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

癌症的机械视觉表型：从发病到疾病进展

基本信息

批准号：
8819519
负责人：
JAYDEV P. DESAI
金额：
$ 31.75万
依托单位：
UNIV OF MARYLAND, COLLEGE PARK
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-03-12 至 2016-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8819519
关键词：
American Cancer Society Antineoplastic Agents Applications Grants Area Atomic Force Microscopy Benign Biological Markers Cancer Institute of New Jersey Cancerous Cause of Death Cells Cessation of life Characteristics Collaborations Color Computer Simulation Computer software Coupling Data Data Collection Diagnosis Disease Disease Progression Elements Engineering Environment Feedback Future Gold Heart Diseases Image Imaging Device Individual Lead Letters Location Machine Learning Malignant - descriptor Malignant Neoplasms Maps Maryland Measurable Measurement Measures Mechanics Methods Modality Modeling Molecular Profiling Normal tissue morphology Pathology Pennsylvania Phenotype Physical shape Proteomics Quantum Dots Research Personnel Robotics Sampling Signal Transduction Site Spatial Distribution Specimen Staging Surface System Techniques Technology Testing Time Tissue Model Tissue Sample Tissues Translating United States United States National Institutes of Health Universities Update Vision Visual Woman anticancer research base bioimaging biomedical informatics college comparative computerized tools design drug discovery insight interest malignant breast neoplasm man new technology novel diagnostics operation prognostic response sensor soft tissue tool tumor

项目摘要

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.

描述（由申请人提供）：美国癌症协会估计，2010年有207,090名妇女被诊断出患有乳腺癌，仅在美国，就有39,840名妇女死于这种疾病。这意味着每天约有1560例死亡归因于癌症 - 总体而言，在美国，每4例死亡中有1人归因于癌症。在美国，癌症是仅因心脏病而死亡的第二大死亡原因。癌症研究和药物发现的几个关键领域的未来进步将依赖于研究人员可靠地检测，表征和跟踪从良性变为癌状态的肿瘤环境中发生的细微变化的能力。该赠款提案的核心目的是设计，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开始的机械和形态变化的见解，并使用乳腺癌病例的癌症组织标本的代表性集合在整个疾病进展过程中遵循这些变化。这些新技术将有助于发现新颖的诊断和预后线索，使用传统的评估方法并不明显。 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)技术，以及3）比较正常组织样品中生物标志物的机械变化，表达特征和空间分布与在恶性肿瘤发作时收集的那些以及乳腺癌病例疾病进展的主要阶段的机械变化，表达特征和空间分布。基于这些目标的成功完成，我们将设计，开发和评估可靠的手段，以提供多模式的决策支持，以执行标本的自动化，更高的表征。最后，我们的团队将在战略采用者网站（埃默里大学和宾夕法尼亚大学 - 请参阅支持信）中部署，测试和优化更新的计算和建模工具套件。为此，我们为这个极为重要的NIH项目组成了一支优秀的工程师和临床医生团队。