高治疗的有效性，并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整体状态，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.