Mechano-Visual Phenotyping of Cancer: From Onset Through Disease Progression

描述（由申请人提供）：美国癌症协会估计，2010年有207，090名妇女被诊断患有乳腺癌，仅在美国，当年就有39，840名妇女死于这种疾病。这意味着每天约有1560人死于癌症-总体而言，在美国，每4例死亡中就有1例死于癌症。在美国，癌症是仅次于心脏病死亡的第二大常见死因。癌症研究和药物发现的几个关键领域的未来进展将依赖于研究人员可靠地检测、表征和跟踪肿瘤环境在从良性状态转变为癌性状态期间发生的微妙变化的能力。这项拨款提案的中心目标是设计，开发和评估计算和成像工具，这些工具提供了有关从恶性肿瘤开始发生的机械和形态变化的见解，并使用乳腺癌病例的代表性癌症组织标本集合在整个疾病进展过程中跟踪这些变化。这些新技术将有助于发现新的诊断和预后线索，这是不明显的使用传统的评估方法。 拟议项目的总体目标是：1）通过基于实验捕获的原子力显微镜（AFM）数据的精确非线性有限元建模来研究采样组织的机械特性的变化，2）增加采样吞吐量以允许同时自动评估多个感兴趣区域，使用基于微电子机械系统（MEMS）技术的微力传感器阵列，以及3）比较机械变化，表情特征，以及正常组织样品中生物标志物的空间分布，这些样品是在恶性肿瘤发生时和乳腺癌病例的疾病进展的整个初级阶段收集的。在成功完成这些目标的基础上，我们将设计、开发和评估一种可靠的方法，为执行自动化、高通量的标本表征提供多模式决策支持。最后，我们的团队将在战略采用者站点（埃默里大学和宾夕法尼亚大学-参见支持信）部署、测试和优化更新的计算和建模工具套件。 为了实现这一目标，我们已经为这个极其重要的NIH项目组建了一支由来自马里兰州大学、帕克学院和新泽西癌症研究所的工程师和临床医生组成的优秀团队。

DESCRIPTION (provided by applicant): The American Cancer Society estimated that 207,090 women were diagnosed with breast cancer in 2010 and that 39,840 women died of this disease in the United States alone during that year. This translates to about 1560 deaths per day attributed to cancer - overall, in the US, 1 in every 4 deaths is attributed to cancer. In the US, cancer is the second most common cause of death only next to deaths due to heart disease. Future progress in several key areas of cancer research and drug discovery will rely upon the capacity of investigators to reliably detect, characterize and track subtle changes that occur in the tumor environment during the transformation from the benign to cancerous state. The central objective of this grant proposal is to design, develop and evaluate computational and imaging tools, which provide insight regarding the mechanical and morphological changes that occur starting with the onset of a malignancy and follow those changes throughout the course of disease progression using a representative ensemble of cancer tissue specimens from breast cancer cases. These new technologies will facilitate the discovery of novel diagnostic and prognostic clues, which are not apparent using traditional methods of assessment. The overarching objectives of the proposed project are: 1) to investigate changes in the mechanical characteristics of sampled tissues through accurate non-linear finite element modeling based on the experimentally captured atomic force microscopy (AFM) data, 2) to increase the sampling throughput to allow automated assessment of multiple regions of interest, simultaneously, using an array of micro force sensors based on micro-electro-mechanical systems (MEMS) technology, and 3) to compare the mechanical changes, expression signatures, and spatial distribution of biomarkers in the normal tissue samples with those collected at the onset of malignancy and throughout the primary stages of disease progression for breast cancer cases. Based on successful completion of these aims, we will design, develop and evaluate a reliable means for providing multimodal decision support for performing automated, higher-throughput characterization of specimens. Finally, our team will deploy, test and optimize the updated suite of computational and modeling tools at strategic adopter sites (Emory University and University of Pennsylvania - see letters of support). To accomplish this, we have assembled an excellent team of engineers and clinicians from the University Of Maryland, College Park and The Cancer Institute of New Jersey for this extremely important NIH project.