Neoplasms

All Conditions

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Rare Diseases

Carcinoma

Carcinoma, Renal Cell

Adenocarcinoma

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Kidney Neoplasms

Urologic Neoplasms

Urogenital Neoplasms

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Kidney Diseases

Urologic Diseases

Male Urogenital Diseases

Renal Cell Carcinoma

Clear Cell Renal Cell Carcinoma

Sequencing

Yunze Xu

Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare subtype of RCC characterized by germline/somatic mutation of the fumarate hydratase (FH) gene, and is an extremely aggressive tumor, with a propensity to disseminate early even in the setting of a small primary tumor.

Affected individuals or individuals suspected of having a germline FH will undergo periodic clinical assessment and genetic analyses for the purpose of: 1) definition and characterization of phenotype, 2) determination of the natural history of the disorder, and 3) genotype/phenotype correlation. Genetic linkage studies may be performed in situations in which the genetic basis of the disorder has not been elucidated.

* Discovery and validation of biomarker predicting FH-deficient RCC systemic treatments response
* Analysis for expression level of ctDNA using Next generation sequencing in FH-deficient RCC blood by systemic treatments response
* Analysis for expression level of mRNA using Next generation sequencing in FH-deficient RCC tissue by systemic treatments response
* Analysis for expression level of small molecule metabolites using mass spectrometry in FH-deficient RCC tissue and blood by systemic treatments response
* Analysis for genetic and protein expression at a single-cell level using a novel flow cytometry and RNA-sequencing protocol in FH-deficient RCC tissue and blood.
* Validation of genetic and protein expression using qRT-PCR or IHC in multiple independent cohort.
* Biological biomarkers-clinical factor combined prediction model of FH-deficient RCC systemic treatments response

Biomarkers of Response to Systemic Treatments in FH-deficient RCC

Renji Hospital, School of Medicine, Shanghai Jiao Tong University

across multiple LC-MS data files. across multiple LC-MS data files. across multiple LC-MS data files. across multiple LC-MS data files. across multiple LC-MS data files

Collection of blood, tissue \&amp; urine to address further scientific questions related to this protocol.

Combination prediction model of biologic biomarkers and clinical factors for the response of systemic treatments in FH-RCC.

RenJi Hospital

All Drugs and Chemicals

Amino Acids

Tyrosine Kinase Inhibitors

Tyrosine

Patients with Renal Cell Carcinoma who have had previous systemic treatment, with adequate tissue samples and radiological data

RNA sequencing, immunohistochemistry, immunofluorescence, and cytometry of tumour tissues

Laboratory analysis of samples

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.

Application of machine learning

Richard Skells

Amanda Walker

Sarah Welsh

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.

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.

Renal Cell Carcinoma tumour specimen (tissue block or slides), kidney tissue specimen (tissue block or slides), previously obtained with consent under NeoSUN study

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

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

Using data from RNA sequencing of tumour tissues, the outcome is to identify novel biomarkers of response.

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.

Using data from immunohistochemistry, the outcome is to identify novel biomarkers of response.

Using data from immunofluorescence, the outcome is to identify novel biomarkers of response.

Using data from CyTOF (mass cytometry), the outcome is to identify novel biomarkers of response.

CCTU- Cancer Theme

Dr. Sarah J. Welsh

Symptoms and General Pathology

Nervous System Diseases

Neoplasm Metastasis

Brain Neoplasms

Neoplastic Processes

Central Nervous System Neoplasms

Nervous System Neoplasms

Brain Diseases

Central Nervous System Diseases

Antineoplastic Agents

Immune Checkpoint Inhibitors

Temozolomide

Cisplatin

Participants underwent local treatments such as surgical resection or radiotherapy or surgical resection plus radiotherapy were divided into local treatment group or otherwise into non-local treatment group

Participants underwent systemic treatments such as chemotherapy or target therapy or chemotherapy plus target therapy were divided into systemic treatment group or otherwise into non-systemic treatment group

Participants underwent local treatments such as surgical resection or radiotherapy or surgical resection plus radiotherapy were divided into local treatment group; Participants underwent systemic treatments such as chemotherapy or target therapy or chemotherapy plus target therapy were divided into systemic treatment group

Participants underwent local treatments such as surgical resection or radiotherapy or surgical resection plus radiotherapy were divided into local treatment group; Participants underwent local treatment plus systemic treatment were divided into local treatment+systemic treatment group

Participants underwent systemic treatments such as chemotherapy or target therapy or chemotherapy plus target therapy were divided into systemic treatment group; Participants underwent local treatment plus systemic treatment were divided into local treatment+systemic treatment group

Participants underwent local treatment plus systemic treatment were divided into local treatment+systemic treatment group or otherwise into non-local treatment+systemic treatment group

neurosurgical resection, stereotactic radiosurgery (SRS), whole brain radiotherapy(WBRT), EGFR target drugs, Immune checkpoint inhibitors，cisplatin, temozolomide

neurosurgical resection

The goal of this observational study is to learn about treatments in brain metastases with poor prognostic factors. The main questions it aims to answer are:

* What kind of local treatment provides a survival benefit for patients with poor prognostic factors?
* What kind of systemic treatment provides a survival benefit for patients with poor prognostic factors?
* Will the combination of local treatment and systemic treatment provide a survival benefit for patients with poor prognostic factors?

Participants will be asked to provide personal information about their living status, symptoms, and disease control during the follow-up.

Treatments for Brain Metastases With Poor Prognostic Factors

Effects of Local and Systemic Treatments in Brain Metastases With Poor Prognostic Factors: Multicenter Retrospective Study

We decided not to share the IPD because the study involved a lot of people's privacy

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Biomarkers of Response to Systemic Treatments in FH-deficient RCC	NCT05243173	Metabolomics Renal Cell Carc... FH-Deficient RC... Systemic Treatm...	Sequencing	18 Years -	RenJi Hospital
Identification of Novel Biomarkers of Response to Systemic Treatments in Renal Cell Cancer	NCT04060537	Carcinoma, Rena...	Laboratory anal... Application of ...	18 Years -	Cambridge University Hospitals NHS Foundation Trust
Treatments for Brain Metastases With Poor Prognostic Factors	NCT05609162	Brain Metastase...	neurosurgical r...	-	Sun Yat-sen University
Treatments for Brain Metastases With Poor Prognostic Factors	NCT05609162	Brain Metastase...	neurosurgical r...	-	Sun Yat-sen University

Spots Global Cancer Trial Database for systemic treatments