Developing a Platform for Prediction of Metastasis Using Multiplexed QD-imaging

开发使用多重 QD 成像预测转移的平台

• 批准号: 8307808
• 负责人: ZHUO Georgia CHEN
• 金额: $ 35.48万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-27 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8307808
• 关键词: Behavior; Bioinformatics; Biological; Biological Markers; Biological Phenomena; Biometry; Breast Cancer Cell; Cancer Prognosis; Cell membrane; Cells; Clinic; Clinical; Color; Colorectal Cancer; Computer software; Data; Data Set; Databases; Detection; Development; Diagnostic Neoplasm Staging; Disseminated Malignant Neoplasm; E-Cadherin; Emerging Technologies; Epidermal Growth Factor Receptor; Epithelial; Flow Cytometry; Head and Neck Squamous Cell Carcinoma; Human; Image; Image Analysis; Immunohistochemistry; Literature; Lung; Machine Learning; Mesenchymal; Methodology; Methods; Modeling; NIH Program Announcements; Neoplasm Metastasis; Nomograms; Operative Surgical Procedures; Outcome; Population; Primary Neoplasm; Production; Quantum Dots; Reaction; Receiver Operating Characteristics; Research; Sampling; Sensitivity and Specificity; Signal Transduction; Site; Specificity; Specimen; Staining method; Stains; Standardization; System; Technology; Testing; Tissue Model; Tissues; Tumor Tissue; Tumor stage; Work; aldehyde dehydrogenases; anticancer research; base; cancer cell; cancer stem cell; clinical application; high risk; lymph nodes; model development; nanoparticle; neoplastic cell; novel; outcome forecast; tool; treatment planning; tumor

DESCRIPTION (provided by applicant): Multiplexed biomarker analysis is more powerful in reflecting the biological behaviors of a tumor than single biomarker analysis, but its standardization and quantification is still a challenge. Furthermore, most computer software does not provide methods for imaging and analyzing subcellular localization of biomarkers and correlating them with biological and clinical information. The objective of this project is to develop a platform which combines imaging and quantification of multiplexed immunostaining plus bioinformatics for the prediction of lymph node metastases (LNM) from the primary tumor (PT) of squamous cell carcinoma of the head and neck (SCCHN). LNM of SCCHN is a precisely defined biological phenomenon which is an ideal model to be utilized to develop this multiplexed biomarker platform (MBP). Based on our preliminary studies, we aim to test the hypothesis that that the MBP can be developed to identify the subcellular distribution and expression of multiple metastasis-related biomarkers simultaneously in PTs. Accurate quantification of these biomarkers will facilitate the prediction of metastasis from PTs. Three emerging technologies, quantum dot (QD)-based immunohistofluorescence (IHF), multispectral imaging, and machine learning will be used to test this hypothesis. Using these approaches, a platform that combines quantifying multiplexed immunostaining with biostatistics will be developed and tested for its sensitivity, specificity, and prediction power for use in the clinic. Therefore, this project fits appropriately to the scope of the NCI program announcement "Developmental Research in Cancer Prognosis and Prediction" (PA-09-159). Three aims are proposed in the study. (1) To develop a multiplexed biomarker system and method based on a bulk tissue model for prediction of LNM in SCCHN PT tissues. This Aim will establish and validate an analysis methodology for multiplexed quantification of membrane and cytoplasmic staining using a new function in InForm software where subcellular localization of certain biomarkers will be specifically analyzed. Prediction of LNM based on this bulk tissue model will be achieved. (2) To develop a per-cell quantification method based on a sub-population model for prediction of LNM in SCCHN PT tissue. The per-cell analysis results will quantified as the percentage of high risk cells from the multiplexed biomarker analyses in the same PTs. The high risk population will be correlated with LNM. The sensitivity and specificity of the prediction by the sub-population model will be compared with that of the bulk tissue model. (3) To develop and validate a nomogram with software combining clinical characterizations of metastasis as a working platform for the prediction of LNM. While the primary endpoint of Aim 1 and 2 is to correlate the three biomarkers with metastasis, other clinical factors such as differentiation status, tumor stage, and site, etc. may also correlate with LNM. The most predictive biomarker set combined with relevant clinical factors will constitute a platform with computer software that will be validated in an additional 100 SCCHN samples for prediction of LNM.

描述（申请人提供）：多重生物标志物分析比单一生物标志物分析更能反映肿瘤的生物学行为，但其标准化和量化仍然是一个挑战。此外，大多数计算机软件不提供对生物标志物的亚细胞定位进行成像和分析并将其与生物和临床信息相关联的方法。该项目的目标是开发一个结合多重免疫染色成像和定量以及生物信息学的平台，用于预测头颈鳞状细胞癌（SCCHN）原发肿瘤（PT）的淋巴结转移（LNM）。 SCCHN 的 LNM 是一种精确定义的生物现象，是用于开发这种多重生物标志物平台 (MBP) 的理想模型。基于我们的初步研究，我们的目的是测试这样的假设：MBP 可以被开发来同时识别 PT 中多种转移相关生物标志物的亚细胞分布和表达。这些生物标志物的准确定量将有助于预测 PT 的转移。基于量子点 (QD) 的免疫组织荧光 (IHF)、多光谱成像和机器学习这三种新兴技术将用于检验这一假设。使用这些方法，将开发一个将量化多重免疫染色与生物统计学相结合的平台，并测试其灵敏度、特异性和预测能力，以供临床使用。因此，该项目非常适合 NCI 计划公告“癌症预后和预测的发展研究”(PA-09-159) 的范围。 该研究提出了三个目标。 (1)开发基于体组织模型的多重生物标志物系统和方法，用于预测SCCHN PT组织中的LNM。该目标将使用 InForm 软件中的新功能建立并验证膜和细胞质染色多重定量的分析方法，其中将专门分析某些生物标志物的亚细胞定位。将实现基于该体组织模型的LNM预测。 (2) 开发一种基于亚群模型的每细胞量化方法，用于预测 SCCHN PT 组织中的 LNM。 每个细胞的分析结果将量化为同一 PT 中多重生物标志物分析中高风险细胞的百分比。高危人群将与LNM相关。亚群模型预测的敏感性和特异性将与大块组织模型的预测敏感性和特异性进行比较。 (3)结合转移临床特征的软件开发并验证列线图作为预测LNM的工作平台。虽然目标 1 和 2 的主要终点是将三种生物标志物与转移相关联，但其他临床因素（如分化状态、肿瘤分期和部位等）也可能与 LNM 相关。最具预测性的生物标志物集与相关临床因素相结合，将构成一个带有计算机软件的平台，该平台将在另外 100 个 SCCHN 样本中进行验证，以预测 LNM。