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

描述（由申请人提供）：多重生物标志物分析在反映肿瘤生物学行为方面比单一生物标志物分析更有力，但其标准化和定量仍然是一个挑战。此外，大多数计算机软件不提供用于成像和分析生物标志物的亚细胞定位以及将它们与生物和临床信息相关联的方法。本项目的目的是开发一个平台，该平台结合了多重免疫染色的成像和定量以及生物信息学，用于预测头颈部鳞状细胞癌（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学现象，是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，我们的目的是测试的假设，即MBP可以开发，以确定亚细胞分布和表达的多种转移相关的生物标志物同时在PT。这些生物标志物的准确定量将有助于预测PT的转移。三种新兴技术，基于量子点（QD）的荧光（IHF），多光谱成像和机器学习将用于测试这一假设。使用这些方法，将开发一个将定量多重免疫染色与生物统计学相结合的平台，并测试其用于临床的灵敏度、特异性和预测能力。因此，该项目适合NCI计划公告“癌症预后和预测的发展研究”（PA-09-159）的范围。 研究提出了三个目标。(1)为了开发基于用于预测SCCHN PT组织中的LNM的本体组织模型的多重生物标志物系统和方法。本目标将使用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。将实现基于该块体组织模型的LNM预测。(2)开发基于亚群模型的每细胞定量方法，用于预测SCCHN PT组织中的LNM。 每细胞分析结果将量化为相同PT中多重生物标志物分析的高风险细胞百分比。高风险人群将与LNM相关。将亚群模型预测的灵敏度和特异性与整体组织模型进行比较。(3)结合淋巴结转移的临床特征，建立并验证列线图，作为预测淋巴结转移的工作平台。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。结合相关临床因素的最具预测性的生物标志物集将构成一个具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。

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.