Molecular Pathology Research for Cancer Diagnostics and Biomarkers

研究的目的是描述和开发人类疾病的新动物模型，并开发更好地描述模型相关性的方法，解决研究进展的关键障碍。其他目标包括开发用于评估和应用疾病生物标志物的新研究技术。在开发癌症诊断方法和研究资源方面取得了进展，这些研究资源可用于开发和确定人类癌症新模型的特征，并可用于适当开发和利用组织生物库。该研究项目包括开发癌症模型的分子诊断能力，开发用于定量病理学的癌症标本自动形态图像分析方法，研究S100在癌症中的作用，以及粘膜黑色素瘤靶向治疗的临床前开发。在开发组织生物库的质量保证方法方面也继续取得进展和应用。开发了独特的空间光谱图像分析算法，用于应用自动模式识别形态测量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。在开发和验证肺癌和癌症转移的算法方面取得了额外的进展。利用定量光学成像开发和验证了对了解耐药机制重要的新模型。研究结果有助于制定生物库质量保证程序。生物库加入的病理学质量保证程序的协调落后于生物标本研究和生物库开发的其他途径。了解生物保藏标本的细胞含量对于发现用于诊断和治疗的组织特异性临床相关生物标志物非常重要。虽然在分子分析和数据挖掘中迅速出现的技术创造了对最小化分析前人为因素诱导变量的适当措施的关注，但较少注意注释生物样本的组成成分，以便生物样本库客户更有效地选择样本。分析前的组织处理和样本的组成都影响下游测定的相关大分子的获取。病理学家对生物储存库提交的材料（特别是作为质量保证程序一部分的组织）进行审查，有助于确保预期的靶细胞在储存的标本中存在且数量充足。这种手动过程可能是乏味和主观的。将数字病理学纳入生物库质量保证程序，使用自动模式识别形态测量图像分析来量化组织切片的数字全载玻片图像中的组织特征区域，可以最大限度地减少与生物库中常规病理学评价相关的可变性和主观性。全切片图像和病理学家审查的形态测量分析可以提供给研究人员，以指导标本的选择。协调病理学质量保证方法，最大限度地减少主观性和提高收藏品之间的再现性，将有利于研究相关的标本选择的研究人员，并可以促进信息共享的综合网络方法，生物库。黑色素瘤是人类和犬的一种重要恶性肿瘤。与基因工程模型不同，散发性犬黑色素细胞肿瘤与人类疾病有几个共同的特征，这可能使犬成为更相关的临床前模型。犬黑色素瘤很少出现在阳光照射的部位。大多数发生在口腔中，其中一个子集具有模仿人类粘膜黑色素瘤原位组分的上皮内恶性黑色素细胞。犬黑素细胞瘤的范围包括良性病变与痣的一些类似，以及侵袭性原发性黑色素瘤，和广泛转移。越来越多的证据表明，人类中不同的亚型，在体细胞和易感生殖系遗传改变，细胞起源，流行病学，与紫外线辐射的关系以及从良性肿瘤到恶性肿瘤的进展方面存在差异，也可能存在于狗中。与人皮肤黑色素瘤相比，犬和人粘膜黑色素瘤似乎罕见地具有BRAF、NRAS和c-kit突变，尽管两个物种共享AKT和MAPK信号传导激活。我们的结论是，有显着的重叠，在犬和人类粘膜黑色素瘤的临床和组织病理学特征。这代表了探索犬口腔黑色素瘤作为临床前模型的机会。

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, addressing critical barriers to research progress. Additional aims include the development of new research technologies for the evaluation and application of disease biomarkers. Progress was made in developing cancer diagnostics and in research resources useful in developing and characterizing new models of human cancer and for the proper development and utilization of tissue biobanks. This research project included developing capabilities in molecular diagnostics for cancer models, developing methods for automated morphometric image analysis of cancer specimens for quantitative pathology, investigating the role of S100 in cancer, and preclinical development of targeted therapy for mucosal melanoma. Continued advances and applications in developing quality assurance methods for tissue biobanking were also continued. Unique spatial-spectral image analysis algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and non-tumor tissue areas in biospecimen tissue sections. Additional progress was made in developing and validating algorithms for cancers of lung and cancer metastasis. Contributions to new models important for understanding mechanisms of drug resistance were developed and validated using quantitative optical imaging. Research resulted in contributions to development of biobank quality assurance procedures. Harmonization of pathology quality assurance procedures for biobank accessions has lagged behind other avenues of biospecimen research and biobank development. Comprehension of the cellular content of biorepository specimens is important for discovery of tissue-specific clinically relevant biomarkers for diagnosis and treatment. While rapidly emerging technologies in molecular analyses and data mining create focus on appropriate measures for minimizing pre-analytic artifact-inducing variables, less attention gets paid to annotating the constituent make up of biospecimens for more effective specimen selection by biobank clients. Both pre-analytic tissue processing and a specimen's composition influence acquisition of relevant macromolecules for downstream assays. Pathologist review of biorepository submissions, particularly tissues as part of quality assurance procedures, helps to ensure that the intended target cells are present and in sufficient quantity in accessioned specimens. This manual procedure can be tedious and subjective. Incorporating digital pathology into biobank quality assurance procedures, using automated pattern recognition morphometric image analysis to quantify tissue feature areas in digital whole slide images of tissue sections, can minimize variability and subjectivity associated with routine pathologic evaluations in biorepositories. Whole-slide images and pathologist-reviewed morphometric analyses can be provided to researchers to guide specimen selection. Harmonization of pathology quality assurance methods that minimize subjectivity and improve reproducibility among collections would facilitate research-relevant specimen selection by investigators and could facilitate information sharing in an integrated network approach to biobanking. Melanoma represents a significant malignancy in humans and dogs. Different from genetically engineered models, sporadic canine melanocytic neoplasms share several characteristics with human disease that could make dogs a more relevant pre-clinical model. Canine melanomas rarely arise in sun-exposed sites. Most occur in the oral cavity, with a subset having intraepithelial malignant melanocytes mimicking the in situ component of human mucosal melanoma. The spectrum of canine melanocytic neoplasia includes benign lesions with some analogy to nevi, as well as invasive primary melanoma, and widespread metastasis. Growing evidence ofdistinct subtypes in humans, differing in somatic and predisposing germ-line genetic alterations, cell of origin, epidemiology, relationship to ultraviolet radiation and progression from benign to malignant tumors, may also exist in dogs. Canine and human mucosal melanomas appear to harbor BRAF, NRAS and c-kit mutations uncommonly, compared to human cutaneous melanomas, although both species share AKT and MAPK signaling activation. We conclude that there is significant overlap in the clinical and histopathological features of canine and human mucosal melanomas. This represents opportunity to explore canine oral cavity melanoma as a pre-clinical model.