Molecular Pathology Research for Cancer Diagnostics and Biomarkers

在这个项目中进行研究，以确定和开发新的人类疾病动物模型，并开发更好地确定模型与人类状况的相关性的手段。该项目的目标包括评估研究技术和生物标志物。在开发癌症诊断方法和开发和表征人类癌症新模型的研究资源方面取得了进展。开发了用于验证人类癌症动物模型的方法和试剂和技术。该研究项目包括开发癌症模型的分子诊断能力，开发用于定量病理的癌症标本的自动形态图像分析方法，研究S100在卵巢癌中的作用以及Ras癌基因激活的非分裂细胞的细胞周期调节。在图像分析领域取得了研究进展，并将其应用于开发适用于组织生物库的质量保证方法。生物储存库支持的转化研究依赖于高质量，良好注释的标本。组织病理学评估有助于深入了解具有代表性的病变对研究目标的影响。研究了记录肿瘤和间质组织学比例的可行性，以增强有关生物库组织异质性的信息。利用商用软件，开发了独特的空间光谱算法，用于应用自动模式识别形态学图像分析来量化生物标本组织切片中的组织学肿瘤和非肿瘤组织区域。75% / 75%（100%）的淋巴瘤、76/77（98.7%）的骨肉瘤和60/70（85.7%）的黑色素瘤均成功获得了测量结果。肿瘤占据的组织面积百分比因患者和肿瘤类型而异，分布在中位数附近：淋巴瘤为94%（四分位间距(IQR) = 14%），黑色素瘤为84% (IQR = 24%)，骨肉瘤为39% （IQR = 44%）。从包括多个个体患者标本的子集中进行的患者内比较显示，淋巴瘤和黑色素瘤的中位变异系数小于或等于12%。骨肉瘤的表型异质性导致变异系数中位数为33%。统一应用，肿瘤特异性模式识别软件允许自动组织特征量化。此外，对收集的区域测量的分散分析，以及来自个体患者的多个标本，支持使用有限的组织切片来衡量某些肿瘤类型的特征。定量图像分析自动化有望最大限度地减少与常规生物库病理评估相关的可变性，并通过帮助指导选择适合研究的标本来增强生物标志物的发现。从有限的组织标本（如病理活检或小型模式生物组织）中确定与疾病相关的蛋白质组谱，仍然是一项分析挑战。可诱导的心脏特异性H-Ras-G12V肥厚性心肌病转基因小鼠模型提供了一个系统，用于探索使用基于质谱（MS）的蛋白质组学从有限的标本中获得疾病相关分子谱的潜力，常规用于病理诊断。一种依赖于两阶段甲醇辅助增溶的方法，用于消化从8微米厚的心肌病和正常心脏的新鲜冷冻组织切片制备的裂解物。利用纳米流反相液相色谱（nfRPLC），结合LIT- FTICR杂交技术，在肥厚型和野生型（对照）心肌中分别鉴定出704个和752个蛋白。对患病心脏中总共106种差异表达蛋白的蛋白质网络分析揭示了与心血管疾病的关联。采用质谱法鉴定H-Ras和vimentin的生物学相关表达，并用免疫组织化学和Western blot交叉验证病变心肌中H-Ras和vimentin的生物学相关表达。更重要的是，MS鉴定和进一步验证Wnt-3a和β -catenin，结合IHC标记磷酸化GSK-3beta并伴有β -catenin的核定位，提供了继发于Ras激活的致病性心肌肥大过程中典型Wnt/ β -catenin途径激活的证据。我们的研究结果表明，所描述的蛋白质组学方法允许从组织病理学组织切片中发现和评估与病理过程相关的差异表达蛋白；本项目的主要材料、设备或方法包括重组DNA技术、体外细胞培养、DNA序列分析、免疫诊断、分子成像、形态测量学、计算机辅助图像分析、光学成像、质谱分析、分子病理学和兽医医学诊断。

In this project research is conducted to characterize and develop new animal models of human disease and to develop the means to better characterize a model's relevance for a human condition. The project aims include the evaluation of research technologies and biomarkers. Progress was made in developing cancer diagnostics and in research resources useful in developing and characterizing new models of human cancer. Methods and reagents were developed and technologies were applied for use in validating animal models of human cancer. This research project included developing capabilities in molecular diagnostics for cancer models, developing methods for automated morphmetric image analysis of cancer specimens for quantitative pathology, investigating the role of S100 in ovarian cancer and cell cycle regulation in nondividing cells undergoing Ras oncogene activation. Research advances were made in the area of image analysis which were applied to develop quality assurance methods applicable to tissue biobanking. Biorepository supported translational research depends upon high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and non-tumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100 percent) lymphomas, 76/77 (98.7 percent) osteosarcomas and 60/70 (85.7 percent) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types, and was distributed around medians of 94 percent (interquartile range (IQR) = 14 percent) for lymphomas, 84 percent for melanomas (IQR = 24 percent), and 39 percent for osteosarcomas (IQR = 44 percent). Within-patient comparisons from a subset including multiple individual patient specimens revealed less than or equal to 12 percent median coefficient of variation for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33 percent median coefficient of variation. Uniformly applied, tumor-specific pattern recognition software permits automated tissue feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens. Determination of disease relevant proteomic profiles from limited tissue specimens, such as pathological biopsies or tissues from small model organisms, remains an analytical challenge. A transgenic mouse model of inducible cardiac-specific H-Ras-G12V hypertrophic cardiomyopathy provided a system to explore the potential of using mass spectrometry (MS)-based proteomics to obtain disease-relevant molecular profiles from limited specimens, routinely used in pathological diagnosis. A method that relies on two-stage methanol-assisted solubilization was developed to digest lysates prepared from 8 micrometer-thick fresh-frozen histological tissue sections of cardiomyopathic and normal hearts. A nano-flow reverse-phase liquid chromatography (nfRPLC), coupled to a hybrid LIT- FTICR, led to identification of a total of 704 and 752 proteins in hypertrophic and wild-type (control) myocardium, respectively. Protein network analyses on a total of 106 differentially expressed proteins in diseased hearts revealed an association with cardiovascular diseases. Biologically relevant expression of H-Ras and vimentin were identified by MS and cross-validated in diseased myocardium by immunohistochemistry and Western blot. More importantly, the MS identification and further validation of Wnt-3a and beta-catenin, in conjunction with IHC labeling of phosphorylated GSK-3beta accompanied by nuclear localization of beta-catenin, provided evidence of canonical Wnt/beta-catenin pathway activation during pathogenic myocardial hypertrophy, secondary to Ras activation. Our results indicate that the proteomic approach described permits molecular discovery and assessment of differentially expressed proteins implicated in pathological processes from histopathological tissue sections; the findings can be further validated with immunohistochemical techniques in serial tissue sections from the specimens analyzed by MS. The significant materials, equipment or methods in this project include use of recombinant DNA technology, in vitro cell culture, DNA sequence analysis, immunodiagnostics, molecular imaging, morphometrics, computer assisted image analysis, optical imaging, mass spectrometry, molecular pathology, and veterinary medical diagnosis.