DYNAMIC MOLECULAR EVOLUTION OF TREATMENT RESISTANT CANCER

难治性癌症的动态分子进化

• 批准号: 10244626
• 负责人: Shuang Zhao
• 金额: $ 136.8万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10244626
• 关键词: Advanced Malignant Neoplasm; Blood specimen; Cause of Death; Clinical; Complication; DNA; Laboratories; Logistics; Malignant Neoplasms; Molecular; Molecular Evolution; Monitor; Operative Surgical Procedures; Patients; Pharmaceutical Preparations; Procedures; RNA methylation; Radiation; Research; Resistance; Resistance development; Risk; Sampling; Techniques; Testing; Time; Tissue Sample; United States; advanced disease; cancer therapy; dynamical evolution; early detection biomarkers; liquid biopsy; novel; potential biomarker; refractory cancer; resistance mechanism; therapy resistant; tumor; tumor progression

Project Summary/Abstract: Cancer is the second leading cause of death in the United States. In some patients, cancer can be cured with surgery and/or radiation. Many patients will develop advanced disease that must be managed with various systemic medications and treatments. Unfortunately, the majority of patients in this group will eventually develop treatment resistant cancer, which is often lethal. Despite its importance, our understanding of how this resistance develops and evolves is limited. The primary challenge is that the study of treatment resistance requires obtaining tumor samples before, during, and after it develops. However, obtaining these samples involves surgical or other invasive procedures that carry some risk of causing a complication which could harm the patient. Repeatedly obtaining tissue samples for research is not feasible for the majority of patients. We propose to use a new “liquid” biopsy technique in which molecular information about the tumor is collected from serial blood samples, which patients with cancer are frequently providing for a variety of laboratory tests already. This information can then be used to understand how treatment resistance develops and how it evolves over time. This will allow us to identify potential strategies to overcome this resistance, as well as create tests to pick it up early so that treatment can be adjusted before the cancer grows and the patient gets sicker. This strategy could be used across cancers and treatments and change the paradigm in how we understand cancer progression as well as change how we monitor and treat patients.

项目总结/文摘: