A Non-small Cell Lung Cancer Tissue Microarray to Analyze Protein Expression

到目前为止，我们已经开发了一个全面的肺组织微阵列，其中包括300 NSCLC例，神经内分泌肿瘤183例。我们进行了系统的评估， 用于肺癌诊断和预后的候选蛋白标志物。开发使用为 组织微阵列已经使得有效地评估基因表达状态成为可能， 数百个人体组织样本然而，客观评估蛋白质染色对 放置在阵列上的单个组织芯通常是耗时的并且可能是可变的。 为了克服这一局限性，我们探索了一种计算机辅助半自动评分（CASA）方法 其允许快速和可靠地对免疫组织化学染色状态进行评分， 我们的肺组织微阵列上的组织核心包含150个肺腺癌（AD）和150个 鳞状细胞癌（SCC）病例。使用Aperio ScanScope TM GL扫描TMA载玻片 系统并通过Aperio ImageScope软件（版本7.3.36）查看。为了评分，图像 转移至Aperio-TMA实验室图像分析软件（7.0.1.235版）并进行分析 使用Aperio的正像素计数算法，该算法基于分布和强度 每个组织核心样本中的染色信号。我们优化了评分算法， 使我们能够定量地确定单个组织核心的染色状态。到 为了评估这种方法的稳健性，我们首先将CASA方法与IHC染色进行了比较 12种染色体重塑蛋白的数据，由经过培训的病理学家使用聚类进行评分 分析. CASA方法对蛋白质因子的聚类结果与 通过手动评分获得。这表明，我们的计算机辅助评分方法是 与手动评分方法相当。使用这种方法，我们评估了 近100种蛋白质通常已知与癌症发展有关。统计 分析使我们能够鉴定共表达与临床表现密切相关的基因。 肺癌患者的生存率。此外，我们还取得了实质性进展， 使用遗传标记进行肺癌诊断。与临床研究人员合作， 康奈尔大学，我们已经成功地开发了一种方法，利用遗传标记来识别 细针穿刺（FNA）活检中的肺癌。染色体拷贝数在1 q32增加， 以3q 26、5 p15和8 q24为标记物，确定FNA活检组织的肿瘤状态 使用荧光原位杂交（FISH）。我们使用了两组肺样本。的 训练集包括6个石蜡包埋的非癌肺组织和33个福尔马林固定的 肺肿瘤活检，以建立肺肿瘤的最佳固定和FISH评分标准 癌症检测测试集包含40个FNA活检的常规石蜡切片， 混合肿瘤亚型和良性肺部疾病用于评估敏感性， FISH方法的特异性。我们的结果表明，仅使用四个标记， FNA活检的肿瘤状态可以100%的特异性和94%的敏感性识别， 在可分析的样品中，无论肿瘤亚型、阶段和大小。遗传 FNA活检中的肺识别诊断具有高度特异性， 补充常规细胞学和放射学在肺癌筛查诊断和随访中的作用。我们 现在已经将这种高度敏感和特异的方法改进为使用显色剂， 因此，基因拷贝数变化可以与组织一起确定 形态学和病理学。

To date, we have developed a comprehensive lung tissue microarray that included 300 NSCLC cases and 183 neuroendocrine tumors. We have engaged in a systematical evaluation of candidate protein markers for lung cancer diagnosis and prognosis. The development and use of tissue microarray have made it possible to effectively evaluate gene expression status in hundreds of human tissue specimens. However, objective assessment of protein staining on the individual tissue core placed on the array is often time consuming and potentially variable. To overcome this limitation, we explored a computer-aided semiautomated scoring (CASA) method that allows fast and reliable scoring of the immunohsitochemistry staining status of the tissue cores on our lung tissue microarray containing 150 lung adenicarcinoma(AD) and 150 squamous cell carcinoma(SCC) cases. TMA slides were scanned by Aperio ScanScope TM GL System and viewed by Aperio ImageScope soft ware (version 7.3.36). For scoring, the images were transferred to Aperio-TMA lab image analysis software (version 7.0.1.235) and analyzed using Aperio's Positive Pixel Count Algorithm which is based on the distribution and intensity of staining signal in each tissue core sample. We optimized the scoring algorithm which allowed us to quantitatively determine the staining status of the individual tissues cores. To evaluate the robustness of this approach, we first compared the CASA approach to IHC staining data for 12 chromosome remodeling proteins scored by a trained pathologist using clustering analysis. The clustering of protein factors by CASA approach was nearly identical those obtained by manual scoring. This demonstrates that our computer-aided scoring method is comparable to the manually scoring method. Using this approach, we evaluated the expression of nearly 100 proteins commonly known to be associated with cancer development. Statistical analysis allowed us to identify genes whose co-expression is strongly associated with clinical survival of the lung cancer patients. In addition, we have made substantial progress toward using genetic markers for lung cancer diagnosis. In collaboration with clinical researchers at Cornell University, we have successfully developed a method using genetic markers to identify lung cancers in fine needle aspirate (FNA) biopsies. Chromosome copy number gains in 1q32, 3q26, 5p15 and 8q24 were used as markers to determine the neoplastic state of FNA biopsies using fluorescent in situ hybridization (FISH). We used two sets of lung of samples. The training set included six paraffin embedded non-cancer lung tissues and 33 formalin-fixed biopsies of lung tumors to establish optimal fixation and FISH scoring criteria for lung cancer detection. The testing set contained 40 routine paraffin sections of FNA biopsies with mixed tumor subtypes and benign lung diseases for used to evaluate the sensitivity and specificity of the FISH method. Our results showed that using only four markers, the neoplastic state of FNA biopsies can be identified with 100% specificity and 94% sensitivity, respectively, in analyzable samples regardless of tumor subtype, stage, and size. Genetic diagnosis is highly specific for lung identification in FNA biopsy and could potentially complement routine cytology and radiology in lung cancer screening diagnosis and follow up. We have now modified this highly sensitive and specific method to the use of chromogenic substrate so that genetic copy number changes can be determined together with tissue morphology and pathology.