A Retrospective Translational Study to Identify Novel Biomarkers of Response to Systemic Treatments in Renal Cell Cancer
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 12
- 主要终点
- Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer.
研究概览
简要总结
This research study aims to investigate changes inside kidney cancers (also known as Renal Cell Carcinoma or RCC), and in normal kidney surrounding the tumour, when patients are treated with systemic therapy.
Samples, radiological images and data from a previous trial (NeoSUN) will be analysed and/or reanalysed, in accordance with the consent of NeoSUN participants.
详细描述
This research study aims to investigate changes inside kidney cancers, and in normal kidney surrounding the tumour, when patients are treated with systemic therapy. Systemic treatment is widely used as routine treatment for patients who have kidney cancer that has spread to other organs. It is also used sometimes before surgery to try to shrink kidney cancers to make surgery easier and less risky. Recent research has shown that kidney cancers consist of many different cells in addition to the cancer cells (including immune, structural and blood vessel cells). However, doctors know very little about what changes systemic therapy causes to cells other than cancer cells.
Researchers now think that these other cells may influence how the tumour cells behave during cancer treatment and how well the cancer responds to treatment.
The NeoSUN clinical trial was run at Cambridge University Hospitals between 2006 and 2015.18 patients were treated with a TKI called sunitinib for 12 days before they had their kidney surgically removed. MRI and CT scans were performed before and after the treatment. Samples of tumour and normal kidney were also taken before and after treatment. All patients consented to use of their tissue and data for future research projects. The investigators would like to analyse the effects that sunitinib had on the tumour and other cells using techniques called immunohistochemistry, immunofluorescence, and CyTOF. These mark the different cells so they can easily be identified and the effects on each one analysed. The investigators would also like to re-analyse the scans performed and use artificial intelligence (AI) to see try to detect new trends. The information may help to guide which drugs might be best used in future to treat kidney cancer more effectively whilst keeping side effects low.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Aged 18 years or older
- •Diagnosis of renal cell cancer (any stage).
- •Patient received systemic treatment for their renal cancer at Cambridge University Hospitals NHS Foundation Trust.
- •Patients must have consent in place, for the use of tissue and imaging to be used for the purposes of clinical research;
- •Use of tissue not required for their diagnosis or treatment to be stored and used for the purposes of clinical research, which may include genetic research.
- •Use of relevant sections of their medical records, or by relevant regulatory authorities, where my tissue is being used for research, giving permission for those individuals to have access to their medical records.
- •Participants must also meet at least one of the following criteria to be eligible:
- •For tissue analysis: Patient must have tumour tissue and/or normal adjacent kidney stored (either as formalinfixed paraffin-embedded tissue, or as 'fresh frozen' tissue).
- •For imaging analysis: Patient must have had at least 1 scan (either CT or MRI) within 28 days of starting treatment with systemic treatment for their cancer.
排除标准
- 未提供
结局指标
主要结局
Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer.
时间窗: 2 years
Using data from CyTOF (mass cytometry), the outcome is to identify novel biomarkers of response.
Radiological identification of biomarkers of response to systemic treatment in Renal Cell Cancer.
时间窗: 2 years
Using data from MRI imaging by analysis of the tumour microenvironment and machine learning interrogation of output data, the outcome is to identify novel biomarkers of response.
次要结局
未报告次要终点
研究者
CCTU- Cancer Theme
Dr. Sarah J. Welsh
Cambridge University Hospitals NHS Foundation Trust
