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

描述(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，就有39,840名妇女死于这种疾病。换算下来，每天约有1560人死于癌症--总体而言，在美国，每4人中就有1人死于癌症。在美国，癌症是仅次于心脏病的第二大常见死因。未来在癌症研究和药物发现的几个关键领域的进展将取决于研究人员可靠地检测、描述和跟踪从良性状态向癌症状态转变过程中肿瘤环境中发生的细微变化的能力。这项赠款提案的中心目标是设计、开发和评估计算和成像工具，这些工具提供关于从恶性肿瘤开始发生的机械和形态变化的洞察，并使用来自乳腺癌病例的具有代表性的癌症组织样本集合来跟踪整个疾病进展过程中的这些变化。这些新技术将有助于发现新的诊断和预后线索，而这些线索使用传统的评估方法是不明显的。该项目的总体目标是：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调查样本组织力学特性的变化；2)增加采样吞吐量，以便能够同时使用基于微型机电系统(MEMS)技术的微力传感器阵列对多个感兴趣区域进行自动评估；3)比较乳腺癌病例正常组织样本中的力学变化、表达特征和生物标记物的空间分布，以及在乳腺癌病例的癌变初期和疾病发展的整个主要阶段收集的生物标志物。在成功实现这些目标的基础上，我们将设计、开发和评估一种可靠的方法，为执行自动化的、更高吞吐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(Emory大学和宾夕法尼亚大学-请参阅支持函)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，我们为这个极其重要的NIH项目召集了一支由马里兰大学、帕克学院和新泽西癌症研究所的优秀工程师和临床医生组成的团队。

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.