Molecular Pathology Research for Cancer Diagnostics and Biomarkers

癌症诊断和生物标志物的分子病理学研究

• 批准号: 8554087
• 负责人: Robert Simpson
• 金额: $ 123.94万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8554087
• 关键词: Address; Agreement; Algorithms; Animal Model; Applied Research; Area; Artificial Intelligence; Automated Pattern Recognition; Automation; Biological Markers; Biological Models; Biopsy; Blood Vessels; CD8B1 gene; CLIC4 gene; Cancer Diagnostics; Cancer Model; Cell Culture Techniques; Cells; Chloride Channels; Collaborations; Combined Modality Therapy; Complex; Comprehension; Computer Assisted; Computer-Assisted Image Analysis; Cytoskeleton; DNA Sequence Analysis; Dendritic Cells; Development; Diagnosis; Diagnostic; Diagnostic Neoplasm Staging; Disease; Disease remission; Disseminated Malignant Neoplasm; Equipment; Evaluation; Genetic Engineering; Growth; Harvest; Hematopoietic Neoplasms; Heterogeneity; Histologic; Histopathology; Human; Image; Image Analysis; Immunological Diagnosis; Immunotherapeutic agent; In Vitro; Interleukin-15; Investigation; Knowledge; Laboratories; Lesion; Lung; Lymphoma; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Mass Spectrum Analysis; Measurement; Mediating; Medical; Medicine; Mesenchymal; Methods; Microdissection; Modality; Modeling; Molecular; Molecular Analysis; Mus; Nuclear; Oncogene Activation; Optical Instrument; Oxidation-Reduction; Pathologic; Pathologist; Pathology; Pattern Recognition; Phenotype; Protein Family; Reagent; Regimen; Relative (related person); Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Resistance; Resources; Role; Scanning; Signal Transduction; Slide; Source; Specimen; Structure of parenchyma of lung; T-Lymphocyte; TNFRSF5 gene; Techniques; Technology; Teratoma; Tissue Preservation; Tissue Procurements; Tissues; Training; Transforming Growth Factor beta; Translational Research; Treatment Efficacy; Tumor Burden; Tumor Tissue; Ultrasonography; Variant; Work; X-Ray Computed Tomography; biobank; cancer cell; cancer therapy; cell type; data acquisition; design; digital; experience; human CLIC4 protein; human disease; image processing; imaging Segmentation; improved; in vivo; insight; instrument; interest; melanoma; member; molecular imaging; molecular pathology; neoplastic cell; optical imaging; osteosarcoma; pluripotency; pre-clinical; quality assurance; ras Oncogene; residence; sarcoma; soft tissue; systems research; technology development; tool; trend; tumor

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 human disease, addressing critical barriers to research progress. Additional aims include the 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. 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 cancer, developing new methods in mass spectrometry for limited tissue such as biopsies or model animals, and effects of Ras oncogene activation. Continued advances and applications in developing quality assurance methods for tissue biobanking were also made. 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. 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. Successfully acquired measurements for lymphomas, osteosarcomas and melanomas were extended to include additional progress in developing and validating algorithms for cancers of the blood and vascular tissues, lung, and connective mesenchymal tissues (soft tissue sarcoma). 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. Pattern recognition image analysis (PRIA) is artificial intelligence automation technology that has the capacity to significantly impact the work that pathologists do when applied to histopathology. To what degree computer-assisted diagnostic pattern recognition image analysis agrees with accepted histopathology approaches has not been clearly established. Digitally scanned histomorphological whole-slide images from two sources served for evaluation of a commercially available pattern recognition image analysis platform, to address diagnostic agreement achievable. Substantial agreement, lacking significant constant or proportional errors, between pattern recognition image analysis and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden 0.988, indicating high reproducibility for both methods, yet pattern recognition image analysis reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of pattern recognition image analysis on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing pattern recognition image analysis error elsewhere in images. Pattern recognition image analysis error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. Pattern recognition image analysis appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of pattern recognition image analysis.Quantitative image analysis pathology was employed to study chloride intracellular channel (CLIC) 4. CLIC 4 is a member of a redox-regulated, metamorphic multifunctional protein family, first characterized as intracellular chloride channels. Current knowledge indicates that CLICs participate in signaling, cytoskeleton integrity and differentiation functions of multiple tissues. Image analysis algorithms were developed and applied to obtain evidence of nuclear localization and quantitation results supporting the indication that CLIC4 suppresses the growth of squamous cancers, that reduced CLIC4 expression and nuclear residence detected in cancer cells is associated with the altered redox state of tumor cells, and the absence of detectable nuclear CLIC4 in cancers contributes to TGF-beta resistance and enhances tumor development.In vivo image analysis provided further means to document enhanced CD8 T cell-mediated therapeutic efficacy using a combination regimen of murine IL-15 administered with an agonistic anti-CD40 Ab (FGK4.5), which led to increased IL-15Ralpha expression on dendritic cells (DCs), as well as other cell types, in a syngeneic established TRAMP-C2 tumor model. IL-15 has potential as an immunotherapeutic agent for cancer treatment because it is a critical factor for the proliferation and activation of NK and CD8(+) T cells. Anti-CD40-mediated augmented IL-15Ralpha expression was critical in IL-15-associated sustained remissions observed in TRAMP-C2 tumor-bearing mice receiving combination therapy.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.

在该项目中，研究的目的是表征和开发人类疾病的新动物模型，并开发更好地表征模型与人类疾病相关性的方法，解决研究进展的关键障碍。 其他目标包括开发用于疾病生物标志物评估和应用的新研究技术。 在开发癌症诊断方法以及可用于开发和表征人类癌症新模型的研究资源方面取得了进展。 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样本自动形态测量图像分析的方法、研究S100在癌症中的作用、开发针对活检或模型动物等有限组织的质谱新方法，以及Ras癌基因激活的影响。 在开发组织生物样本库质量保证方法方面也取得了持续的进展和应用。 生物样本库支持的转化研究依赖于高质量、注释明确的标本。 组织病理学评估有助于深入了解代表性病变对于研究目标的影响。 研究了记录肿瘤和基质的组织学比例的可行性，以努力增强有关生物储存库组织异质性的信息。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样本组织切片中的组织学肿瘤和非肿瘤组织区域。 成功获得的淋巴瘤、骨肉瘤和黑色素瘤的测量结果得到了扩展，包括在开发和验证血液和血管组织、肺和结缔间质组织（软组织肉瘤）癌症算法方面取得了额外进展。 定量图像分析自动化预计将最大限度地减少与常规生物样本库病理评估相关的变异性，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。模式识别图像分析 (PRIA) 是一种人工智能自动化技术，应用于组织病理学时能够显着影响病理学家的工作。 计算机辅助诊断模式识别图像分析与公认的组织病理学方法的一致程度尚未明确。 来自两个来源的数字扫描组织形态学全幻灯片图像用于评估市售模式识别图像分析平台，以解决可实现的诊断一致性。 对于肺转移癌区域，模式识别图像分析和手动形态测量图像分割之间获得了实质性一致性，缺乏显着的常数或比例误差（Passing/Bablok 回归）。 Bland-Altman 分析表明异方差测量和随着肿瘤负荷增加而方差增加的趋势，但平均偏差没有显着趋势。方法间的平均肿瘤含量差异百分比为-0.64。对相对于肿瘤大小百分比的方法间测量差异的分析表明，方法差异主要对最小测量产生影响（肿瘤负荷 0.988，表明两种方法的再现性均较高，而模式识别图像分析再现性优越（C.V.：PRIA = 7.4，手册 = 17.1）。对形态复杂的畸胎瘤的模式识别图像分析的评估导致诊断一致 病理学家对畸胎瘤子集的多能性进行评估。畸胎瘤组织学特征多样性的适应经常导致有害的权衡，增加图像中其他地方的模式识别图像分析错误。 模式识别图像分析误差是非随机的并且受到组织形态学变化的影响。训练算法时遇到的文件大小限制以及光谱图像处理优势的后果有助于诊断 对于某些畸胎瘤，存在不准确的情况。 模式识别图像分析似乎更适合表型多样性有限的组织。技术改进可以提高诊断一致性，一致的病理学家输入将有利于模式识别图像分析的进一步开发和应用。采用定量图像分析病理学来研究氯细胞内通道（CLIC）4。CLIC 4是氧化还原调节的变质多功能通道的成员 蛋白质家族，首先被表征为细胞内氯离子通道。目前的知识表明 CLIC 参与多种组织的信号传导、细胞骨架完整性和分化功能。 开发并应用图像分析算法来获得核定位和定量结果的证据，支持 CLIC4 抑制鳞状癌生长的迹象，癌细胞中检测到的 CLIC4 表达和核驻留的减少与氧化还原的改变有关 肿瘤细胞的状态，以及癌症中可检测到的核 CLIC4 的缺乏有助于 TGF-β 抗性并增强肿瘤的发展。体内图像分析提供了进一步的方法来记录使用小鼠 IL-15 与激动性抗 CD40 Ab (FGK4.5) 联合给药方案增强的 CD8 T 细胞介导的治疗功效，这导致 IL-15Ra 表达增加 同种建立的 TRAMP-C2 肿瘤模型中的树突状细胞 (DC) 以及其他细胞类型。 IL-15 具有作为癌症治疗免疫治疗剂的潜力，因为它是 NK 和 CD8(+) T 细胞增殖和激活的关键因素。 抗 CD40 介导的 IL-15Rα 表达增强对于 IL-15 相关的持续缓解至关重要 TRAMP-C2荷瘤小鼠接受联合治疗。该项目的重要材料、设备或方法包括使用重组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、计算机辅助图像分析、光学成像、质谱、分子病理学和兽医医学诊断。