A High Throughput Diagnostic Assay for Lung Cancer

描述(由申请人提供)： 在美国，每年新诊断的肺癌病例为17万例。其中超过60%的患者将在一年内死亡，使肺癌成为男性和女性最大的癌症杀手。正确的肿瘤组织病理学诊断是决定适当治疗的关键。然而，肿瘤的精确分类仍然是一个重大的生物医学挑战。此外，组织学相似的肿瘤可能会有不同的临床结果，强调需要更详细的分子分类。肺癌细胞表面受体(S)特异性配体的产生将影响包括功能诊断在内的临床问题。我们的总体目标是产生一组细胞特异性分子，可以用来对肿瘤类型进行分类，并在高通量诊断分析中利用这些癌症特异性试剂。利用噬菌体展示技术，我的实验室已经开发出平台方法来分离与特定细胞系结合并介导摄取的多肽。我们已经确定了针对25种不同细胞类型的细胞特异性靶向多肽，包括4种肺癌细胞系。分离的多肽显示出显著的细胞特异性，即使在相似的细胞中也是如此，并且能够区分正常细胞和癌细胞以及不同的肺肿瘤细胞。这种高分辨能力表明，可以识别出选择性地与不同类型的肿瘤结合的多肽，即使是那些分类相似的肿瘤。我们建议通过分离针对4种不同肺癌细胞系的细胞靶向多肽来扩展这一组肺特异性试剂，然后将这些多肽用作诊断试剂。这些多肽将被检测亲和力和细胞特异性。我们将从噬菌体骨架中去除多肽，并合成保留其亲和力和细胞特异性的多肽支架。我们将开发一种基于肽结合的高通量荧光分析，这将允许对肺癌样本进行更多的分子分类。该分析可以被多路复用，从而可以在单个样品上检测多个结合事件。在这个试点项目结束时，我们将开发一个新的诊断平台，可以扩展到临床样本。此外，这里开发的技术也可以应用于其他形式的癌症。

DESCRIPTION (provided by applicant): Within the United States, 170,000 new cases of lung cancer are diagnosed per year. Over 60% of these patients will die within one year, making lung cancer the largest cancer killer of both men and women. The correct histopathological diagnosis of a tumor is critical in determining the appropriate treatment. However, precise classification of tumors remains a significant biomedical challenge. Furthermore, tumors of similar histology can have different clinical outcomes, stressing the need for more detailed molecular classifications. Generation of ligands specific to receptor(s) on a surface of a lung cancer cell will impact clinical issues including functional diagnosis. Our overall goal is to generate a panel of cell-specific molecules that could be used to classify tumor types and utilize these cancer specific reagents in a high throughput diagnostic assay. Using phage display technologies, my laboratory has developed platform methodologies to isolate peptides that bind to and mediate uptake into specific cell lines. We have identified cell-specific targeting peptides for 25 different cell types, including 4 lung cancer cell lines. The isolated peptides display remarkable cell specificities, even among similar cells, and are able to discriminate between normal and cancerous cells as well as different lung tumor cells. This high discriminating power suggests that peptides could be identified that selectively bind to different tumor types, even those with similar classifications. We propose to expand this panel of lung specific reagents by isolating cell targeting peptides for 4 different lung cancer lines and then utilize these peptides as diagnostic reagents. These peptides will be assayed for affinity and cell-specificity. We will remove the peptides from the phage backbone and synthesize peptide scaffolds that retain their affinity and cell-specificity. We will develop a high throughput fluorescent assay based on peptide binding that will allow for a more molecular classification of lung cancer samples. The assay can be multiplexed so that multiple binding events can be examined on a single sample. At the end of this pilot project we will have developed a novel diagnostic platform that can be expanded to clinical samples. Furthermore, the technologies developed here can be applied to other forms of cancer.