Identification of Novel Biomarkers of Response to Systemic Treatments in Renal Cell Cancer

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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A Retrospective Translational Study to Identify Novel Biomarkers of Response to Systemic Treatments in Renal Cell Cancer

Conditions
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Carcinoma, Renal Cell

Intervention / Treatment
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* Other: Laboratory analysis of samples
* Other: Application of machine learning


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 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. Exclusion Criteria: None

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Research<br>Patients with Renal Cell Carcinoma who have had previous systemic treatment, with adequate tissue samples and radiological data | Other: Laboratory analysis of samples<br>* RNA sequencing, immunohistochemistry, immunofluorescence, and cytometry of tumour tissues<br>Other: Application of machine learning<br>* Using machine learning to interrogate data generated from analysis of Renal Cell Cancer tumours using RNA sequencing, immunohistochemistry, immunofluorescence, cytometry, and MRI imaging of tumours.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer. | Using data from RNA sequencing of tumour tissues, the outcome is to identify novel biomarkers of response. | 2 years | 
| Radiological identification of biomarkers of response to systemic treatment in Renal Cell Cancer. | 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. | 2 years | 
| Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer. | Using data from immunohistochemistry, the outcome is to identify novel biomarkers of response. | 2 years | 
| Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer. | Using data from immunofluorescence, the outcome is to identify novel biomarkers of response. | 2 years | 
| Biological identification of biomarkers of response to systemic treatment in Renal Cell Cancer. | Using data from CyTOF (mass cytometry), the outcome is to identify novel biomarkers of response. | 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Cancer, Renal, Tyrosine Kinase Inhibitors, Systemic Treatments, Biomarkers