Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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DOI：
10.3791/3334
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发表时间：
2011-10-01
影响因子：
1.2
通讯作者：
Harrison, David J.
中科院分区：
文献类型：
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作者：
Faratian, Dana;Christiansen, Jason;Harrison, David J.
在外科实践中，组织病理学家很好地认识到单个肿瘤内的形态异质性。虽然这通常表现为明显分化为公认的组织亚型或不同的病理分级，但通常在表型上存在更多细微的差异，无法进行准确的分类(图1)。最终，由于形态由潜在的分子表型决定，具有明显差异的区域可能伴随着协调细胞功能和行为的蛋白质表达的差异，从而导致外观的差异。可见的和不可见的(分子)异质性对预后的意义尚不清楚，但最近的证据表明，至少在遗传水平上，在原发肿瘤(1，2)中存在异质性，其中一些亚克隆会导致转移性(从而致命性)疾病。此外，一些蛋白质被测量为生物标记物，因为它们是治疗的靶点(例如，他莫昔芬和曲妥珠单抗(Herceptin)的ER和HER2)。如果这些蛋白质在肿瘤中表现出不同的表达，那么治疗反应也可能是不同的。广泛使用的免疫组织化学组织病理学评分方案要么忽略了蛋白质表达的量化，要么在数值上将其同质化。同样，在破坏性技术中，肿瘤样本被均质化(如基因表达谱)，定量信息可以被阐明，但空间信息会丢失。胰腺癌的遗传异质性定位方法要么依赖于单个细胞悬液的产生(3)，要么依赖于大体解剖(4)。最近的一项研究使用量子点来绘制前列腺癌组织的形态和分子的异质性[5]，提供了形态和分子映射是可行的原理证据，但没有对异质性进行量化。由于免疫组织化学充其量只能是半定量的，并且受到观察者内部和观察者之间的偏见的影响，因此需要更灵敏和更定量的方法来准确地定位和量化情况下的组织异质性。我们开发并应用了一种实验和统计方法，以基于自动定量分析系统(AQIA)系统来系统地量化肿瘤整个组织切片中蛋白质表达的异质性(6)。组织切片用针对细胞角蛋白和感兴趣目标的特定抗体标记，再与荧光团标记的二级抗体偶联。幻灯片使用全幻灯片荧光扫描仪进行成像。图像被细分为数百到数千个瓷砖，然后每个瓷砖都被分配了一个Aqua评分，这是一个衡量组织上皮(肿瘤)成分中蛋白质浓度的指标。根据辛普森的生物多样性指数(7)，利用最初在生态学中使用的异质性的统计测量，生成热图来表示蛋白质的组织表达，并分配异质性分数。到目前为止，还没有人尝试在组织学准备中与蛋白质表达一起系统地绘制和量化这种变异性。在这里，我们说明了该方法首次应用于卵巢癌中ER和HER2生物标记物的表达。该方法为将异质性作为翻译研究中生物标记物表达的自变量进行分析铺平了道路，从而确立了异质性在预后和治疗反应预测中的意义。
Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor(1,2), and some of these sub-clones give rise to metastatic (and therefore lethal) disease.Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension(3), or on macrodissection(4). A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue(5), providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ.We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system(6). Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index(7).To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.